ROLE OF MODERN TECHNOLOGY IN AGRICULTURE 9789393974655, 9682133341

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ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

Dheerendra Singh Er. Bilal Ahmad Langoo Er. Dhananjay M. Kadam Shrankhla Mishra

All the rights reserved. No part of this book may be reproduced, stored in a retrieved system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of author or publisher.

All rights reserved ISBN: 978-93-93974-65-5 ROLE OF MODERN TECHNOLOGY IN AGRICULTURE Dheerendra Singh Er. Bilal Ahmad Langoo Er. Dhananjay M. Kadam Shrankhla Mishra

Year of Publication: September, 2023 Front Cover Blue Duck Designer

PUBLISHER Blue Duck Publications Srinagar, J&K Cell: 9682133341 Website: blueduckpublications.com

PREFACE The dawn of the 21st century has witnessed a remarkable transformation in the agricultural landscape worldwide. As the global population continues to burgeon, the challenges faced by agriculture in meeting the ever-increasing demand for food, fiber, and other agricultural products have become increasingly complex. In this endeavor, modern technology has emerged as a powerful ally, reshaping the very essence of agriculture as we know it. The book you hold in your hands, “Role of Modern Technology in Agriculture,” represents a significant milestone in our understanding of how technological advancements have redefined and rejuvenated the agricultural sector. It is a culmination of diverse perspectives, experiences, and insights from experts, scholars, and practitioners who have dedicated their careers to exploring the intricate relationship between technology and agriculture. Within these pages, readers will embark on a journey through the multifaceted facets of modern technology’s role in agriculture. From precision farming and smart irrigation systems to biotechnology and artificial intelligence, this book explores the profound impact of innovation on crop production, livestock management, supply chain logistics, and environmental sustainability. We delve into the promise and potential of emerging technologies such as drones and the Internet of Things (IoT) in revolutionizing how we cultivate, harvest, process, and distribute agricultural products. Additionally, we examine the ethical, social, and environmental implications that accompany the adoption of these technologies, ensuring a comprehensive understanding of their effects on society at large. Our contributors hail from various corners of the academia and expertise, bringing with them diverse experiences and regional perspectives that enrich our exploration of modern

agriculture's technological dimensions. They share their expertise and research findings, offering readers a comprehensive view of the global landscape of agricultural technology adoption and innovation. In an era where the global population's sustenance is intrinsically tied to the agricultural sector's efficiency and sustainability, it is paramount that we appreciate the symbiotic relationship between technology and agriculture. This book serves as a vital resource for policymakers, researchers, students, and anyone with an interest in the intersection of innovation and agriculture. As we embark on this enlightening journey through the "Role of Modern Technology in Agriculture," we hope that readers will gain a deeper appreciation for the transformative power of technology in ensuring food security, environmental stewardship, and economic prosperity for generations to come. It is our collective hope that the insights contained herein will inspire further exploration, innovation, and collaboration in this vital field. Editors

CONTENT S. No.

1.

Chapter Title Revolutionizing Agriculture: The Vital Role of Farm Mechanization in Modernizing Practices

Page No.

1-10

Dr. Neha Dwivedi

2.

3.

Application of Modern Technology in Agriculture in India Tirunima Patle, Bhavana Tomar & Sneh Singh Parihar

Monitor and Control Crop Irrigation System through Smart Sensors

11-27

28-43

Ayushi Trivedi, Nirjharnee Nandeha, J.Himanshu Rao, Smita Agrawal, Amit Kumar, & R.S.Dangi

4.

Sustainable Prospects for Modern Agriculture Technology

44-64

Ayushi Trivedi & Nirjharnee Nandeha

Applications of Digital Agriculture 5.

6.

Bhavana Tomar, Sneh Singh Parihar, Tirunima Patle, Prasant Singh & Vikrant Malik

Application of Remote Sensing and Geographic Information System in Modern Agriculture Technology J Himanshu Rao & Ayushi Trivedi

65-98

99-115

7.

Application of Robotics, Artificial Intelligence and Deep Learning in Modern Agriculture Technology

116-132

Nirjharnee Nandeha & Ayushi Trivedi

8.

Role of Agroforestry in Modern Agriculture Technology

133-147

Disha Joshi & Anshul Mishra

9.

10.

11.

Application of Drones in Modern Agriculture Technology

148-161

Shubham Dhakad, Ghanshyam Panwar & Shraddha Sethi

Application of Modernized Irrigation 162-172 Techniques Gottam Kishore, Rooha Blessy & S. C. Haokip

Functional dynamics of the microbiome through Metagenomics and Transcriptomics approach

173-184

Shanu

12.

Crop Monitoring through Modern Technology Smita Agrawal, Ayushi Trivedi, Amit Kumar & R.S. Dangi

185-198

Chapter 1 Revolutionizing Agriculture: The Vital Role of Farm Mechanization in Modernizing Practices Dr. Neha Dwivedi Assistant Professor, Department of Agricultural Economics RVSKVV-College of Agriculture, Indore (M.P.) Corresponding Author Email ID: [email protected]

Abstract Farm mechanization is an essential component of contemporary agriculture that strives to improve farming practices by making the most use of time and resources. In India, where the agriculture industry has struggled with issues including population expansion, food demand, rural underdevelopment, and socioeconomic gaps among farmers, farm mechanization is playing a catalytic role in improving agricultural productivity and the socio-economic situations of farmers. Indian agriculture saw a tremendous transformation during the Green Revolution, which was characterized by the adoption of dynamic techniques and technologies including tractors, hybrid crops, and irrigation systems. Nevertheless, despite advancements, mechanization continues to be essential in tackling problems including smallholder farming, poverty, and the upcoming urbanization trend. Overall, farm mechanization is a key catalyst for changing Indian agriculture and addressing the sector's difficulties. Key-words: Farm Mechanization, Tractors, Productivity & Sustainable

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Introduction Farm mechanization refers to the enhanced farming technique requiring the least amount of time and resources. The agriculture industry has undergone numerous changes throughout the past century. The utilization of animal and human power for agriculturally related tasks has significantly decreased in India's agriculture industry (Ravi Kishore et al., 2022). The fad has made room for a variety of agricultural implements. Numerous of them are powered by fossil fuels, as a result, traditional agriculture has given way to a process that is more mechanized (Singh & Sahini,2019). The introduction of tractors and tractor-driven equipment (Mechanical) revolutionized farming practices. In contrast, crucial inputs like seeds, fertilizers, and pesticides (chemicals) are the main ones that have been produced to boost output and productivity (Kumar & Kutumbale, 2019). Rising population, increased food demand, underdeveloped rural areas, and the pitiful socioeconomic situation of Indian farmers have been the country's agriculture sector's inescapable truths since Independence. Since the Green Revolution, only a few states have emerged as the model state for agriculture due to the rise of mechanization. It is still difficult to boost production and boost farmers' earnings in smallholder agriculture like that of India. One such catalytic tool in agriculture is farm mechanization, which can help increase output and productivity by transforming many formerly subsistence farmers who worked on small holdings with animal and human power into active commercial farmers who used mechanized sources of farm power. Since skills are essential to machine-based operations, farm mechanization, or more precisely agriculture mechanization, will also relieve the farmer of the drudgery required in manual operations as well as free up wage workers. The efficient use of agricultural technology enables farmers to quickly rotate crops on the same plot of land while also increasing productivity and output. The cropping intensity is increased and the commercial ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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viability of agricultural land will be increased by growing many crops in the same area. Fishing, dairying, and animal husbandry all benefit from mechanization. In addition to ensuring the best possible use of resources like land, labour, and water, farm mechanization also saves farmers important time and lessens drudgery. This wise use of labour, time, and resources enables the timely planting of crops and sustainable intensification (multicropping), which increases production (Mehta et al.,2014). However, India's farm mechanization level is currently between 40 and 45 percent, with the northeastern states having very little mechanization compared to states like Uttar Pradesh, Haryana, and Punjab. Comparing this amount of agriculture mechanization to the United States (95%), Brazil (75%), and China (57%), it is still low Despite having lower levels of mechanization than other developed nations, the pace of agricultural growth has averaged 3.56% over the past decade (Ravi Kishore et al., 2022). The Evolution of Farm Mechanization India's agricultural situation in the years following independence was completely concealed. The nation was relying on imports to feed the population due to a serious food shortage. In order to address this issue, the "Green Revolution" of the 1960s introduced a number of novel practices, including the use of chemical fertilizers, better high-yielding hybrid seeds, irrigation system construction and expansion, and farmer education. The "Green Revolution" was shaped by technological advances like better water systems and more mechanization in agricultural operations. As a result of the "Green Revolution‘s success, India's cereal imports were minimal in the 1970s, with the exception of 1981–1982, when 2.3 million tonnes of food grains were once again imported due to severe crop failures in 1979–80. India produced enough food to be self-sufficient at the turn of the 20th century; for instance, 212 million tonnes of food grains were produced in the years 2001–2002. The best solution will be partial ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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mechanization in nations like India where labour is in plentiful supply and capital is scarce. The increasing strain of people on the land, tiny and dispersed holdings, extreme poverty, and farmer ignorance are some of the issues preventing agriculture from becoming mechanized. By 2050, the World Bank projects that half of the population of India will reside in urban areas. By 2050, the proportion of agricultural employees in the total labor force is predicted to fall from 58.2% in 2001 to 25.7%. This demonstrates the necessity to increase agriculture mechanization throughout the nation. Additionally, the employment of machinery in farming operations was made possible by the country's drastic expansion in the level of farming and global competition. One of the primary reasons for expanding farm mechanization is the lack of agricultural labour and the need to boost farm production. Agricultural mechanization has a key role to play in this development process. Following are the 17 different processes observed under four varied stages of the crop. Production Cropestablishment Weeding Fertilization Irrigation Crop Protection Harvesting

Postharvest/storage Drying

Processing

Marketing

Chopping

Packaging

Grading Winnowing Cleaning Storage

Milling Grinding Pressing

Transport

Source: Vikash Kumar & Vishakha Kutumbale, 2019. Types of Farm Mechanization Different types of equipment are used at various stages of farming: 1. Land Preparation and Planting: Tractors equipped with plows, harrows, and seed drills prepare the soil and plant seeds uniformly, ensuring optimal germination rates. 2. Crop Care and Maintenance: Sprayers, spreaders, and precision equipment assist in applying fertilizers, pesticides, and ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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herbicides accurately, reducing wastage and minimizing environmental impact. 3. Harvesting and Post-Harvest Processing: Combine harvesters, threshers, and automated sorting systems streamline the harvesting process and preserve the quality of harvested produce. Farm mechanization status in the country Purpose of farm mechanization is met through available source of farm power. Farm power available is in the form of human beings, animals, tractors, power tillers, diesel engine and electric motors. Farm power availability is calculated as per following formula using average power from each source: Farm Power Availability (kW/ha) = Farm mechanization status in the country Purpose of farm mechanization is met through available source of farm power. Farm power available is in the form of human beings, animals, tractors, power tillers, diesel engine and electric motors. Farm power availability is calculated as per following formula using average power from each source: Farm Power Availability (kW/ha) = Farm mechanization status in the country The purpose of farm mechanization is met through available sources of farm power. Farm power available is in the form of human beings, animals, tractors, power tillers, diesel engines, and electric motors. Farm power availability is calculated as per the following formula using the average power from each source: Farm Power Availability (kW/ha) = [(Number of agricultural Workers × 0.05 kW) + (Number of draught animals × 0.38 kW) + (Number of Tractors × 26.1 kW) + (Number of Power tillers × 5.6 kW) + (Number of diesel engines × 5.6 kW) +

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(Number of electric motors × 3.7 kW)] / Available net sown area in ha] (Singh & Singh, 2023) Table: Farm power availability in the country during different periods Periods

Power, kW/hr

1960-61

0.28

1971-71

0.32

1981-82

0.471

1991-92

0.759

2001-02

1.231

2005-06

1.502

2009-10

1.724

2011-12

1.92

2016-17

2.24

2018-19

2.49

2020-21

2.761

Source: S.P.Singh and K.K.Singh (2023). It is clear from the table that farm power availability in the country has increased from 0.28 kW/ha in the year 1960-61 1960 to 2.761 kW/ha in the year 2020-21. Advantages of Farm Mechanization 1. Input savings and increase in yield input to the crops. Adopting farm mechanization saves approximately 15-20% of seeds and fertilizers. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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much input cost that farmer spends on labourers. in more plants per hectare which ultimately increases the yield per hectare. Studies show that adopting mechanization increases cropping intensity by up to 5-20%. 2. Increase in efficiency efficiency of farm wors to carry out heavy work with ease in less time. by approximately 15-20 20 percent implying more time to invest in other activities. work more precisely with less error in operations like sowing, harvesting, spraying, etc. duction costs but also increase the yield and ultimately the income. 3. Social benefits help of heavy-duty machines for tilling-like operations. critical operations can be carried out by machines. and workloads which attracts young farmers towards agriculture and the rural sector. sts can reduce the vulnerability of small farm holders to socio-economic socio new employment opportunities like manufacturing, repairing, and custom machine hiring services. 4. Decrease in Cost of Work

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unit costs is to enlarge the size of the farms and go in for more intensive farming. adjusted properly if mechanization is resorted to. 5. Decrease in demand for work animals for ploughing, and water lifting process whereas the cost of maintenance of draught animals remains the same during both periods of working and idleness as animals are living beings and need to be fed whether they are doing work or not. to be done in a short time. 6. Reduces Fodder area and enlarges food area surplus animal power would be reduced so that large areas of land required for producing fodder could be utilized for producing food for human consumption. d be better attended to and better fed under mechanized agriculture, for new and nourishing varieties of feeding stuff would be grown in cultural (waste) lands after reclaiming them for cultivation. Future of Farm Mechanization in India The scale of the farm is significant today and has a direct impact on mechanization. The bulk of farms in India are modestly sized. In India, the agricultural sector is made up of 83.3 percent of small and marginal farmers. India had more than 145 million agricultural holdings between the years of 2015 and 2016 according to the 2018 Agricultural Census. About 125 million of them were marginal or small farmers. The average size was 1.41 ha between 2015 and 2016 compared to 2.3 ha between 1970 and 1971. India suffered significant land imbalances as a result, which are still evident today. It is therefore better to use it as a strength ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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(Kumar & Kutumbale, 2019). Therefore, India's future sustainable agricultural growth would mostly depend on marginal and small farmers. small farms play a crucial role in growth and the eradication of poverty. Global development and poverty reduction experiences indicate that agricultural-based GDP growth is at least twice as effective at reducing poverty as GDP growth derived from other sources (Yedk, 2022). The improvement of agricultural growth and the reduction of poverty depend heavily on small holdings. The first step should be to identify the obstacles to agricultural mechanization adoption, particularly for small and marginal farmers. So, in addition to raising production and productivity, farm mechanization will eventually aim to increase sustainable productivity. Research and development are crucial to the agriculture sector's future. Conclusion Farm automation is a dynamic technology. It changes when agriculture in a region, state, or nation changes. Farm mechanization technologies will need to be developed and adopted quickly in order to have eco-friendly sustainable agriculture with internationally competitive outputs. This can only be accomplished by diversifying agriculture and adopting frontier technology. The two objectives to be accomplished are cost reduction and quality improvement. Due to the capital-intensive nature of farm mechanization technology, all farm mechanization Research and Development projects must be demand-driven and use a reverse engineering approach. A strategical and sustainable farm mechanization will surely lead India to overcome low agricultural productivity and high input cost in long run. References Kumar V. & Kutumbale V. (2019). Perspectives of Farm Mechanization in India – A Review Paper. Journal of Gujrat Research Society.ISSN:0374-8588, Vol.21, Issue 10.

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Kumar V. & Kutumbale V. (2019). Perspectives of Farm Mechanization in India – A Review Paper. Journal of Gujrat Research Society.ISSN:0374-8588, Vol.21, Issue 10. Kumar V. & Kutumbale V. (2019). Perspectives of Farm Mechanization in India – A Review Paper. Journal of Gujrat Research Society.ISSN:0374-8588, Vol.21, Issue 10. Mehta R.C., Chandel S.N., and Senthilkumar T. (2014). Status, Challenges, and Strategies for Farm Mechanization in India. Agricultural Mechanization in Asia, Africa, and Latin America. Vol.45 no.4 Ravikishore, M., Supriya, P. and Subbaiah, K.R. (2022). Farm Mechanisation 2.0: The Future of Indian Agriculture. Food and Scientific Reports, 3(11):26 -32. Ravikishore, M., Supriya, P. and Subbaiah, K.R. (2022). Farm Mechanisation 2.0: The Future of Indian Agriculture. Food and Scientific Reports, 3(11):26 -32. Singh R. S. & Sahni K. R. (2019). Transformation of Indian Agriculture through Mechanization. Economic Affairs, Vol. 64, No. 2, pp. 297-303, DOI: 10.30954/0424-2513.2.2019.4 Singh P.S. & Singh K.K. (2023). Status and Prospects of Farm Mechanization for Sustainable Agriculture. RASSA Journal of Science for Society 5(1): 35-47. DOI: 10.5958/25833715.2023.00006.0 Singh P.S. & Singh K.K. (2023). Status and Prospects of Farm Mechanization for Sustainable Agriculture. Journal of Science for Society 5(1): 35-47, DOI: 10.5958/25833715.2023.00006.0 Yedk P. T. (2022). Farm Mechanization in India and Perspectives -A Review Paper. International Journal of Advanced Research in Science, Communication, and Technology, 2(2), DOI: 10.48175/IJARSCT-9305

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Chapter 2 Application of Modern Technology in Agriculture in India Tirunima Patle1, Bhavana Tomar2*, Sneh Singh Parihar3 1.

Assistant Professor, Department of Agriculture Sciences, IES University, Bhopal, M.P, India 2. Ph.D. scholar, School of Agricultural Sciences, GD Goenka University, Gurugram, Haryana, India 3. Research scholar, School of Agriculture, ITM University, Gwalior, M.P, India *Corresponding author: [email protected]

Abstract: The application of modern technology in agriculture has revolutionized traditional farming practices, propelling the agricultural sector towards enhanced efficiency, sustainability, and productivity. This chapter delves into the multifaceted integration of modern technologies within the agricultural domain, highlighting their profound impact on crop production, resource management, data-driven decision-making, and sustainable practices. The chapter begins by elucidating the technological foundations that underpin contemporary agriculture, including the Internet of Things (IoT), remote sensing, robotics, and precision agriculture. It subsequently explores the utilization of these technologies in optimizing various aspects of agricultural operations, such as soil management, irrigation, pest control, and livestock management. This technological integration not only enables real-time monitoring but also facilitates the implementation of customized interventions, leading to more informed and efficient practices. Furthermore, the chapter examines the socio-economic impact of modern technology on agricultural communities. It addresses concerns related to the ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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digital divide and access to technology, emphasizing the need for inclusive adoption strategies that empower all farmers. The discussion extends to agribusiness models, supply chain optimization, and market linkages, showcasing how technology facilitates seamless integration between producers and consumers, reducing wastage and increasing market access, also emphasizes the significance of resilience and adaptation in the face of evolving technological landscapes. Kew Words: Modern, Precision Agriculture, GIS, GPS, IoT & Remote sensing Introduction: India is home to 1.3 billion people, and globally ranks second in terms of the agricultural output. The agriculture, forestry and fishing sector accounted for 16.4% of the gross value added (GVA) in 2021. In contrast, the sector is serving as a primary source of livelihood for more than 50% of the country‘s population. Low and stagnant income across these sectors remains a focal point of policy debate in India. These sectors accounts for the majority of the poor of the country. Recent estimates show that about 220 million people are poor in India. One of the most prominent pathways to enhance farmers‘ income is the adoption of improved agricultural technologies. The literature reveals that adoption of improved technologies is the key to increase agricultural productivity and farmers‘ income [1]. Agriculture has been the backbone of India's economy for centuries, providing livelihoods to a substantial portion of its population and ensuring food security. However, the traditional practices that have sustained Indian agriculture for centuries are now facing unprecedented challenges. Rapid population growth, urbanization, and changing consumption patterns have escalated the demand for food and other agricultural products. Concurrently, the sector grapples with resource limitations such as dwindling water resources, soil degradation, and unpredictable ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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climatic patterns exacerbated by climate change. In this context, the need for technological innovation becomes imperative. [2]. Modern technology has emerged as a powerful enabler in this pursuit, offering solutions to enhance productivity, optimize resource utilization, and promote ecological balance. It offers the potential to enhance agricultural productivity, optimize resource utilization, and mitigate the environmental impact of farming practices. The deployment of modern technology in agriculture, often referred to as 'AgTech,' has witnessed a paradigm shift in recent years. Innovations such as precision agriculture, remote sensing, blockchain-enabled supply chains, and farmer-centric mobile applications have begun to redefine the possibilities of sustainable farming. By integrating these technologies with traditional knowledge systems, India can chart a course towards a more resilient and productive agricultural sector. [3] In the backdrop of India's diverse agro-climatic zones and complex socio-economic dynamics, the integration of modern technology in agriculture holds the promise of addressing some of the sector's most pressing challenges. From precision farming and data-driven decision-making to the deployment of advanced sensors and artificial intelligence, technology has the potential to reshape the landscape of Indian agriculture. This chapter embarks on an exploration of the myriad applications of modern technology in Indian agriculture. It will traverse the realms of precision farming, smart irrigation, datadriven decision-making, and supply chain optimization, while also addressing the challenges and opportunities presented by the digital divide and equitable technology access. Through an indepth analysis of the Indian agricultural context, this chapter aims to provide insights into how modern technology can be harnessed to foster agricultural growth, rural development, and national food security. Modern Technology Landscape in Indian Agriculture ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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The integration of modern technology has permeated various facets of Indian agriculture, ushering in transformative paradigms that enhance efficiency, sustainability, and connectivity across the sector. This chapter rigorously dissects the technologically driven advancements that have reshaped India's agricultural landscape, encompassing digital innovations, precision agriculture, data analytics, supply chain optimization, and strategies to bridge the digital divide. 1. Digital Innovations and Connectivity The burgeoning digital ecosystem has catalyzed innovation, offering platforms that foster enhanced connectivity and information dissemination throughout the agricultural value chain. Mobile applications tailored for farmers have emerged as tools of empowerment, equipping them with real-time weather updates, pest advisories, and market insights [4]. These applications synergize traditional knowledge with data-driven insights, enabling proactive decision-making aligned with the region's specific needs. a) Mobile Applications for Farmers: Mobile applications such as "KrishiSewa" and "IFFCO Kisan" have gained traction by delivering personalized recommendations that optimize crop cultivation, pest management, and irrigation scheduling [5]. These applications facilitate direct communication between scientists, extension agents, and farmers, creating a dynamic feedback loop that enhances agricultural practices. The integration of farmergenerated data and scientific expertise cultivates localized intelligence that underpins precision-oriented interventions. b) Farmer-to-Consumer Platforms: Intricately linked to these applications are farmer-toconsumer platforms that bridge the urban-rural gap, offering consumers traceability and authenticity in their produce [6]. Initiatives like "Farm to Fork" leverage technology, including QR codes and blockchain, to establish transparent supply chains that ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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resonate with consumers' evolving demands for safe, sustainable, and ethically produced food. 2.Precision Agriculture: Precision agriculture, a cornerstone of modern agronomy, maximizes resource utilization through data-driven strategies. Global Positioning System (GPS) and Geographic Information System (GIS) applications revolutionize field mapping and enable targeted resource allocation [7]. Remote sensing and satellite imagery synergistically elevate precision agriculture, providing farmers with insights into crop health, moisture distribution, and nutrient deficiencies. a. GPS and GIS Applications: GPS-equipped machinery precisely navigates fields, optimizing seed planting, fertilizer distribution, and irrigation practices. This spatially informed precision not only curtails input wastage but also cultivates sustainable practices that harmonize agricultural productivity with environmental conservation [8]. b. Remote Sensing and Satellite Imagery Remote sensing and satellite imagery furnish farmers with macroscopic views of their fields' health. Monitoring spectral indices aids in detecting early signs of stress, guiding interventions before crop losses become irreparable [9]. By transcending geographical barriers, these technologies engender global best practices in local contexts. c. IoT Sensors for Soil Monitoring: Internet of Things (IoT) sensors, embedded within fields, record critical soil metrics in real time. These data enable precise irrigation scheduling, optimizing water use while mitigating over- or under-watering [10]. The synergy between precision agriculture and IoT fortifies resilience in the face of climate uncertainties.

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Figure: 1 Smart farming representing Information and Communication Technologies (ICT) [6] 3. Data Analytics and Artificial Intelligence: The influx of big data has engendered the evolution of data analytics and artificial intelligence in agriculture. Predictive analytics harness historical and real-time data to forecast yields, enabling informed decisions and risk management [11]. Machine learning algorithms empower disease and pest detection, facilitating timely interventions to curtail losses. a) Predictive Analytics for Yield Forecasting: Predictive models, trained on historical data and weather patterns, anticipate yield fluctuations. By facilitating proactive responses, farmers can optimize resource allocation and market engagement [12]. b) Disease and Pest Detection: Machine learning algorithms process images to detect diseases and pests, expediting diagnoses and optimizing treatment strategies. This data-driven approach ensures judicious use of pesticides and promotes ecological balance [13]. c) Climate-Resilient Crop Selection:

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Artificial intelligence assists in identifying climateresilient crop varieties. Through data assimilation, models discern optimal cultivation strategies that align with changing climatic patterns [14]. 4. Supply Chain and Market Linkages: The application of technology reverberates throughout the supply chain, propelling transparent, efficient, and equitable market linkages. Blockchain technology ensures traceability, building trust between producers and consumers [15]. Emarketplaces empower farmers by granting them direct access to a wider consumer base, sidestepping intermediaries and enhancing financial returns. a. Blockchain for Traceability: Blockchain's immutable ledger authenticates the provenance of produce, assuaging concerns of adulteration and fraud. The secure platform bolsters consumer confidence and enables farmers to reap premium prices for their quality produce [16]. b. E-Marketplaces and Agri-Fintech: E-marketplaces transcend physical boundaries, connecting producers to diverse consumer segments. Agri-fintech solutions streamline payment processes and offer credit facilities, bolstering the financial resilience of farmers [17]. 5. Overcoming the Digital Divide: While technology is poised to revolutionize Indian agriculture, ensuring equitable access is imperative. Accessibility and inclusivity challenges need to be addressed through strategic interventions that empower marginalized farming communities. a. Accessibility and Inclusivity Challenges: Rural connectivity gaps hinder the adoption of technology in remote areas. Initiatives must prioritize infrastructure development, extending digital literacy and fostering local capacity [18]. b. Government Initiatives for Rural Connectivity: ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Government-led programs like "Digital India" are instrumental in bridging the digital divide. These initiatives augment access to information, markets, and financial services, catalyzing a more inclusive technological revolution [18]. Sustainable Agriculture and Resource Management The realm of sustainable agriculture, examining innovative strategies and advanced technologies that seek to balance agricultural productivity, resource conservation, and ecological harmony [19]. 1. Smart Irrigation Systems: Water scarcity is a critical challenge in agriculture, necessitating the adoption of smart irrigation systems that optimize water use. A) Drip and Sprinkler Irrigation: Drip and sprinkler irrigation are pivotal examples of precision water application methods. Drip irrigation delivers water directly to plant roots, minimizing evaporation and runoff losses associated with traditional methods. Sprinkler irrigation simulates rainfall, ensuring uniform water distribution. These approaches enhance crop water-use efficiency, resulting in improved plant growth and reduced water wastage. [20] B) IoT-Based Irrigation Management: The emergence of the Internet of Things (IoT) has revolutionized irrigation management through real-time data collection and analysis. Sensor networks placed in fields monitor variables like soil moisture levels, weather conditions, and crop health. This data is transmitted to farmers, enabling them to remotely control irrigation systems. [21] 2. Soil Health Management: The preservation of soil health is fundamental to sustainable agriculture, as it directly affects crop productivity, nutrient cycling, and ecosystem stability [22].

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A) Sensor-Based Soil Testing: Sensor-based soil testing is a cornerstone of precision agriculture. Sensors measure soil properties, including nutrient content and pH levels. This data informs targeted nutrient application, minimizing excess fertilizer use and mitigating nutrient runoff into water bodies. By tailoring nutrient inputs to actual crop needs, this technology enhances nutrient-use efficiency and reduces environmental impacts [22].

Figure 2. Sensors applied in the field of agriculture [22] B) Nutrient Management with Technology: Technological solutions have transformed nutrient management practices. Sophisticated models and data analytics assist in determining optimal nutrient application rates and timings. By synchronizing nutrient delivery with crop growth stages, farmers ensure that plants receive nutrients when they are most needed. This approach not only optimizes plant health and yield but also curbs nutrient losses, safeguarding water quality and minimizing environmental degradation [22]. 3. Climate-Adaptive Farming: Adapting agriculture to changing climate patterns is crucial for long-term sustainability and resilience. a) Weather Forecasting for Farming Decisions:

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Accurate weather forecasting aids farmers in making informed decisions in response to changing weather conditions. Early warnings about extreme events, such as storms or droughts, enable timely actions to protect crops and minimize losses. By adjusting planting schedules or irrigation plans based on forecasted weather, farmers can optimize resource allocation and mitigate risks [23]. C) Crop Planning in Changing Climates: Crop selection and management practices need to evolve in tandem with shifting climatic conditions. Farmers are now choosing crop varieties that are better adapted to changing temperature and precipitation patterns. This proactive approach reduces vulnerability to climate-related risks, ensuring consistent yields and maintaining food security [23]. Data-Driven Decision-Making in Agriculture: The realm of data-driven decision-making, elucidating its multifaceted applications and the transformative potential it offers to enhance agricultural sustainability and productivity. 1. Big Data in Agriculture: The proliferation of data sources and technologies has given rise to the concept of big data in agriculture. [24] a) 4.1.1 Data Collection from Various Sources: The agri-food system is replete with data streams from multiple sources, ranging from satellite imagery and weather sensors to market trends and soil parameter [24]. Remote sensing technologies capture data on crop health, growth stages, and water stress, providing a comprehensive overview of field conditions. b) 4.1.2 Data Integration and Analysis: The integration of diverse datasets is pivotal in unraveling complex agricultural dynamics. Geographic Information Systems (GIS) facilitate spatial analysis, enabling farmers to correlate soil properties, weather patterns, and yield data [25]. Advanced analytics techniques uncover hidden patterns, ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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offering insights into optimal planting times, fertilizer application, and disease prevalence. 2. AI in Agricultural Decisions Artificial intelligence revolutionizes agricultural decision-making through predictive modeling, pattern recognition, and real-time monitoring. The integration of AI augments the precision and timeliness of interventions. a) 4.2.1 Crop Disease Prediction: AI algorithms process vast datasets to predict the onset of crop diseases. Models assimilate historical disease outbreaks, weather patterns, and crop conditions to forecast disease susceptibility. Early detection enables proactive management, minimizing losses through timely interventions [26]. b) 4.2.2 Pest Management: AI-driven pest management optimizes pest control strategies through real-time monitoring and adaptive interventions [26]. Sensor networks detect pest presence, triggering targeted treatments that minimize chemical usage and preserve beneficial insect populations. c) 4.2.3 Irrigation Optimization: AI algorithms, combined with data from IoT sensors, optimize irrigation practices. Models analyze soil moisture levels, weather forecasts, and crop water requirements to tailor irrigation schedules [21]. This precise irrigation strategy conserves water while ensuring optimal crop growth. Socio-Economic Impact and Challenges 1 Empowering Smallholder Farmers: Technology has the potential to empower smallholder farmers, enhancing their livelihoods and economic standing. a) 5.1.1 Access to Information and Training: Providing smallholders with access to real-time information about weather patterns, market prices, and crop management practices equips them to make informed decisions. Training programs and mobile apps offer valuable knowledge, ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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enabling farmers to adopt modern cultivation techniques and improve their yields. [4] b) 5.1.2 Financial Inclusion through Agri-Fintech: Agri-fintech solutions facilitate financial inclusion for smallholders by offering access to credit, insurance, and digital payment systems. These services mitigate financial risks, enabling farmers to invest in inputs, equipment, and better agricultural practices. 2 Digital Divide and Equity: As technology advances, concerns about equitable access and bridging the digital divide come to the forefront. a) Bridging the Urban-Rural Gap: Ensuring equitable technology access between urban and rural areas is crucial. Providing reliable internet connectivity, particularly in remote regions, enables farmers to access online markets, information, and financial services. b) Gender-Inclusive Technology Adoption: Gender equity is pivotal in technology adoption. Special efforts are needed to ensure that women farmers have equal access to technological resources, training, and information. This empowers women to actively participate in agricultural decision-making and benefit from technological advancements [27]. 3 Challenges and Concerns: While technology holds immense promise, it also brings forth challenges and concerns that must be navigated. a) Data Privacy and Security: The collection and utilization of data in agriculture raise concerns about privacy and security. Farmers' data, ranging from crop information to financial details, need robust protection to prevent misuse or unauthorized access. b) Technological Literacy: Effective technology adoption requires a level of technological literacy. Ensuring that farmers, particularly those ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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from older generations, possess the skills to navigate and benefit from digital tools is crucial. Training programs play a pivotal role in addressing this challenge. [28] Conclusion and Future Outlook: The transformation of Indian agriculture through modern technology is not only a paradigm shift but a commitment to shaping a more resilient, equitable, and sustainable future. By leveraging the symbiotic relationship between technology and traditional wisdom, India's agricultural sector can navigate the challenges of a dynamic world while harnessing its inherent strengths. To witnessed the convergence of traditional wisdom and cutting-edge innovations in shaping a technology-driven agricultural landscape. From the integration of precision agriculture techniques that optimize resource utilization, to datadriven decision-making strategies that enhance crop management and pest control, it is clear that technology is shaping a more resilient and productive agricultural sector. The prospects for technology-driven agricultural growth are both promising and transformative. The integration of mobile applications, remote sensing, data analytics, and AI not only elevates productivity but also fosters sustainable practices that mitigate environmental impact. As technology continues to evolve, the adoption of smart farming practices, automated machinery, and blockchain-enabled supply chains holds the potential to streamline processes, enhance market linkages, and increase the financial resilience of farmers. While technology offers unparalleled opportunities, it is imperative to address ethical considerations and ensure the principles of sustainable development are upheld. As we venture into a digitally driven agricultural future, it is essential to safeguard data privacy, prevent technological exclusion, and ensure that the benefits of technological advancements are accessible to all stakeholders. Moreover, embracing technology must align with sustainable development goals, minimizing ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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negative environmental externalities and promoting social inclusivity. Reference: [1] Matuschke, I., Mishra, R. R., & Qaim, M. (2007). Adoption and Impact of hybrid wheat in India. World Development, 35(8), 1422–1435. https://doi.org/10.1016/j.worlddev.2007.04.005 [2] Duflo, E., Kremer, M., & Robinson, J. (2011). Nudging farmers to use fertilizer: Theory and experimental evidence from Kenya. American Economic Re[1]view, 101(6), 2350– 2390. [3] Joshi, P. K., & Tripathi, G. (2020). Transforming Indian agriculture: Role of modern technologies. In Transforming agriculture in South Asia (pp. 163–183). Springer. [4] Foster, A. D., & Rosenzweig, M. R. (2010). Microeconomics of technology adoption. Economic Growth Centre Discussion Paper no. 984. 13. International Monetary Fund IMF and CIA Factbook. Annual Review of Economics. Yale University Press, 2. https://doi.org/10.1146/annurev.economics.102308.124433 [5] Ganeshan, M. K., & Vethirajan, C. (2021). The Impact of technology and agriculture mobile applications for farmers in India, 3rd International Conference on Recent Advances in Management and Technology (ICRAMT-2020) Conference Proceeding (Souvenir), on, 8 and 9 January 2021 at Invertis University (pp. 372–376). UP. India. [6] Adhiguru, P., & Devi, S. V. (2012). ICT in Indian agriculture: Learning‟s and way ahead. International Journal of Extension Education, 8, 1–4. [7] Dhivya, B. H. et al. (2017). A Survey on Crop Yield Prediction based on Agricultural Data. International Journal of Innovative Research in Science, Engineering and Technology, 6(3), 4177–4182. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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[8] Mariani, L., & Ferrante, A. (2017). Agronomic management for enhancing plant tolerance to abiotic stresses—Drought, salinity, hypoxia, and lodging. Horticulturae, 3(4), 52–69. https://doi.org/10.3390/horticulturae3040052 [9] Paul, J., Katz, R., & Gallagher, S. (2004). Lessons from the field: An overview of the current usage of information and communication technologies for development. [10] Kempenaar, C. et al. Towards data-intensive, more sustainable farming: Advances in predicting crop growth and use of variable rate technology in arable crops in the Netherlands 13th International Conference on Precision Agriculture (ICPA), St. Louis, MO, United States, 2016. [11] O‘Grady, M. J., & O‘Hare, G. M. P. (2017). Modelling the smart farm. Information Processing in Agriculture, 4(3), 179–187. https://doi.org/10.1016/j.inpa.2017.05.001 [12] Rani, A. S. (2017). The Impact of Data Analytics in Crop Management based on Weather Conditions. International Journal of Engineering Technology Science and Research, 4(5), 299–308. [13] Dhivya, B. H. et al. (2017). A Survey on Crop Yield Prediction based on Agricultural Data. International Journal of Innovative Research in Science, Engineering and Technology, 6(3), 4177–4182. [14] Taneja, G., Pal, B. D., Joshi, P. K., Aggarwal, P. K., & Tyagi, N. K. (2019). Farmers‘ Prefer[1]ences for climate-smart agriculture—An assessment in the Indo-gangetic plain. In B. Pal, A. Kishore, P. Joshi & N. Tyagi (Eds.), Climate smart agriculture in South Asia: Technologies, policies and institutions (pp. 91–111). Springer. [15] Nedumaran, G., & Manida, M. (2019). E-marketing strategies for organic food products. International Journal of Advance and Innovative Research, 6(2), 57–60, ISSN: 2394-7780

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[16] Milovanovic, S. (2014). The role and potential of information technology in agricultural improvement. Economics of Agriculture, 61(2), 471–485. [17] Manzar, O. (2004). Adversity to success the world‟s Best eContent and Creativity Experience. The country, Global ICT Summit, paper INDIA. Digital Press Empowerment Foundation. [18] Rebekka, S., & Saravanan, R. (2015). Access and Usage of ICTs for Agriculture and Rural Development by the tribal farmers in Meghalaya State of North[1]East India. Journal of Agriculture Information, 6(3), 24–41. [19] Knudsen, M. T., Halberg, N., Olesen, J. E. et al. (2005). Global trends in agriculture and food systems: Challenges and promises. CA B International. [20] Varma, S., & Namara, R. E. (2006). Promoting micro irrigation technologies that reduce poverty. Water Policy Briefing, 23. [21] Kinzli, K. D. et al. (2011). Linking a developed decision support system with advanced methodologies for optimized agricultural water delivery, Efficient Decision Support Systems-Practice and Challenges in Multidisciplinary Domains (pp. 187–212). IntechOpen. [22] Pongnumkul, S. et al. (2015). Applications of smartphonebased sensors in agriculture: A systematic review of research. Journal of Sensors, 2015, 1–18. [23] Pal, B. D., Kishore, A., Joshi, P. K., & Tyagi, N. K. (2019). Climate smart agriculture in South Asia: Technologies, policies and institutions. Springer. [24] Sekhar, C. C. et al. (2018). Effective use of Big Data Analytics in Crop planning to increase Agriculture Production in India. International Journal of Advanced Science and Technology, 113, 31–40. [25] Cambra Baseca, C. C., Sendra, S., Lloret, J., & Tomas, J. (2019). A smart decision system for digital farming. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Agronomy, 9(5), 216–234. https://doi.org/10.3390/agronomy9050216 [26] Donatelli, M., Magarey, R. D., Bregaglio, S., Willocquet, L., Whish, J. P. M., & Savary, S. (2017). Modelling the impacts of pests and diseases on agricultural systems. Agricultural Systems, 155, 213–224. https://doi.org/10.1016/j.agsy.2017.01.019 [27] Manida, M., & Nedumaran, G. Impact of E-communication on agriculture development through CSR in Agri-farmer in Rajapalayam taluk. International Journal of Analytical and Experimental Modal Analysis(XI), 106–114. ISSN NO: 08869367. [28] Singh.K.M and Kumar. A. (2015). Role of information and communication technology in Indian agriculture: An overview. SSRN Electronic Journal.

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Chapter 3 Monitor and Control Crop Irrigation System through Smart Sensors Ayushi Trivedi1, Nirjharnee Nandeha2, J.Himanshu Rao3, Smita Agrawal4, Amit Kumar5,R.S.Dangi6 1,3

Department of Soil and Water Engineering, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, 474002, Madhya Pradesh 4,5 Department of Horticulture, College of Agriculture, Khandwa – Rajmata Vijayaraje Scindia Krishi VishwaVidyalaya, Gwalior, Madhya Pradesh 2 Department of Agronomy, Indira Gandhi Krishi Vishwavidyalaya, Raipur, 492001, Chhattisgarh 6 Department of Agronomy, College of Agriculture, Khandwa – Rajmata Vijayaraje Scindia Krishi VishwaVidyalaya, Gwalior, Madhya Pradesh

Abstract The developing world, where agriculture and climatic factors dominate the economy, served as the inspiration for this chapter. Making the proper operational decision at the right time is essential for production farming profitability based on the circumstances at hand and past performance. Precision farming is a methodical approach created to increase agricultural productivity by precisely adjusting crop and soil management to each field's unique needs while maintaining environmental quality. In order to remotely measure the environmental factors in an agricultural field, this study emphasizes the creation of an automated irrigation system with portable wireless sensor networks and decision support techniques. Ecological characteristics like soil moisture, temperature, humidity, and light intensity are recorded via radio satellites, mobile phones, sensors, internet-based connectivity, and microcontrollers. Using IoT technology, the data gathered from the sensors is sent straight to ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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the cloud server. They can be viewed by users from anywhere in the globe using a device with internet access. Modern agriculture is made more efficient by the development of sensor-based applications that are potentially productive and cost-effective. Various restrictions have been mentioned in the previouslyexamined publications, such as the lack of power in the field, which can be overcome by employing a solar panel that simultaneously uses electricity to replenish the battery. Design system optimization is the major factor that improves Bluetooth application in the agricultural industry. Keywords: IoT, Modern agriculture, Smart Sensors, Radio satellites & Bluetooth application 1. Introduction Most economies around the world are based on agriculture because it produces a large portion of the GDP and ensures food security (World Bank, 2020d). Contrarily, due to its use of 70% of the world's freshwater resources to irrigate 25% of its cropland, agriculture has been recognized as a key water user industry (FAO, 2020a, FAO, 2017a, Khokhar, 2017). Resources that are essential for agricultural output, such water availability, are under extra strain from climate change and population growth (Ungureanu et al., 2020). The Anon (2019) predicts that by 2050, there will be 9.7 billion people on the planet, increasing need for nutrient-rich food and clean water. By 2050, the Food and Agricultural Organization (FAO) projects that irrigated food production will have increased by more than 50%, necessitating an increase in water abstraction for agriculture of 10% (FAO, 2020b). Agriculture cropping systems must make optimal use of the available water and land resources because the land on which food is grown does not expand in order to feed the world's population in the future. Therefore, it is crucial to comprehend the mechanisms that might increase water usage efficiency, lead to large water savings, and increase yield. The ratio of actual water ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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withdrawal to predicted irrigation water demand is known as water use efficiency in irrigated agriculture (FAO, 2020a). Efficiency in water use is a dimensionless concept that may be applied to plants, fields, plans, basins, and entire nations. However, according to agronomists (Hatfield and Dold, 2019, Sharma et al., 2015, Ullah et al., 2019, Unver et al., 2017), water use efficiency is defined as crop yield divided by the amount of water needed to create the yield. Researchers have paid a lot of attention to water use efficiency as a result of the effects of climate change, which are causing water scarcity to continue to vary globally in both place and time (Hess and Knox, 2013). Agriculture experts, irrigation engineers, and policy makers have had to reevaluate how water is used in agriculture due to competition from other economic sectors for the limited water supply. It appears that in order to address the falling land base and water allocations to meet agricultural production needs, state-ofthe-art approaches to water management and systems will need to be used. A higher water use efficiency is ensured by the application of precision agricultural technologies (Evans and Sadler, 2008). Precision farming is at the center of shaping itself to offer solutions to the industry's major issues. Precision agriculture is the "use of technologies that integrate sensors, information systems, improved machinery, and informed management to improve production by accounting for dynamics within sustainable agricultural systems," according to Yin et al. (2021). Farmers can conserve valuable resources by using smart irrigation and precision agriculture, in particular, without endangering plants' ability to absorb moisture (Pierce, 2010). Water must be applied to a field at the proper time, in the proper quantity, and at the proper location (Singh et al., 2019). As a result, monitoring and control tactics must be used for the best irrigation schedule while taking into account the variations in soil moisture conditions, shifting weather patterns, and plant physiological circumstances. Conventional irrigation systems apply irrigation ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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water without taking into account variations in weather factors and spatiotemporal variation of soil properties that affect crop evapotranspiration (Vories et al., 2021). The actual depth of irrigation water that plants receive as a result varies spatially. Inadequate irrigation can cause plant stress, which ich can lower crop production and quality, while applying more irrigation water than is necessary results in fertiliser leaching, deep percolation, surface ponding, and runoff. In order to implement an optimal irrigation schedule using a smart irrigation system, ystem, sensors that track soil, plant, and weather variables are needed. Irrigation control, on the other hand, deals with the distribution of inputs and making the necessary modifications in accordance with the crop response to conserve irrigation water while minimizing the effects of disturbances and uncertainties. Several survey articles on increasing water use effectiveness in irrigated agriculture have been published in recent years. To the best of our knowledge, there do not appear to be many systematic literature evaluations on the use of monitoring and control systems for increasing water use efficiency. The authors fall short in demonstrating how monitoring and control techniques improve precision agricultural water use efficiency. By including bothh intelligent agricultural water use monitoring tools and irrigation control techniques to increase water use effectiveness.

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Fig 1: Climate Smart Agriculture 2. Automatic Irrigation is required for (i) Conserving energy and resources for careful use. (ii) Simple system installation in the field. (iii) To apply the proper quantity of water at the proper time in order to make it easier for farmers to manage farm irrigation and nurseries. (iv) In automated irrigation systems, valves are employed to turn on and off the motor. (v) A sensor-based controller makes it simple to operate a pump or motor, negating the need for manual labour to manage or keep an eye on irrigation systems. Crop efficiency involves reducing overwatering from saturated soil and avoiding irrigation at the wrong time to conserve more water. The agriculture industry uses a huge amount of freshwater because there are no affordable irrigation solutions. Fortunately, earlier attempts to deploy agricultural monitoring systems for irrigation practises were fraught with issues that impeded the development of this industry. In the past, cable data collecting systems were used in farm surveillance systems to connect sensor units to monitoring stations. In climatology and geography, rainfall and evapotranspiration play a significant role in determining the soil's moisture content. Soil moisture is determined by the ratio of monthly (or annual) evaporation to precipitation. In the above context from the regular weather data, the ratio of daily evaporation and precipitation can also be used to compute the daily soil moisture. Evaporation is derived from other metrological necessities, while precipitation is directly available. It is possible to organise intelligent algorithms for analysing scientific knowledge to give a better grasp of the ongoing processes. Heterogeneous systems are utilised to collect information at a higher level. Different terms, such as Precision Farming, Variable Rate Technology (VRT), Smart Agriculture, Global Positioning Service (GPS), and Site-Specific Crop Management, are used to refer to sensors-based irrigation. Thanks ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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to contemporary technology and their tiny size, sensors can now be employed in the field of human life. Numerous issues with sensor networking are being researched as a result of this technology. Various academics from around the world are working to find solutions to some of the major problems with sensor networks, including low memory, energy constraints, and data security. 3. Technology for Smart Irrigation: New Controllers The use of smart irrigation technology spans a wide range, including advantages for consumers. It's crucial to select the best technology for the job if you want to save water. Some sections of Oklahoma have watering limitations; thus, the irrigation timer can be changed to reflect the permitted watering days. Climate-based controllers and soil moisture-based controllers are the two primary divisions of irrigation controllers. Controls Based on the Climate Climate-based controllers, also known as evapotranspiration (ET) controllers, modify irrigation schedules based on local meteorological information. Evapotranspiration is the result of both soil surface evaporation and plant substance transpiration. These climate-based controllers collect information about the local weather and modify the irrigation run-time so that the landscape only receives the right amount of water. Three different types of ET controllers can be found: Signal-based controllers generate the ET value for a grass surface at the site using meteorological data from a publically accessible source. The controller receives the ET data after it has been wirelessly transmitted to it. A pre-programmed water consumption curve, based on historical water use in various regions, is used by historic ET controllers. Temperature and solar radiation can be accounted for in the curve. On-site weather measurement controllers employ weather information gatheredonsite to determine how much water and ET should be measured continuously. It has been demonstrated that evapotranspiration ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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controllers lower outdoor water consumption. Properties in Las Vegas, Nevada, with ET-based controllers experienced an irrigation reduction of 20 percent on average compared to properties with homeowner-scheduled irrigation (Devitt et al., 2008). In addition, a study on St. Augustine turfgrass revealed an irrigation cost savings of 43 percent on average over the summer compared to homeowner-scheduled irrigation, with no compromise to the quality of the turfgrass (Davis et al., 2009). The equation parameters determine how accurate ET controllers are. Costing from $250 and $900 on average, ET controllers. ET controllers of professional grade cost between $900 and $2,500. Controllers for Soil Moisture Sensors Soil moisture sensor controllers are a part of the second category of smart irrigation controllers. Soil moisture sensor controllers use a soil moisture sensor installed underground in the root zone of lawns to assess water need rather than relying on weather information. The volumetric water content of the soil is estimated by the soil moisture sensor. The percentage of the total soil volume that is taken up by water is known as the volumetric water content. Once the volumetric water content reaches a userdefined threshold, the controllers can be set to open the valves and begin irrigation. The suitable threshold number varies from roughly 10% to 40% depending on the type of soil and vegetation. In a representative area of the grass, far enough away from sprinkler heads, tree roots, walkways and walls, soil moisture sensors must be set. Soil moisture controllers have been demonstrated to reduce irrigation while preserving turfgrass quality, much like ET controllers. Soil moisture controllers had an average 72 percent irrigation savings and a 34 percent water savings during drought circumstances when compared to homeowner irrigation schedules (Cardenas-Laihacer et al., 2010; Cardenas-Laihacer et al., 2008). Some studies have demonstrated that smart controllers will boost water use in locations where actual water use is often lower than the required amount for irrigation (Mayer and Deoreo, 2010). ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Controllers for soil moisture sensors typically cost between $280 and $1,800. Pricing varies depending on the product's maker and the end user, which might be either residential or commercial clients. The use of irrigation accounts for 70% of all freshwater withdrawals worldwide. India uses over 90% of its freshwater for irrigation. Only 5% of the water in India is reportedly used for residential purposes. Despite this, farmers frequently experience below-average crop yields as a result of poor irrigation management techniques. What if farmers could keep an eye on the moisture levels in their fields? They could make cost-effective irrigation decisions with the use of this information. Farmers can now optimise their water use, enhance crop production, grow high-quality crops, lessen the degradation of water resources, and save a lot of money by monitoring soil moisture. Farmers will be able to better grasp the actual soil water status and how much their crops will actually need to utilise by monitoring soil moisture. Making knowledgeable irrigation judgements about when to irrigate and how much water to use is made possible by soil moisture sensors be supplied to prevent manufacture of poor quality. Some of these issues that farmers frequently ask can be simply answered with the aid of soil moisture monitors.  When should I irrigate my garden?  How can I tell if the earth has absorbed enough water?  How deep does irrigation water irrigate the soil?  Am I wasting too much water? Too much or the correct amount?  How much water loss from soil evaporation should I anticipate?  How much water has been absorbed by the crop roots?  How much reserve will my crop have access to during the days when irrigation is not present? Temperature Sensor ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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As the research's main focus, the temperature of farmland microclimate, temperature time series data are arranged, divided into different time granularities, and trend characteristics are evaluated. The different temporal granularity is predicted using the neural network prediction method and the conventional time series analysis approach, respectively. The most popular techniques for assessing time series data include classic time series prediction models and data-driven time series forecasting models. Temperature series are time series data. Systems for real-time irrigation scheduling Irrigation schedules that are updated in real-time attempt to boost harvest yields and decrease crop water stress through the management of soil moisture. Water is needed for crops' evapotranspiration (ET), also referred to as evaporation and transpiration (E&T). However, a number of plants suffer from an excess of water. The stage of growth, climate, and type of crop are taken into account when determining how much water plants require. Therefore, irrigation strategies that increase water efficiency are planned. Weather and soil monitoring The growth and development of plants are influenced by efficient and effective monitoring systems, which are also crucial for developing an irrigation control system that will increase food output while minimizing water loss. Utilizing the Internet of Things (IoT) and wireless sensor networks (WSN), monitoring in the specific context of precision irrigation promotes the collection of data that effectively reflects the status of the plant, soil, and weather of irrigation regions. IoT has enabled the development of a low-cost technology approach that has improved the control and monitoring system for the irrigation process in order to construct a real-time system of monitoring. Additionally, WSN makes a substantial contribution to precision farming's real-time monitoring. In this method, a wireless sensor network is created ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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in order to sense, compute, and communicate data on various parameters. One crucial factor needed for the growth of the plant is considered to be soil moisture. To maintain an ideal irrigation schedule, effective soil moisture content monitoring may be considered essential. The low-cost capacitance-based type of soil moisture sensing is primarily dependent on the operation of a dielectric device. Soil monitoring uses well-based technology in the SMART irrigation system to measure the amount of soil moisture present. In order to accurately measure the amount of soil moisture and transfer the reading to the controller, soil moisture sensors are buried in the root zone of trees, shrubs, or turfs. By employing this technique, the crucial information is carried forward to comprehend how the linked tasks must be planned and executed for the best results. Water on-demand irrigation and suspended cycle irrigation are the two main soil moisture sensor-based technologies. A suspended cycle operates more like a standard timer controller with watering, duration, start, and finish schedules. The distinction is that when the soil has enough moisture, the irrigation system will automatically halt the following scheduled irrigation. Contrarily, water on demand irrigation doesn't need any kind of programming or set amount of time to run. In this system, the user sets the threshold, and irrigation begins when the soil moisture falls below the necessary levels. Additionally, weather monitoring is the practice of evaluating the local and surrounding weather for a significant crop area. In order to detect risks and create methods for mitigating adversities, the environmental circumstances of the work area are studied, in other words. Once more, the deployment of WSN might be seen as a crucial method for connecting various sensors to one another in order to monitor the physical environment. Through a feedback loop, real-time monitoring and data analysis from installed sensors are carried out, and this ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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further activates the control devices. Another Internet of Things (IoT)-based weather monitoring system has been built, which enables the monitoring and evaluation of the crop's environment, including humidity, air temperature, wind speed, sun radiation, and soil moisture control. It makes use of weather-based sensors that are connected to wireless communication standards to send real-time data. By employing this technique, it becomes possible to obtain comprehensive weather data, which helps in the development of techniques that could support irrigation in the long run. Management of water Regarding irrigation, water management could be seen as a critically important idea. The lack of access to clean water has become a problem on a global scale, thus the agricultural and other businesses must pay close attention to and concentrate on this problem. To ensure that the ideal amount and timing of water application, water management could be thought of as the management of soil moisture. For the agricultural industry, efficient water management is essential since it can lower costs and increase crop yield. Water management is essential because it enables agriculture sector organizations to manage resources and complete necessary tasks in a timely manner. Given that diverse initiatives are being undertaken at varied scales, it is crucial to know whether or not they will be successful. Natural resource conservation is a growing area of concern for organizations in the modern world as a result of the limited availability of these resources. Water is one of the most important and practical resources in this regard, and it must be preserved and safeguarded in whatever way possible. Due to the substantial water consumption involved in irrigation processes, the organizations involved and connected to these activities must be very careful and passionate about finding solutions to optimize water utilization. To give the agriculture industry several advantages, efficient water management technologies are needed. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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The external environment is very unpredictable, which could have a considerable impact on agricultural activity. For instance, rising fuel costs may result in higher irrigation water pumping costs. The cost of pumping irrigation water will go up if the price of fuel goes up, which could affect how efficiently the project is run overall. Organisations may be able to build larger reservoirs through water management and adapt to alternative techniques, which could minimise the risks and negative effects. Understanding the fundamental relationships between soil, crops, and water is considered to be one of the most important aspects in efficient irrigation water management. Ample knowledge of the procedures and end products must be gained in order to effectively perform the necessary agricultural operations (irrigation). Only if such information is acquired may the needed activities be carried out in a sufficient manner. Without enough knowledge and information, it would be impossible to investigate how irrigation activities must be managed and controlled under challenging circumstances, which could ultimately result in worse overall performance. Water management is important for a number of reasons, and achieving the highest level of work efficiency could be one of them. It may be possible to ensure that agricultural crops receive the necessary application of water in arid locations and during seasons of low rainfall by using water management techniques. Due to the fact that several projects are being undertaken in regions with inadequate water supplies, attention must be given to water management in order to ensure that water is distributed and applied as needed. Additionally, rainfall is very scarce in many regions, thus it is necessary to store a lot of water to ensure that the lack of rainfall can be overcome. The lack of confidence and vulnerability heighten the need for effective water management so that future water needs may be fulfilled and activities don't halt. In the modern world, a considerable amount of water is lost through various uses, such irrigation. The need for better ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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management is exacerbated by the massive amounts of water that are being wasted without being used in any way. It is necessary to establish strategies and procedures that would reduce resource waste and result in efficient resource use. The necessity for water management is also exacerbated by the possibility that there won't be enough clean water in the future. The agricultural industry employs a variety of approaches and techniques for managing water, and each of these approaches has advantages and restrictions of its own. One or more of them might be described as:  Measure, metre, and manage  Reverse osmosis can be controlled, reverse osmosis can be avoided, rainfall can be collected, and reservoirs can be built. These could be mentioned as some of the agricultural industries' most successful water management techniques. While these techniques might lead to effective water management, much will depend on their execution and effectiveness in terms of how well the results can be predicted.

Fig 2: Internet-of-Agro-Things (IoAT) based Agriculture Cyber-Physical System (A-CPS) ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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4. Conclusion To cut down on water waste and automate the irrigation system for sizable crop regions, a smart irrigation and monitoring system has been proposed. The system primarily keeps track of the behaviour of soil moisture, air humidity, and air temperature to assess how much water a plant requires. The system employs machine learning and analyses the difference between the threshold value and the actual values collected from sensors. Following this procedure, machine learning compares the results with the weather forecast and determines if irrigation is required or not. The farmer can choose to turn on the water pump with a button click after receiving a notification on his smartphone. The system also features a web app, which is useful if the farmer ever wishes to view the statistical sensor data and evaluate the change in sensor readings over time. Additionally, the system can be adjusted for different plant types, and the customer is given a list of plant options in both his web app and mobile app. With this, the user is able to select the specific type of plant being grown, which results in a more accurate threshold value and irrigation prediction. Additionally, if there is no internet connection, an SMS system can be integrated. With this, the user would receive an SMS alerting him to the prediction, and he could choose whether to turn on or off the water pump by reacting to the SMS. References Cardenas-Lailhacar, B., M. D. Dukes, and G. L. Miller. 2008. Sensor-based automation of irrigation on bermudagrass, during wet weather conditions. Journal of Irrigation and Drainage Engineering. 134(2): 120-128. Cardenas-Lailhacar, B. and M. D. Dukes. 2008. Expanding disk rain sensor performance and potential savings. Journal of Irrigation and Drainage Engineering. 134(1):67-73. Cardenas-Lailhacar, B., M. D. Dukes, and G. L. Miller. 2010. Sensor-based automation of irrigation on bermudagrass, ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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during dry weather conditions. Journal of Irrigation and Drainage Engineering. 136(3): 184-193. Davis, S. L., M. D. Dukes, and G. L. Miller. 2009. Landscape irrigation by evapotranspiration-based controllers under dry conditions in southwest Florida. Agriculture Water Mgmt. 96(12): 1828-1836. Devitt, D. A., K. Carstensen, and R. L. Morris. 2008. Residential water savings associated with satellite-based ET irrigation controllers. Journal of Irrigation and Drainage Engineering. 134(1): 74-82. Mayer, P.W. and Deoreo, W.B. 2010. Improving urban irrigation efficiency by using weather-based ―smart‖ controllers. American Water Works Association. 102(2):86. M. Ayaz, M. Ammad-Uddin, Z. Sharif, A. Mansour, and E. M. Aggoune, ―Internet-of-Things Smart (IoT)-Based Agriculture: Toward Making the Fields Talk,‖ IEEE Access, vol. 7, pp. 129 551–129 583, 2019. Trivedi A, Awasthi MK, Gautam VK, Pande CB, Din N (2023) Evaluating the groundwater recharge requirement and restoration in the Kanari river, India, using SWAT model. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-023-03235-8. Trivedi, A., and Awasthi, M. K. (2021). Runoff estimation by Integration of GIS and SCS-CN method for Kanari River watershed. Indian Journal of Ecology, 48(6), 1635–1640. agrocares, ―What is the difference between precision, digital and smart farming?‖ 2019, last Accessed on 08 May 2020. J. Lowenberg-DeBoer, The economics of precision agriculture, Nov 2018, pp. 461–494. A. Gupta, H. P. Gupta, P. Kumari, R. Mishra, S. Saraswat, and T. Dutta, ―A real-time precision agriculture monitoring s ystem using mobile sink in wsns,‖ in 2018 IEEE International Conference on Advanced Networks and ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Telecommunications Systems (ANTS). IEEE, 2018, pp. 1– 5. S. K. Ram, S. R. Sahoo, B. B. Das, K. K. Mahapatra, and S. P. Mohanty, ―Eternal-Thing: A Secure Aging-Aware SolarEnergy Harvester Thing for Sustainable IoT,‖ IEEE Transactions on Sustainable Computing (TSUSC), vol. XX, no. YY, p. Accepted on 08 Apr 2020, 2020. N. Ahmed, D. De, and I. Hussain, ―Internet of Things (IoT) for Smart Precision Agriculture and Farming in Rural Areas,‖ IEEE Internet of Things Journal, vol. 5, no. 6, pp. 4890– 4899, December 2018. S. Shekhar, J. Colletti, F. Munoz-Arriola, L. Ramaswamy, C. Krintz, ˜ L. R. Varshney, and D. Richardson, ―Intelligent Infrastructure for Smart Agriculture: An Integrated Food, Energy and Water System,‖ arXiv CoRR, vol. abs/1705.01993, 2017. S. Saraswat, H. P. Gupta, T. Dutta, and S. K. Das, ―Energy efficient data forwarding scheme in fog-based ubiquitous system with deadline constraints,‖ IEEE Transactions on Network and Service Management, vol. 17, no. 1, pp. 213– 226, 2019.

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Chapter 4 Sustainable Prospects for Modern Agriculture Technology 1

Ayushi Trivedi1, Nirjharnee Nandeha2

Department of Soil and Water Engineering, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, 474002, Madhya Pradesh 2 Department of Agronomy, Indira Gandhi Krishi Vishwavidyalaya, Raipur, 492001, Chhattisgarh

Abstract Despite being familiar with agricultural processes, the agriculture business is becoming more data-centric and demands precise, more cutting-edge data and technologies than previously. Different information and cutting-edge communication technologies, like the Internet of Things (IoT), are advancing the agriculture industry. The quick development of these advanced technologies has transformed virtually every other business, including advanced agriculture, which has gone from a statistical to a quantitative approach. The most recent prospects in a succession of difficulties have been caused by this radical upheaval, which has rocked traditional farming methods. This indepth chapter sheds light on the IoT's potential to promote agriculture as well as the difficulties in integrating these cuttingedge systems with traditional agricultural ones. These cuttingedge technologies with sensors are briefly examined in relation to sophisticated agricultural applications. Best management practises are necessary for many sensors that can be used for particular agricultural practises (such as land preparation, irrigation systems, pest and disease management, etc.). This study takes into account the integration of all practical methods, from planting through harvesting, packaging, and shipping, as well as cutting-edge farming technologies. The use of additional tools, such as ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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unmanned aerial vehicles (UAVs), for agricultural monitoring and other beneficial actions, such optimising crop yields, is also highlighted in this chapter. Additionally, sophisticated IoT-based programmes are also covered. Finally, based on our in-depth analysis, we discovered cutting-edge prospects for the Internet of Things, which are crucial tools for sustainable agriculture. 1. Introduction Major technological breakthroughs have been put into place across a large portion of human history in order to increase agricultural production with limited resources. A threat to the balance between supply and demand for food is always posed by a growing population and climate change. According to estimates, the world's population will expand by around 25% to 9 billion people by the year 2050. The population will nevertheless rise faster, mostly in developing nations like Mexico, India, China, and others. By 2050, developing nations can also anticipate an acceleration of the urbanization trend. By 2050, 70% of the world's population, up from 49% today, is anticipated to reside in cities. In addition, since living standards are predicted to rise in the future, there will be an even greater demand for food, particularly in developing countries. We should be more careful about nutritional values and food quality due to the ongoing expansion in world population. By 2050, food output should quadruple in order to fulfil projected food demand. To meet the global food demand by 2050, it is specifically recommended that grain crops and meat output be expanded from 2.1 billion to 3 billion tonnes and 200 million to 470 million tonnes, respectively. Indeed, several nations' economy depend heavily on crops like chewing gum, cotton, and rubber. Additionally, markets for crop-based bioenergy have lately started to expand. Even five years ago, the manufacture of ethanol utilized 110 million tonnes of coarse grains, or nearly 10% of total world production. The growing use of agricultural crops for the creation of biofuels, bioenergy, and other industrial uses is a danger to food security. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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The need for biofuels and bioenergy has increased strain on already limited agricultural resources. Unfortunately, only a small portion of the Earth's surface is suitable for agricultural usage due to several limitations (e.g., temperature, geography, soil, and water quality). In addition, due to economic and political variables including population increase, climate change, and landuse patterns, the amount of farmed land is further constrained, and the rapid urbanization process is continuously exerting pressure on the supply of arable land. Therefore, these elements would pose serious risks to crop production because the amount of arable land is declining. Additionally, the scarcity of arable land over the past few decades has already lowered crop production. For instance, in 1991 there were 19.5 million square miles of arable land for crop production, which accounted for 39.47% of all agricultural land. By 2013, however, that area had decreased to 18.6 million square miles, or about 37.73% of all agricultural land worldwide, and it is predicted that this trend will continue. As a result, a wider disparity between the supply and demand of food is seen, which is alarming at first. Furthermore, soil properties, topography, and climate all affect the type and yield of crops. Major characteristics that affect adaptability and crop quality include nutrients that are available, soil types, soil health, insect resistance, and the quality and quantity of irrigation. Crop traits and yields can typically vary within a same farm plot. Therefore, to get the best yields, precision farming or site-specific analysis is needed. Growers and farmers must employ multiple cropping, mixed cropping, year-round cropping, or intensive cropping in addition to improving crop productivity. Growers and farmers need the most recent/advanced technology and services-based strategies to produce more food on less land and with less resources in order to meet these needs. For the purpose of taking the necessary actions to increase crop yield, farmers and growers must monitor their croplands on a nearly real-time basis. Smart agriculture is necessary as a result of this. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Currently, rather than doing actual farm work, farmers and growers must spend 70% of their time observing and comprehending the status of the crops. Therefore, to progress the agriculture industry, accurate and cutting-edge technology are needed. Climate and environmental factors should also have a limited impact on sustainable agriculture. The "on-site monitoring" features of the Internet of Things (IoT), which enable producers to monitor farms remotely, help them achieve better results. Wireless sensors make it easier to accurately continuously monitor crops, and most significantly, they can spot problems when crops or plants are still in the early phases of growth. The newest instruments and technologies improve agricultural activities during the crop growth stages, including crop harvesting, cropping material transportation, and storage conditions. Advanced technology, including the Internet of Things (IoT), is important during these stages of a crop's development because it helps growers complete their tasks quickly. For crop monitoring, growers can now benefit from a number of technologies, such as harvesters, robot weeders, unmanned aerial vehicles (UAVs), and other autonomous machinery. Several sensors have been put in the area to serve this purpose. These sensors quickly offer data on the state of the crop, the soil, and related variables. The newest agricultural technology includes the Internet of Things (IoT), which aids in gathering data from the field. New technologies, including the IoT, are currently being developed by numerous institutions and businesses for efficient farm management. These technologies minimize related inefficiencies while assisting in achieving the best outcomes and advancements in their respective domains. Farmers can afford the newest technology, but they have limited access to knowledge about the Internet of Things. Despite this, the Internet of Things (IoT) is seen as an emerging technology for sustainable agriculture and is better adapted by farmers to gain improved ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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agricultural productions. The scope of various services, including information related to the cloud-based sensors, automation of various farming operations, and effective decision-making are all made possible by these new technologies, such as the Internet of Things (IoT). The Internet of Things will revolutionize the agricultural business, which is the main economic sector and the foundation of the nation. Additionally, it highlights the major difficulties in using this technology in advanced agriculture, including a fragmented market, insufficient connectivity and coverage, investment, a lack of new suitable equipment, a shortage of competent labour, and others. Scientists, researchers, and engineers are creating a variety of innovative technologies and ways to monitor crops and associated field data in order to advance the agriculture industry. To capture real-time data at much higher resolutions, several manufacturing agencies are approaching to supply various tools and gadgets including robots, IoT sensors, and UAVs. Federal and non-federal organizations are collaborating for this reason to improve IoT applications to maintain food safety and security. There are numerous initiatives to highlight the IoT's contribution to agriculture. The majority of the data that had previously been published either did not provide sufficient insights or simply concentrated on different IoT-based designs, prototypes, enhanced approaches, and how to use the IoT for monitoring applications and associated environment and agricultural data management. They concentrated on the device, network, and application layers of the IoT architecture. 2. Significant Applications and Services for Agriculture Advanced technologies, such the Internet of Things and unmanned aerial vehicles, have altered and merged with all of the conventional methods. Currently, the use of various wireless sensors and Internet of Things sensors opens the door to a number of breakthroughs for crop development. These new emerging technologies are currently addressing a number of conventional ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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crop challenges, such as disease management, efficient irrigation, cultural practices, and drought responses.

Fig 1: Significant Applications and Services for Agriculture 2.1 Soil surveillance In order for plants to grow, the soil is essential. At the field scale, soil monitoring is essential. A grower can choose wisely at various stages of a plant's development by learning about the health of the soil. The primary goal of soil analysis is to quantify the amount of nutrients that are present in the soil, which in turn inspires several treatments to meet the level of nutrients. Surprisingly, the soil test is recommended every year during the spring season; but, depending on the local environmental factors, it may be undertaken during the winter or the fall. To analyze and calculate the amount of fertilizers and irrigation needed, it is advised to consider a number of soil characteristics, such as soil types and soil moisture. These fundamental components also aid in shedding light on other important factors, such as physical, chemical, and biological ones. Because certain plants have deeply ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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rooted plants while others have shallowly rooted plants, the soil map aids in identifying regions with appropriate soil health and texture for the compatibility of seeds, sowing timing, and even sowing depth. To track the health of soil, many academics have lately created methods, technologies, and equipment. The main resources for farmers and producers that can be used to track soil characteristics such water holding capacity, moisture, chemical, and physical aspects are these new technologies. These techniques, which include salinity, pH, soil organic carbon (SOC), electrical conductivity (EC), nitrogen, potassium, and phosphorus, also assist in assessing the amount of fertilizer that is necessary. 2.2 Irrigation 97% of the water on Earth is in the oceans, however it is salt water. Freshwater makes up the remaining 3% of the total, of which glaciers account for 2% to 3%. One-half of the three percent of freshwater is in the form of surface water, and the other half is in the form of groundwater. To survive, people must rely on the 0.5 percent of freshwater available. Storage of freshwater in lakes, rivers, and other reservoirs is included in this 0.5 percent. Nearly 75% of freshwater is used for agriculture. The demand for crops has climbed to 75% in many nations, including Brazil. The true reason for the ad hoc delivery of irrigation water is the lack of appropriate monitoring systems to estimate crop water requirements, among other limitations. For instance, the United States consumes 80% of its freshwater for irrigation. In 2013, the United Nations Convention to Combat Desertification (UNCCD) reported that there is a global water scarcity for irrigation in around 168 countries. According to research, there will be a severe global water deficit for irrigation by 2030. Freshwater will only be accessible to nations that have adopted the finest water resources management practices due to the rise in irrigation water requirements. Effective irrigation practices should ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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be adopted as part of awareness campaigns to lessen the water constraint. To cut down on losses and water shortages, many novel irrigation techniques are deployed, such as sprinkler and drip irrigation systems. Traditional methods, such as irrigation in furrows and floods, waste water. Additionally, many soil nutrients are diminished by traditional irrigation methods (such as flooding) due to water losses, which have an effect on crop productivity. Intelligent irrigation systems are among the robust technologies and instruments that are required. Using information about soil types, previous soil moisture, and weather patterns, the smart irrigation techniques assist in estimating the amount of water that crops will need. The enhancement of crops is eventually a result of the major contribution made in this circumstance by numerous new tools, such as the Internet of Things. 2.3 Crop Disease and Its Management Due to Phytophthora infestans, which caused the late blight of the potato and numerous crop losses, there was a famine in Ireland. About a million people in Ireland perished as a result of this famine. Southern maize leaf blight, brought on by Cochliobolus heterostrophus, resulted in crop losses of close to one billion dollars in the USA. Canada later caught this crop disease. According to the Food and Agriculture Organization (FAO), crop diseases result in yearly crop losses of 20% to 40%. To make up for these losses, a number of agricultural management techniques were used, including the application of insecticides and fungicides. Since the turn of the last century, these techniques have been used in sophisticated agriculture. Advanced technologies (such as the IoT, wireless sensors, and UAVs) are helpful for disease diagnosis and pest management even though treatment, perception, and assessment are crucial in disease forecasting, monitoring, and management. For instance, remote sensing technologies can be used to economically capture vast tracts of crops. The environment, plant health, disease and ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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pests, and crop processing can all be studied using the remote sensors. The low cost of automatic activation and recovery support are just two benefits of this remote sensing tool. For instance, in recent years, poor pollination practices have seriously impacted the yields of numerous crops. 2.4 Fertilizer In order for plants to grow, develop, and reproduce, they need to be provided with the essential nutrients via fertilizers or organic amendments. Three important nutrients are required for the growth of flower, fruit, and root: nitrogen for leaf growth, phosphorus for root growth, and potassium for the expansion of stem and water passage in plant tissue. The imbalanced application of these fertilizers to the plant can have an impact on its health. On the other hand, using too much of these nutrients and fertilizers can harm the environment, including the soil, water, and air quality, as well as the health of the plant and result in financial losses. In the case of nitrogen, for instance, only half of the entire amount is used for crop development and the other half is released into the environment. A harmful effect on the ecology and climate results from the unbalanced application of undesirable nutrients to the crops. Additionally, it raises the soil's nutrient content. For the precise estimation of the rate of fertilizers in smart agriculture, new technology like the IoT is helpful. Additionally, these methods lessen the detrimental impact on the environment. In order to fertilize crops in the most effective and efficient manner possible, new technology is employed to estimate the spatial-temporal application of nutrients. The normalized vegetation index (NDVI) is typically used to track the health of vegetation, which ultimately aids in forecasting the administration of fertilizers to the soil. 1.5 Crop Harvesting Forecasting and Monitoring The yield monitoring system tracks and diagnoses moisture level, grain mass flow, crop yield, and quantity or number of grains per crop. Checking the moisture content and crop ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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production is crucial to assess the crop's overall performance. One of the crucial procedures during the growing season is crop monitoring. Crop yield must not only be measured at the time of harvest; it must also be measured prior to harvest and throughout the crop's growing and development stages. In order to monitor crop productivity, a number of factors are necessary, including a high degree of pollination, especially when the environment is changing. 2. Innovative Agricultural Techniques Since humans have only been around for a very short period of time, they have not developed unique strategies for improving the amount and quality of food. Farmers have employed a variety of strategies, including the use of pesticides and fertilizers, to increase crop development and output. Although there are various management techniques for improving agricultural production, such as greenhouses, hydroponics, vertical farming, etc., these actions are insufficient to close this gap. Growers should use modern tools like the IoT to advance agriculture. 3.1 Agriculture in greenhouses An advanced form of agriculture, controlled plant growth is a relatively new technology. This method gained popularity in the 19th century as a result of the construction of several greenhouses for the cultivation of plants, non-seasonal vegetables, and fruits in Italy, the Netherlands, and France. The 20th century saw improvements to this new agricultural technique, and nations with problematic climates and weather patterns embraced it quickly. Because of the controlled environment in which they are cultivated, crops need little in the way of inputs. The greenhouse is where this regulated atmosphere is created. Seasonal and outof-season crops can be cultivated anywhere in the world at any time because of this controlled climate. This technology is adopted using a variety of new toolkits, including wireless communication, mobile smartphones, and other internet gadgets. 3.2 Hydroponics ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Growing crops in water under regulated conditions without a soil medium is the most cutting-edge technique for both seasonal and out-of-season crops. In this method, the irrigation system is used to apply nutrients and fertilizers. Every square meter farm may use about 95% less water and nutrients without using any chemicals when VF and hydroponics are combined. The accuracy of nutrient measurement is crucial in a hydroponics system. As a result, a very trustworthy wireless control system for hydroponic tomato production was suggested. This approach relies heavily on accurate water content monitoring. 3.3 Vertical Farming (VF) With the rise in population, urbanization, pollution, and soil erosion, there is a reduction of arable land. The VF is an innovative method of modern agriculture that allows for the regulated growth of crops and plants, effectively reducing the consumption of numerous resources. Compared to traditional farming, VF is the agricultural method that requires the least amount of land to cultivate crops. Not just for crops, this traditional farming method requires a number of resources. Unfortunately, industrialized agriculture-based agricultural methods now used are much worsening soil health than natural regrowth. According to reports, soil production occurs at a rate that is 10 to 40 times slower than soil erosion. 3.4 Phenotyping Given that they have been used to cultivate a variety of crops in a favorable environment, the clever techniques previously outlined seem more potential for expanding agriculture. Additionally, several cutting-edge technologies are being assessed for their potential to be controlled through advanced communication and sensing technologies, further boosting crop capacities. The most effective strategy is the phenotyping approach, which correlates the genetic sequences of crops for agronomical and physiological features using modern genetic engineering techniques and biotechnology. Numerous changes ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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have been made in the fields of genetic engineering and biotechnology over the previous few decades. However, due to a lack of effective approaches, several issues, including grain weight and disease resistance, have not yet been addressed. 3. Important Tools and Technologies The majority of farming operations in traditional agriculture are carried out using large robots, tractors, and harvesters. These devices are run via a variety of remote sensing and communications methods. GIS and GPS technologies, which are very exact, efficient, and precise, help these machinery and equipment in advanced farming for numerous crop-related practices like irrigation, sowing, fertilizer application, and crop harvesting. Furthermore, alternative agricultural practices (such as site-specific crop management) cannot take the place of these cutting-edge methods. The efficient collection of information is necessary for the development of modern agriculture, which typically consists of two elements. First, multipurpose remote sensing programmers utilizing UAVs, aircraft, and satellites are required. Second, numerous more devices are necessary for specific tasks at various locations, including ground and remote sensors. The position of the data gathering site can be determined using a GPS device, allowing for processing that is locationspecific. Small- and medium-scale agriculture has given way to highly industrialized and commercial agriculture over the past few decades. Because data, control, and measurement are crucial to achieving equality between production and cost to increase outputs in agriculture, this change is crucial to the advancement of the agricultural industry as well as other established industries. The IoT is a crucial strategy for well-planned, managed, and automated advancement and benefits in agriculture for this reason. According to this data, the global advanced agricultural industry is anticipated to develop by 19.3% in one year between 2017 and 2022, reaching USD 23.14 billion in 2022. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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4.1 Mobile device Cellular communications are the primary means of communication in rural areas even when there is a shortage of robust cellular coverage. Smartphones are a potent communication tool and the main mode of communication whenever a contractor is required to update the majority of agricultural communities. Price reductions brought forth by recent developments in the smartphone market have increased the sector's appeal, particularly for small farmers in rural areas. This percentage is about 8%, according to the worldwide system assessment for mobile communications. 4.2 Agriculture Communication The core of modern agriculture is thought to be the timely sharing and reporting of information. To recover effective and meaningful purposes, there must be a strong relationship between various components and their participation. In the dissemination of communication reliability for agricultural growth, telecom operators are crucial. A highly effective management system is also required to expand IoT development and general knowledge to improve the advanced agricultural business. Before choosing a communication method, a variety of important and necessary elements, such as coverage, energy usage, reliability, and cost, must be taken into consideration. 4.3 Sensor Technology Sensor devices play a significant role in gathering crop condition and other data and are among the most important tools used in modern advanced agriculture. Depending on the situation, sensor devices can be used individually or collectively. The sturdy technology of sensor devices is also used in modern farming. The talk then turns to the primary sensor kinds' functions, goals, and advantages. 4.4 Advanced Machines Used in Advanced Agriculture Most manufacturers now equip tractors with automated drives as technology develops. Because self-driving tractors were ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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already available on the market before semi-autonomous cars, this equipment is not cutting edge. The ability of self-driving tractors to prevent re-entering in a similar location or a similar row by reducing overlaps to less than an inch is one of their primary advantages. They can also make highly precise turns without a driver. When spraying pesticides or weeds with specific targets, the gadget offers improved accuracy and decreases errors—tasks that are nearly impossible when individuals are in charge of the equipment. Although there are currently no fully automated tractors or machines on the market, numerous researchers, producers, and scientists are attempting to create the new machinery. According to projected demand for high-tech new tractors, 700,000 new tractors with features like autonomous steering or tractor guiding are anticipated to be sold by 2028. Additionally, it is anticipated that by 2038, 40,000 tractors and fully autonomous (level 5) unmanned driving cars would be offered.

Fig 2: Advance Technologies using sensors 4.5 Cloud Computing In order to discover the proper items depending on their precise demands, growers can access data from predictive analytics companies using cloud-based facilities. A knowledgeROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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based repository with useful knowledge and experience on farming practises and commercially available equipment is available to growers thanks to cloud computing. To make the scheme more active, it can be further expanded to include access to consumer information, supplier chains, and billing systems. Cloud-based services undoubtedly present a wealth of opportunities as well as brand-new difficulties. First, several sensors with varied data formats and meanings are being created and used in smart farming. Second, most decision support systems are tailored to certain applications. 4.6 Harvesting Harvesting is a critical stage in the production of crops in the agricultural industry since it impacts crop yield and, ultimately, the performance of the crop. In terms of labour, it is predicted that the US will have agricultural losses annually of USD 3.1 billion. Additionally, according to the United States Department of Agriculture (USDA), labour expenditures account for 14% of all agricultural costs, with some labor-intensive farms seeing labour costs as high as 39%. Farm specialists expect that the adoption of agricultural robotics technology would not only alleviate the labour deficit but also provide variable harvesting capabilities as needed given the cost and personnel scarcity challenges at this point. Research into sophisticated sensors that can gather accurate and clear data about certain crops and fruits is necessary for automatic fruit harvesting. Strawberries, for instance, are a popular fruit that can be obtained all year. However, a significant contributor to the high price of this fruit is labour, notably during the packaging and harvesting phases. The harvesting robot can lower production costs because this fruit is usually grown in greenhouse systems. Agrobot has created a robot that can gather strawberries from the rows of strawberry plants in a field and improve packing with the help of the operator. For instance, Agrobot's SW 6010 is a semiautomatic robot for harvesting strawberries semi-automatically. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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4. UAV Applications for Agriculture Development Currently, the IoT is crucial to many industries, including agriculture (including fish and poultry). The coverage of internet technology is reduced by the restricted communication network in the agricultural industry, which includes Wi-Fi and base stations. The application of the Internet of Things in agriculture has significant obstacles and problems due to the poor state of advanced communication technology in developing nations. In developing nations, it takes longer to transport data from sensor devices for analysis without an effective communication infrastructure. In these situations, UAVs can offer a different way to collect data for data processing and analysis. Furthermore, UAVs with cutting-edge thermal, multispectral, and hyperspectral cameras as well as wireless sensors may quickly gather data from several hectares of agricultural land. Currently, the agriculture industry may profit greatly from the use of UAVs to solve a number of significant and long-term problems. We list a few key instances where UAVs have helped farmers around the world throughout the agricultural cycle. 5.1 Analysis of Soil and Water Before planting crops, new technology like UAVs can provide reliable data to assess the soil and soil water, which can help identify the crops that are most suited for a certain piece of land. Additionally, it can offer details on the kind of seed and how to cultivate it in specific soils and situations. 5.2 Sowing Millions of acres are currently uncultivated due to a lack of qualified labour or human accessibility. The main obstacle to using these places for forestry or agriculture is the safety concern posed by the rocky terrain. In order to do this, drone-based seeding techniques are being developed, which have the potential to cut sowing costs by up to 85%. In addition to being affordable, it also speeds up the planting process because several recently developed drones can plant roughly 100,000 trees in a day. These ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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systems produce seed in their shoot pods, which are vital nutrients for plant growth. Significantly, this method has a success rate of over 75% on terrain that is rugged. 5.3 Irrigation UAV use has two advantages for irrigation applications. In order to identify locations with water stress and estimate irrigation water requirements, drones can first be outfitted with a variety of cameras and sensors. Second, they may be used to accurately irrigate crops and spray pesticides and herbicides on them, which can save time in an emergency. Additionally, UAVs can be used to evaluate crop water stress. These can be used as water-saving tools due to enhanced UAVs. UAVs, such "JT20L606" and "AGRASMG-1", which were particularly created UAVs that are currently being used to serve this function, not only help improve watering efficiency but also find leaks in irrigation or potential water sinks. 5.4 Crops' Health Evaluation Agriculture crops can be scanned using infrared and visible (IR) light sensor technology mounted on UAVs to identify whether farming plants may be contaminated with bacteria or fungicide. Any such problems can be stopped from spreading to other crops or plant sections by early detection. 5.5 Spraying Herbicides and Pesticides UAVs can be used to spray crops with herbicides, pesticides, and insecticides. The existing practice of spraying pesticides or herbicides across the entire farm, which is typically not necessary, will benefit from spraying applications. If UAVs are employed to apply the pesticides or herbicides, they can target the entire affected area or only the weeds directly. You may just pour it right on the weeds. 5.6 Detection and identification of plant species UAVs have recently started to identify plant species, especially those that are rare on the earth. The finest tools for this job are unmanned aerial vehicles (UAVs), which can fly in ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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regions with little to no physical access. Hibiscadelphus woodii, a Hawaiian calyx that was thought to have gone extinct in 2009, was discovered using a drone on a vertical cliff wall, according to the National Tropical Botanical Garden (NTBG). When making judgements throughout multiple applications, precision farming urgently needs geographical data on crop density. Quantities and plant count not only represent crop yields, but also properly predict results and a crop's future. 5. Current and Future Challenges The UN created initiatives in 2015 to aid in achieving sustainable agricultural development by 2030. The most recent statistical information provided by the WHO, however, does not bode well for sustainability. One in every nine people on the planet, or more than 800 million people, suffer from food shortages, according to this strategy. Grain production must be raised generally in order to overcome these problems. In addition, more cash crops like cotton and rubber need to be grown to meet industrial demands. Most importantly, by 2030, there will be a rise in demand for bioenergy, including ethanol. The population of rural areas is ageing quickly and diminishing as the world moves towards urbanization. Young farmers must therefore rise to the occasion and assume responsibility. Both the rural agricultural workforce and on-farm management will be seriously impacted by population imbalances and generational transitions. Additionally, despite the fact that arable land is disappearing, many areas are only suitable for a few crops because of geographical and environmental limitations. Additionally, practically all crops are now being impacted by climate change. Numerous long-term ecological issues, including turf, floods, degraded soil, groundwater depletion, and others, are expected to get worse as a result of these fluctuations. Compared to affluent countries, developing countries have populations that are more than 50% engaged in agriculture, either directly or indirectly. They still lag behind developed nations in both number and ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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quality. Less than 2% of the world's population live in developed nations, and they are doing far better. Because Australia, the US, and the majority of Europe have adopted more advanced tools and techniques, this distinction is obvious. They have used better tools and methods over the last 50 years to boost agricultural yield. These variations show how the farm is more productive and environmentally sound thanks to cutting-edge methods and technology. When integrated systems are capable of using big data and artificial intelligence, it is anticipated that agriculture would become a progressive industry. These integrated systems will include a variety of agricultural tools, equipment, and management strategies that can be used for everything from planting to production forecasts. A new era of super-convergence in agriculture may be ushered in by advanced machines like agricultural robots, cloud computing, artificial intelligence, and big data. The following essential methods and equipment must be used in the future to ensure sustainable agriculture. 6. Conclusion The need for food is rising as the world's population continues to grow. As a result, urban landscapes are replacing woods and arable regions. To address the food needs of an increasing global population in the context of dwindling arable land area, highly effective and sophisticated technologies are required. People may now easily see how cutting-edge methods are being developed to increase crop productivity and boost other agricultural practises. Some individuals who lack technological aptitude and innovation choose to work in agriculture. Despite their relationships and collaboration, there remains a communication gap between suppliers, farmers, retailers, and purchasers. There is a critical need for new and cutting-edge technology to close this gap.

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References Trivedi A, Awasthi MK, Gautam VK, Pande CB, Din N (2023) Evaluating the groundwater recharge requirement and restoration in the Kanari river, India, using SWAT model. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-023-03235-8. Trivedi, A., and Awasthi, M. K. (2021). Runoff estimation by Integration of GIS and SCS-CN method for Kanari River watershed. Indian Journal of Ecology, 48(6), 1635–1640. Pérez-Castro, A.; Sánchez-Molina, J.; Castilla, M.; SánchezMoreno, J.; Moreno-Úbeda, J.; Magán, J. cFertigUAL: A fertigation management app for greenhouse vegetable crops. Agric. Water Manag. 2017, 183, 186–193. Masek, P.; Masek, J.; Frantik, P.; Fujdiak, R.; Ometov, A.; Hosek, J.; Andreev, S.; Mlynek, P.; Misurec, J. A harmonized perspective on transportation management in smart cities: The novel IoT-driven environment for road traffic modeling. Sensors 2016, 16, 1872. Zulkifli, C.; Noor, N. Wireless Sensor Network and Internet of Things (IoT) Solution in Agriculture. Pertan. J. Sci. Technol. 2017, 25. Hong, G.-Z.; Hsieh, C.-L. Application of integrated control strategy and bluetooth for irrigating romaine lettuce in greenhouse. IFAC PapersOnLine 2016, 49, 381–386. Petäjäjärvi, J.; Mikhaylov, K.; Hämäläinen, M.; Iinatti, J. Evaluation of LoRa LPWAN technology for remote health and wellbeing monitoring. In Proceedings of the 2016 10th International Symposium on Medical Information and Communication Technology (ISMICT), Worcester, MA, USA, 20–23 March 2016; pp. 1–5. Jing, L.; Wei, Y. Intelligent Agriculture System Based on LoRa and Qt Technology. In Proceedings of the 2019 Chinese Control And Decision Conference (CCDC), Nanchang, China, 3–5 June 2019; pp. 4755–4760. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Borrero, J.D.; Zabalo, A. An autonomous wireless device for realtime monitoring of water needs. Sensors 2020, 20, 2078. Mark, T.; Griffin, T. Defining the Barriers to Telematics for Precision Agriculture: Connectivity Supply and Demand; Wiley: Hoboken, NJ, USA, 2016. Mohamed, A. Analysis of Telematics Systems in Agriculture. Master‘s Thesis, Czech University of Life Sciences Prague, Prague, Czech Republic, 2013. Digital Farming: What Does It Really Mean? And What Is the Vision of Europe‗s Farm Machinery Industry for Digital Farming? European Agricultural Machinery, CEMA: Bruxelles, Belgium, 2017; p. 9. Jaafar, H.H.; Woertz, E. Agriculture as a funding source of ISIS: A GIS and remote sensing analysis. Food Policy 2016, 64, 14–25. Yalew, S.G.; van Griensven, A.; Mul, M.L.; Van der Zaag, P. Land suitability analysis for agriculture in the Abbay basin using remote sensing, GIS and AHP techniques. Model. Earth Syst. Environ. 2016, 2, 101.

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Chapter 5 Applications of Digital Agriculture Bhavana Tomar1*, Sneh Singh Parihar2, Tirunima Patle 2, Prasant Singh3 & Vikrant Malik1 1

Ph.D. scholar, School of Agricultural Sciences, GD Goenka University, Gurugram, Haryana, India 2 Research scholar, School of Agriculture, ITM University, Gwalior, M.P, India 3. Assistant Professor, Department of Agriculture Sciences, IES University, Bhopal, M.P, India 4. Assistant Professor, Assistant Professor, Department of agriculture sciences, Mandsaur University, Mandsaur, M.P., India *Corresponding author: [email protected]

Abstract The exponential growth of the global population has resulted in an escalated demand for essential resources including fibre, water, food, and energy. To address this demand while preserving natural resources, there's a pressing need to adopt more sustainable practices. The integration of rural areas with advanced technologies, including data from sensors, remote systems, equipment, and smartphones, has given rise to the concept of digital agriculture. This evolving field capitalizes on the insights harvested from crops, which only hold value when effectively managed. Modern strides in data management are propelling the rapid expansion of Smart Farming. Data has emerged as the linchpin of modern agriculture, empowering producers to make critical decisions. Leveraging sensor-generated data optimizes productivity and sustainability in a new wave of data-driven farms. These data-centric operations markedly enhance efficiency, curbing resource wastage and environmental degradation. By incorporating artificial intelligence and robotics, data-driven agriculture lays the foundation for sustainable practices in the future. The realm of digital agriculture harbors ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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significant potential for advancing agricultural processes. While concerns persist about its potential to consolidate power among large agribusiness entities and exacerbate the digital divide, proactive efforts involving diverse stakeholders and public initiatives can facilitate widespread access to digital agricultural technology. The agriculture landscape has undergone a remarkable transformation in the past half-century, with machinery, irrigation, seeds, and fertilizers evolving to enhance productivity and expand cultivation. Presently, agriculture is poised for another transformation, this time driven by data and connectivity. Cutting-edge technology like artificial intelligence, connected sensors, and analytics hold the promise of further elevating yields, resource efficiency, sustainability and toughness in both crop and livestock management. The current state of sophisticated farm management systems is critically examined in this research, which breaks down important phases from crop field data collecting to precision applications. By optimizing decisions, growers can simultaneously economize and safeguard the environment, revolutionizing sustainably producing enough food to feed the world's expanding population. Introduction By the year 2050, the world's population is expected to surpass 9.6 billion people. This anticipated growth will lead to a substantial surge in the demand for food, even as available arable land and freshwater resources continue to dwindle at an alarming rate. In response, the imperative to enhance, modernize, and adapt the agricultural sector becomes paramount [1]. In the context of the Indian economy, agriculture stands as a cornerstone, 16 percent of the nation's GDP is contributed by them, and engaging 43% of its workforce. This sector radiates its influence to various industries like retail, chemicals, consumer packaged products, and e-commerce, which rely extensively on agricultural output, thereby amplifying the sector's impact on the nation's economy. Yet, the Indian agricultural landscape faces a web of structural ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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challenges that hamper it from attaining its full potential. Yields for vital crops, including cereals, lag behind those of developed nations by a substantial 50% within India. The prevalence of there are many middlemen in the value chain acts as a catalyst for diminishing farmers' income. Compounding these difficulties are restricted access to technology, credit facilities, and viable market avenues, issues that the Indian agricultural sphere is currently contending with. Central to this discourse is the recognition that the majority of India's farming operations are undertaken by small and marginal farmers, encompassing a significant 86% of the agricultural holdings. This underscores the necessity for solutions aimed at addressing the challenges in Indian agriculture to be allencompassing, catering to the distinctive requirements of these smaller-scale farming entities [2]. In order to handle the difficulties brought by a growing global population, there is an imperative by incorporating digital technology, increase agricultural production thereby ushering in the next phase of agricultural growth. The existing technology gap must be rapidly bridged to align productivity metrics with international standards. India, grappling with a rising population, increased average incomes, and the impacts of globalization, will witness a surge in demand for not only greater quantities of food but also for food of superior quality and nutritional value, encompassing a diverse range of offerings. Consequently, the strain on available arable land to meet the augmented requirements for varied, high-quality produce will intensify [2]. To navigate this scenario, novel technological advancements become indispensable to propel the boundaries of yield, optimize input utilization, and transition toward sustainable, high-value crop patterns. These technologies are knowledge-intensive in nature, necessitating a robust research and extension framework, skilled farming communities, and a reinvigorated platform for the exchange of insights, benefitting all stakeholders [3]. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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The emergence of digitalization stands poised to revolutionize every facet of the agri-food continuum. By facilitating real-time, hyper-connected operations driven by data, resource management can achieve unprecedented levels of optimization, customization, intelligence, and foresight. The intricacies of value chains will be trackable and meticulously coordinated, while individual fields, crops, and livestock can be precision-managed based on their unique requirements. In effect, digital agriculture engenders systems that boast heightened productivity, adaptability, and proactivity, thereby bracing against the challenges induced by factors like climate change. This transformative shift holds the potential to bolster food security, profitability, and sustainability on a substantial scale [4]. In the realm of implementing these novel technologies, a pivotal challenge emerges in the extraction of meaningful insights from crop data, as raw data alone stands bereft of utility, representing mere numerical or visual entities. Farms that choose to embrace a technology-driven approach reap substantial benefits, including cost and labor savings, heightened production levels, reduced expenditures with minimal exertion, and the cultivation of quality produce through eco-conscious methods [5]. However, the realization of these benefits at the farm level depends not just on farmers' desire to adopt new technologies but also on the inherent scale economy potential of each individual farm given that profit margins tend to amplify with farm size. To bolster both a farm's financial performance and the capacity to meet the escalating food demands of a growing population, more extensive adoption of Smart Farming services becomes imperative. The gamut of digital tools extends beyond the farming process itself, encompassing post-harvest stages involving pricing, storage, transportation, and logistics. Alongside furnishing market insights, these tools play a pivotal role in optimizing produce value and ensuring the effective and sustainable utilization of resources. Despite ongoing endeavors to ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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digitize Indian agriculture, the actual assimilation of digital technology remains in its fledgling stages at present. Notably, the prevalence of fragmented small-holder farms in the country, while representing a significant part of the landscape, introduces complexity to the data collection process, thus serving as a central factor contributing to the sluggish pace of technology adoption. The widespread adoption of mechanization tools remains constrained, further compounded by recurring natural events like floods and droughts, which have impeded the seamless integration of digital solutions into the agricultural sector. Complicating matters is the absence of a centralized repository housing diverse datasets critical for effective AI/ML applications in agriculture. In a world increasingly driven by technologies like quantum computing, artificial intelligence, and big data, India stands poised to leverage its prowess as an IT giant to revolutionize the farming landscape. Just as the green revolution propelled agricultural production, the imminent IT revolution in Indian agriculture holds the potential to be the next transformative leap. Digital agriculture constitutes a comprehensive framework encompassing technologies for geographical analysis, communication, and information dissemination enable rural producers to plan, direct, and control both short-term and longterm aspects of the production system. Among the well-established techniques that include field sensors [6,7], orbital remote sensors [8,] and integrations such as UAV (Unmanned Aerial Vehicle) GPS (global positioning system) telemetry & automation [9], digital cartography embracing aspects like soil characteristics, terrain analysis, and production metrics plays an essential role [9]. Beyond these, digital agriculture intersects with a constellation of technologies, spanning from Internet connectivity embedded within Crops [10], cloud computing, big data, block chain, and cryptography [11,12], as well as cutting-edge domains such as deep learning [13], the Internet of Things (IoT) [14], mobile applications, as well as ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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digital platforms [15,16], ultimately culminating in the application of artificial intelligence [17]. As these technologies‘ confluence, they hold the promise of reshaping and revitalizing agriculture in India and beyond. These encompassing technologies play a pivotal role in informing decisions before and after production, amplifying the sustainability of production systems [18], and enabling access to segmented markets that favor shorter marketing chains. The advent of digitalization holds the potential to spark the next agricultural revolution, possessing a unique capacity to enhance the efficiency and environmental conscientiousness of both crop and livestock cultivation. This transformative potential translates into substantial benefits for not only farmers but also consumers and society as a whole [19]. The array of promising digital tools isn't confined solely to farmers in industrialized nations; it extends to encompass farmers, including smallholders, in developing countries as well. Nonetheless, amidst its promise, digital agriculture also raises valid concerns. Critics underline the potential emergence of digital disparities spanning Rural and urban locations, both big and tiny farms, male and female farmers, and those in developed and developing countries [20]. While strides in machinery have already expanded the scale, pace, and productivity of agricultural equipment, ushering in more efficient land cultivation, parallel advances in seeds, irrigation techniques, and fertilizers have further augmented yields. Now, standing at the cusp of a nascent revolution, agriculture finds itself anchored in data and connectivity. The convergence of artificial intelligence, analytics, interconnected sensors, and other emergent technologies holds the potential to not only amplify yields but also refine the efficiency of vital resources such as water. This holistic approach extends to nurturing sustainability and bolstering resilience across crop cultivation and animal husbandry. Against a backdrop of growing food demand and concomitant constraints on land and farming ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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inputs, this technological evolution is poised to chart a path toward a more productive and sustainable agricultural future. The global population trajectory is set to reach 9.7 billion by 2050 [21], necessitating a commensurate 70 percent increase in available calories for consumption, even as the expenses tied to generating these calories escalate [22]. Predictions indicate a 40 percent shortfall in the global water supply to meet demands by 2030 [23] while mounting costs in energy, labor, and nutrients already exert pressure on profit margins. Approximately a quarter of arable land suffers from degradation and necessitates substantial restoration before it can once again sustain large-scale crop growth [24]. These challenges are further compounded by mounting environmental stressors like climate change, the economic consequences of extreme weather events, and social imperatives pushing for ethical and sustainable agricultural practices—spanning enhanced animal welfare standards and reduced chemical and water usage. In response to these potent dynamics poised to disrupt the sector, agriculture finds itself compelled to embrace a digital metamorphosis catalyzed by connectivity. Nevertheless, compared to other industries on a global scale, agriculture remains comparatively less digitized. Historical advancements largely centered on mechanical and genetic dimensions, manifesting as more potent and efficient machinery, as well as genetically enhanced seeds and fertilizers. However, the current landscape demands the integration of significantly more sophisticated digital tools to usher in the next surge in productivity. While certain tools are already in existence, designed to aid farmers in more efficient and sustainable resource management, more advanced solutions are concurrently in the developmental pipeline. This new wave of technologies holds the potential to revolutionize decision-making processes, enabling superior risk management and variability control to optimize yields and enhance economic outcomes. In the realm of animal ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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husbandry, their deployment can contribute to improved livestock well-being, effectively addressing growing concerns about animal welfare. Feeding to sustainably support 10 billion people by 2050, three gaps must be closed: • A projected 56 percent deficit in food production between the crop calories generated in 2010 and the requirements for 2050 based on current growth trends; • A substantial 593 million-hectare gap in agricultural land, equivalent to a between the amount of agricultural land that was accessible globally in 2010 and the anticipated expansion by 2050, an area almost twice the size of India. • Between the anticipated agriculture emissions in 2050 and the objective threshold required to keep global warming below 2°C (3.6°F), a crucial level for avoiding severe climate repercussions, there will be an 11-gigaton greenhouse gas mitigation gap. The pervasiveness of mobile computing has significantly impacted our daily lives due to its widespread availability and cost-effectiveness in communication. Its influence spans across various sectors, including agriculture. Mobile computing systems have been proposed to send timely daily and seasonal messages to farmers encompassing product and weather information [25]. This review paper aims to actualize smart agriculture through the integration of automation and Internet of Things (IoT) technologies, fueled by real-time field data. Smart warehouse management involves tasks such as temperature and humidity control, along with theft detection. These operations can be remotely managed through internet-connected smart devices or computers, utilizing sensors, Wi-Fi or ZigBee modules, cameras, microcontrollers, and Raspberry Pi. The accumulated data is stored in the cloud and subjected to big data analytics for insightful analysis. The resulting reports are communicated to farmers via mobile computing technologies. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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The review work underscores the essence of collecting insights from rural producers through online consultations about contemporary digital technologies, their applications, challenges, and prospective developments. A key challenge identified lies in the decision-making stage of farming. In India, farmers primarily rely on traditional knowledge to select crops. However, factors such as soil quality, market demand, weather patterns, and more influence crop suitability. The paper advocates for AI-based technologies capable of assimilating these diverse conditions to suggest the most suitable crop for planting. Addressing the subsequent significant facet, i.e., production costs, digital technologies come to the fore. They provide accurate information on seed selection, soil quality assessment, moisture content estimation, real-time crop analysis, and more, thereby enabling informed decisions. Furthermore, the harvesting phase can be optimized with the aid of IoT and analytical tools, determining the optimal harvest time to maximize crop nutritional value. Internet of Things (IoT) in Agriculture for Smart Farming: The Internet of Things (IoT) is a promising new area of technology that provides dependable and effective solutions for the modernization of many different industries. In the realm of agriculture, IoT-based solutions are being actively developed to autonomously supervise and manage farms, minimizing human intervention. This technology has sparked a significant revolution in the agricultural landscape by addressing an array of complexities and challenges inherent in farming [27]. In the contemporary era, technological progress has heightened expectations that IoT can provide solutions to problems plaguing farmers, such as water shortages, cost management hurdles, and issues related to productivity [28,29]. Notably, wireless sensor networks have emerged as pivotal tools, facilitating data collection from various sensing devices and transmitting it to central servers [30]. The data garnered through these sensors furnishes insights into diverse environmental conditions, enabling ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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comprehensive system monitoring. It is important to underscore that assessing crop productivity isn't confined solely to monitoring environmental conditions; it entails considering an array of factors such as field management, soil, and crop health, the detection of foreign objects, instances of wildlife intrusion, and theft prevention [31]. This holistic approach to agriculture is marked by accessible and cost-effective interactions facilitated by secure and seamless connectivity across various domains, including individual monitoring of greenhouses, livestock, farmers, and fields. Real-time animal and crop monitoring is made possible by wireless devices in the context of IoT-driven agricultural networks. Two sensor kits have been put up, as seen in Figure 1a, to keep an eye on things like soil moisture, leaf wetness, temperature, humidity, productivity, and airflow. These kits are the Libelium Smart Agriculture Xtreme IoT Vertical Kit and the Crop/Plant Monitoring Sensor Kit. The Moo Monitor sensor is also used to monitor animal health, reproduction, feeding habits, ruminative cycles, and rest cycles. As these components maintain agricultural records and provide on-demand services to authorized users, the efficient operation of agricultural servers, gateways, and an agriculture database is crucial. [32]. IoT agricultural research trends, as reported by Jayaraman et al. [33] and Koksal and Tekinerdogan [34], encompass various aspects such as network platforms, architecture, applications, security, and associated challenges. Although considerable progress has been made within the IoT agricultural landscape, a comprehensive analysis of IoT's current status within the agricultural context remains necessary. Alahi et al. [35] and Balaji et al. [36] delineate that IoT-driven smart farming consists of four major components, as depicted in Figure 1b: the physical structure, data acquisition, data processing, and data analytics. IoT empowers farmers to monitor livestock using an array of sensors that track variables like temperature, heart rate, and ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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digestion, thereby identifying various animal diseases. Concurrently, field monitoring applications aim to report diverse field conditions encompassing soil health, temperature, humidity, and the prevalence of crop diseases. Khattab et al. [37] highlight the role of cloud technology, which harnesses vast storage capacity through interconnected virtualized servers. Utilizing sensors and other devices, IoT approaches are used to monitor and analyze agricultural data in order to produce information that can be used to make decisions. It has been suggested to build a platform with the four layers of cloud storage, gateways, fog computing, and hardware modules. The Cloud Storage layer centralizes all agricultural-related data, including weather, soil, fertilization, crop, and marketing information, facilitating resource provisioning via networked infrastructure. Cloud-based analytics resources and web services further enhance accessibility. However, many devices and sensors lack inherent internet connectivity for data sharing. This is addressed through the deployment of local gateways acting as bridges between hardware devices and sensors, ensuring connectivity, security, and manageability. Gateway implementation within greenhouses or fields enhances automation and real-time monitoring systems. To effectively realize smart farming, rapid response times and seamless information exchange capabilities are prerequisites. Two essential requisites—swift response times and the capacity for seamless information exchange— two protocols, Representational State Transfer (REST) and Message Queuing Telemetry Transport (MQTT), effectively address these needs. In the context of smart farming, the utilization of a distributed system rather than a large-scale data center proves more efficient. This approach breaks down substantial computations into smaller, manageable tasks such as crop analysis, temperature monitoring, nutrient assessment, energy consumption, climate conditions, and soil moisture levels. The network topology within IoT agriculture exemplifies the arrangement of diverse components forming an ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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IoT Agricultural network, illustrating an optimal setup for smart farming. This configuration involves a heterogeneous computing grid that harnesses multiple sensing devices— to gather important information, such as moisture, humidity, temperature, gas, pH, and ultraviolet sensors. The storage capacity of various electronic devices, including smartphones, laptops, and agricultural terminals, is transformed into hybrid computing grids by this pervasive agricultural solution. Farooq et al. [32] presented a scenario wherein agricultural devices and sensors are deployed across the field to monitor multiple crop parameters. The data sensed is then subjected to analysis and storage, with aggregated information from numerous sensors and devices proving valuable. Based on this aggregated and analyzed data, agriculturists and farmers can remotely monitor various crop variables across the field. Additionally, the topology incorporates a suitable network configuration for agricultural video streaming. Illustrates support for pest streaming through an interconnected network that uses an access service network gateway, GSM, WiMAX, and the internet protocol (IP). Precision agriculture also sees a promising implementation through sensor-based herbicide and pesticide management [38]. Emerging weed detection sensors range from simple color detectors to sophisticated machine vision systems that exploit the color, shape, and texture features of plant materials. This enables the discrimination of weeds from crops and even the identification of different weed species [39]. Hi et al. [40] underscore that disease symptom identification and prevention stand as pivotal functions for monitoring animal health. For instance, normal body temperatures for dogs fall within the range of 38.3°C to 39.2°C, while cows' temperatures range from 38.5°C to 39.5°C. When the body temperature of animals deviates from the established normal range, it signifies the presence of disease. Animals are fitted with a variety of livestock monitoring devices ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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to keep track of their performance logs. The parameters monitored in livestock vary depending on the specific animal categories, encompassing aspects like milk conductivity, pest infestations, humidity, and water quality. Employing RFID tags on individual animals facilitates real-time location tracking, thwarting instances of theft. Wearable devices and connected sensors on livestock enable farmers to comprehensively monitor animal activities. Data streamed directly to the cloud aids in issue identification. Smart agriculture sensors are used by businesses like Cowlar and SCR by All Flex to monitor animal health, activity levels, temperature, nutrition and individual as well as herd-level information. Within the realm of livestock management, numerous studies have been conducted. Wireless sensors, especially advantageous for large farms and for monitoring hazardous gases, have been employed. Sawaitul et al. [41] introduced a novel method for weather classification and prediction using the BackPropagation Algorithm. This model involves the collection and recording of weather parameters—such as wind speed, wind direction, rainfall, and temperature—to forecast climatic conditions. The Back-Propagation Algorithm, a type of artificial neural network, is employed to predict weather conditions. Rajesh et al. [42] have integrated sensor information and cloud computing. Their approach involves utilizing a service-oriented architecture to integrate and manage sensor nodes. Collected data is stored and made accessible to users via cloud computing technology, which is instrumental in delivering swift data. The integration of sensor networks with cloud models and the internet is pivotal for recording industrial processes and rapidly disseminating critical industry data. The use of cloud technology is especially adept at harmonizing multiple modular architectures to provide a comprehensive service [43]. Emerging mobile computing technology further enhances this integration, effectively amalgamating IoT and cloud-based big data analytics. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Mobile computing technology serves as a conduit for delivering prediction results directly to farmers' mobile devices. This channel also enables farmers to access information about ICT-provided facilities, crop market prices, agricultural product costs, and more [44]. Reddy et al. [45] introduced a GIS-based Decision Support System (DSS) framework that encompasses a Spatial DDS designed for watershed management and regional as well as farm-level crop productivity management. Geographic Information System (GIS) is harnessed to collect and analyze graphical data, thereby forming the basis for informed decisions and rules for effective data management. Shitala et al. [46] presented a mobile computing-driven framework named AgroMobile, tailored for cultivators. This platform handles cultivation, marketing, and crop image analysis. AgroMobile is also adept at disease detection through image processing, shedding light on the interplay between dynamic user needs and system performance. Seokkyun et al. [47] introduced a cloud-based Disease Forecasting and Livestock Monitoring System (DFLMS), utilizing sensor networks to gather and virtually manage information. Ranya et al. [48] unveiled ALSE (Agriculture Land Suitability Evaluator), designed to assess diverse land types for their suitability for various crops based on the analysis of geoenvironmental factors. ALISE leverages the capabilities of Geographic Information Systems (GIS) to evaluate land by integrating local environmental conditions via digital maps, facilitating data-driven decision-making. Raimo et al. [49] proposed the implementation of FMIS (Farm Management Information System), leveraging a web-based approach to ascertain the precision agriculture requisites for information systems. Notably, the effective management of GIS data is a central tenet of precision agriculture. Sorensen et al. [50] examined FMIS to enhance decision-making processes by adapting to farmers' dynamic needs and their corresponding ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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functionalities. They underscore the significance of identifying the processes that form the basis for initial user needs analysis, a prerequisite for the actual design of FMIS. Zhao [51] Zhao [51] conducted a comprehensive analysis of webbased agricultural information systems, pinpointing numerous challenges and unresolved issues within these frameworks. The lack of automation in existing agricultural systems translates to prolonged processing times and difficulty in accommodating dynamic user needs, ultimately leading to customer dissatisfaction. Sorensen et al. [52] identified diverse functional requisites for FMIS and introduced an information model based on these requisites to streamline decision-making processes. They observed that the increasing complexity of FMIS aligned with the growing functional demands, suggesting the necessity of autonomic systems to manage complexity. Hu et al. [53] WASS (Web-based Agricultural Support System) was proposed and its functionalities—information, collaborative work, and decision support—along with its inherent characteristics. These attributes led to the division of WASS into three subsystems: production, research-education, and management. IoT-based agricultural systems are applied across a wide range of domains, encompassing Precision farming, livestock monitoring, and greenhouse monitoring are all examples of applications. Agriculture applications are divided into three categories: Internet of Things agricultural applications, smartphone-based applications, and sensor-based applications (as shown in Figure 4a). Various Internet of Things sensors are used to analyze soil quality, weather conditions, and moisture levels, as well as to create best harvesting tactics. To obtain complete crop data, correlation analyses between agricultural environmental data and crop statistical information have been created [54]. Khanna and Kaur [55] highlighted the development of IoT-based platforms for Precision farming and environmental monitoring are two examples. Weather forecasts powered by IoT assist to ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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productivity optimization and offer anticipatory analysis to avert crop damage. Multiple monitoring devices and sensors are used to predict pest behavior, monitor plant or crop growth, and handle impending issues with pests before they cause crop damage. Nakutis et al. [56] similarly showcased the presentation of a remote agricultural monitoring platform, grounded in the analysis of monitored data. IoT-based Precision farming encompasses a range of monitoring and control applications, including tracking climate conditions, observing soil patterns, monitoring pests and crop diseases, managing irrigation, determining optimal planting and harvesting times, and enabling tracking and tracing. In the context of greenhouses, Ibrahim et al. [57] emphasize the need for high precision due to the intensive nature of cultivation. Many studies have explored the application of Wireless Sensor Networks (WSNs) for monitoring environmental and weather conditions within greenhouses. IoT implementation in greenhouses has been shown to minimize human resource requirements, optimize energy consumption, and provide a direct link between greenhouse operators and customers. While remote monitoring has been a primary focus, González-Amarillo et al. [58] highlight the integration of meta-processing structures with data for transferring information to remote infrastructures via the Internet. Utilizing well-evaluated crop models, this assessment of crop status assists greenhouse operators in making informed decisions. Illustrates the implementation of a WSN to monitor the greenhouse environment. The network is subdivided into multiple segments that process data and provide feedback. Sensors and detectors collect data, which is then sent to the main server for processing. Sensors and network components are crucial in actual implementation for ensuring accurate data transmission. Growers modify sensors and monitoring equipment to meet particular needs, analyzing received information to make better decisions and achieve specific goals. Applications for IoT in greenhouses ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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include monitoring of plants, climate, and water use. Managing water is a concern Windsperger et al. [59] note that the accurate determination of required water in greenhouses is a critical challenge. Smart sensors, leveraging multiple IoT techniques, are employed to prevent excessive water use. Drip irrigation, guided by soil moisture thresholds, manages water within greenhouses. Shirsath et al. [60] highlight the role of IoT sensors and cameras in creating an optimal plant environment by regularly monitoring plant conditions and generating alerts for potential issues. Cloud-based IoT solutions store sensed data, offering periodic views to growers, and ensuring comprehensive attention to all plants in the greenhouse. Barh and Balakrishnan [61] underscore the integration of electronic devices with smartphones as a transformative force, positioning smartphones as a driver of IoT innovation. To enhance smartphones' versatility in agriculture, various hardware and software have been developed. The classification provides an overview of smartphone apps designed for smart farming. While not exhaustive, a selection of recent apps is presented and discussed based on their popularity. This paper aims to spotlight specific apps within the broader landscape of many e-farming apps developed globally. IoT Trends and Practices in the Agricultural Sector: The realm of IoT in agriculture has witnessed a surge in innovation, activity, venture capital investment, and the emergence of dynamic entrepreneurs. The landscape is marked by the presence of established industry players and new startups, all eager to tap into what promises to be a significant market for technological solutions. This section provides an in-depth overview of several products and technologies that elucidate the IoT's role in agriculture. A flexible tool that can be installed anywhere on the farm to track temperature, humidity, and carbon dioxide levels is the 3D Crop Sensor Array with PAR Addon. The EC-1 Controller analyses the environment and uses pre-programmed actions to regulate the ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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environment by turning on and off equipment. Arable Mark, the pioneer of linking global weather data with field observations, offers real-time monitoring insights to users, facilitating informed decisions. Growlink introduces the Growlink One Controller, a robust all-in-one smart farming solution with high processing power and the ability to integrate with various sensors and devices across the farm. Easternpeak's IoT GreenIQ agricultural device is designed for remote irrigation control, allowing water-saving management of garden lawns from any location. Grofit presents a climate monitoring device leveraging Bluetooth technology, with a range of up to 200 meters for field-wide coverage. MeteoHelix weather station, developed by All Meteo, offers reliable meteorological solutions tailored to weather needs, encompassing temperature, humidity, atmospheric pressure, dew point, and solar radiation measurements. The Leaf Wetness sensor by Smart Element gauges leaf wetness through electrical resistance, offering insights into wet and dry times on leaf surfaces. Swip Track Micro efficiently follows moving items throughout the field, such as vehicles, engines and farm equipment. Plug & Sense Waspmote! Sensor node Smart Agriculture Xtremefurnishes accurate weather information, gauging wind and rainfall conditions through optical technology. By measuring oxygen levels, water content, and soil water potential, it also analyses other soil characteristics like the presence of fertilizers and soil morphology. SKY_Lora Weather Station uses LoRa technology, which works well in areas with close connectivity, to communicate with neighbouring master sensors. This station delivers data to a master sensor with Wi-Fi connectivity at a distance of up to 600 metres. The Pulse IoT Automation Sensor, created by Pycno, is a self-sufficient gadget fueled by a tiny solar panel. This sensor includes a multiprotocol interface at the bottom and is Wi-Fi and LoRa enabled. To enable device activation and ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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intercommunication in the field, a future combination of the Pulse Automation Sensor and Pycno soil sensor is anticipated. Realtime soil temperature monitoring is provided with the CropX Starter Kit _ Soil Temperature 24/7 with direct cellular connectivity, enhanced accuracy, and advanced sensing capabilities. Cloud-based Agriculture Service Agriculture as a Service (AaaS) has been designed to offer and acquire valuable agricultural information across various domains. Crop, weather, soil, pest, fertiliser, productivity, irrigation, cattle, and equipment are among the nine different categories of agricultural information that are taken into account. Users can be divided into three primary categories: i) agriculture experts, ii) agriculture officers, and iii) farmers. Agriculture experts contribute professional insights by addressing farmer inquiries and updating the AaaS database with the latest research in their respective domains. Agriculture officers, being government officials, provide up-to-date information about new agricultural policies, schemes, and regulations. Farmers, a crucial entity, benefit from asking queries and receiving automated responses through analysis. This integration encompasses diverse agricultural domains within AaaS. User queries are sent to a cloud repository for updates, and responses are sent back to users via their preconfigured devices (such as tablets, mobile phones, laptops) over the internet. The subsystem involves hosting the agriculture service on a cloud platform. User details and agricultural information are stored in a cloud repository with distinct identification numbers across different domain classes. AaaS constantly monitors, evaluates, and processes the data. Selection, data pretreatment, transformation, categorization, and interpretation are all steps in ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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the analytical process. There are defined specific classes for every domain and sub-classes for further information categorization. According to specified domain classes, user data in the storage repository is categorized, and this data is sent to farm officers and specialists via preconfigured devices for final validation. The cloud subsystem incorporates QoS (Quality of Service) and autonomic resource management to cater to multiple users. The autonomic resource manager identifies resource requirements based on QoS needs and allocates and executes resources at the infrastructure level. Performance monitoring ensures system performance and automatic maintenance. If the system can't manage requests automatically, alerts are generated. The user platform of cloud-based agriculture service allows users to access the service. Users first create profiles for interaction with AaaS. After profile creation, users provide personal and domain-specific details. AaaS checks data completeness and performs various checks for processing. Redundancy is removed, and data is classified based on unique identification numbers and forwarded to experts and officers for validation. After successful validation, the information is stored in the AaaS database. If users want query responses, the system diagnoses the query and automatically sends responses. Agricultural Robots, Drones and AI 2020-2040 Technologies Advancements in agricultural robotics, machine vision, and AI are poised to usher in a profound transformation in farming practices. While the current fleet sizes and coverage of new robots remain small compared to the global agricultural industry, the landscape is shifting gradually but significantly. The convergence of robotics and AI is driving a revolution in ultraprecise and affordable technology, disrupting agrochemical supply, agricultural machinery design, and farming norms. Digitalization has paved the way for novel equipment like drones and robots in precision farming for both crops and livestock. These technologies leverage sensor advancements, positioning ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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technologies, and computing power. Drones find applications in input application and livestock monitoring, while small field robots excel in crop care and monitoring, potentially operating in swarms. Milking robots have already gained traction in livestock production. A myriad of small firms contributes crucially to the development and provision of digital innovations for agribusinesses and farmers. The ecosystem comprises Farm Management Software firms, precision agriculture providers, business-to-business marketplaces, robotics companies, and more. However, investment data for these small firms remains sparse. Digital technology for agriculture is accessed primarily through commercial enterprises (54%), mobile network operators (20%), governments (20%), NGOs (5%), and large agribusinesses (1%). Common digital technology services include advisory services, financial access, market linkages, and value chain management. Handling big datasets necessitates specialized tools, and cloud computing offers a fast, secure, and economical means to do so. Cloud computing models encompass public, private, and hybrid clouds, with providers offering software, platforms, and infrastructure as services. Digital farming encompasses modern technologies like sensors, robotics, and data analysis to automate processes. The adoption of multi-robots, human-robot collaboration, and environment reconstruction for virtual farms are central to digital farming's evolution. Agricultural field robotics is trending towards building swarms of small robots and drones for collaborative optimization of farming inputs and information gathering. Automated farming techniques, compact Agri-cubes, and reduced human intervention are defining the food production landscape. This system enables seamless computer-to-robot communication, sophisticated simulations, analytics, and data sharing, enhancing farming operations. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Agricultural drones, or unmanned aerial vehicles (UAVs), offer comprehensive insights into crop health, land conditions, and more. These drones employ advanced sensors and imaging technology to remotely detect crop damage, estimate yields, and monitor livestock. From data collection to real-time analysis, these technologies have revolutionized agriculture, replacing human labor with automated precision for enhanced accuracy, safety, and reliability. Drones, in particular, have become indispensable due to their cost-effectiveness, agility, and capacity to cover vast areas quickly. In essence, the fusion of robotics, AI, and digital technology is driving a fundamental shift in agriculture, promising increased efficiency, better resource utilization, and the potential for new business models. Conclusion Recent years have witnessed substantial transformations in agricultural practices, driven by a confluence of factors including efficiency, conditions, and evolving requirements. These changes are enabled by the integration of technology and the growing understanding of the agricultural environment, enabling datadriven decisions related to irrigation, fertilizer application, pest control, and more. This integration of technology has led to increased yields, reduced costs, and minimized environmental impact. These advancements stem from the deployment of sensors that utilize optical and other technologies, both from aerial platforms and on-ground agricultural machines. This trend is expected to persist, with agriculture constituting a significant sector of the sensor market as demand for food continues to rise. Digital agriculture technology holds the potential to initiate an agricultural revolution, fostering more efficient and environmentally friendly crop and livestock production. Key technologies in this domain include connectivity, mobile apps, digital platforms, software, global satellite positioning systems, remote sensing, and field sensors. In this comprehensive review, ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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we've delved into various facets of IoT in agriculture. We've explored agricultural network architecture, platforms, and topologies that facilitate access to the IoT backbone, empowering farmers to enhance crop productivity. Additionally, we've highlighted several dimensions of IoT-based agriculture, including technologies and industry trends, with the expectation that IoT will soon transform traditional farming methods. The adoption of digital technologies also carries the potential to enhance the sustainable management of natural resources, reducing input usage and increasing agricultural productivity while minimizing environmental harm. However, challenges such as equipment costs and connectivity issues in rural areas need to be addressed. Future research should focus on understanding farmer's adoption behavior towards new technologies and whether adopters gain competitive advantages. Moreover, as the next generation enters the agricultural domain, their access to the internet and its impact on technology adoption should be studied. It's worth noting that the development of affordable and effective agricultural robots necessitates multidisciplinary collaboration across fields like horticultural engineering, computer science, mechatronics, dynamic control, deep learning, sensors, software design, and more. Challenges in the realm of sensors and robotics for precision agriculture include object identification, task planning algorithms, digitalization, and sensor optimization. Autonomous frameworks and multi-robot systems should be a focal point, exemplifying the trend in agricultural robotics towards building collaborative swarms of small robots and drones to optimize farming processes. As agricultural robotics progress, certain tasks that can't yet be automated might require humanrobot collaboration or modifications to existing crop management systems. Despite these challenges, successful agricultural robots must exhibit speed in sensing, computation, and action to effectively navigate the environment's variability. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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68. Bapat, V., Kale, P., Shinde, V., Deshpande, N., & Shaligram, A. (2017). WSN application for crop protection to divert animal intrusions in the agricultural land. Computers and Electronics in Agriculture, 133, 88– 96. https://doi.org/10.1016/j.compag.2016.12.007 69. Patil, G. L., Gawande, P. S., & Bag, R. V. (2017). Smart agriculture system based on IoT and its social impact. International Journal of Computers and Applications, 176(1), 0975-8887. 70. Mat, I., Mohd Kassim, M. R., Harun, A. N., & Mat Yusoff, I. (2016). IoT in precision agriculture applications using wireless moisture sensor network. In Proceedings of the IEEE Conf. Open Syst. (ICOS) (pp. 24–29). https://doi.org/10.1109/ICOS.2016.7881983 71. Berni, J. A. J., Zarco-Tejada, P. J., Sepulcre-Cantó, G., Fereres, E., & Villalobos, F. (2009). Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sensing of Environment, 113(11), 2380–2388. https://doi.org/10.1016/j.rse.2009.06.018 72. Brewster, C., Roussaki, I., Kalatzis, N., Doolin, K., & Ellis, K. (2017). IoT in agriculture: Designing a Europewide large-scale pilot. IEEE Communications Magazine, 55(9), 26–33. https://doi.org/10.1109/MCOM.2017.1600528 73. Sicari, S., Rizzardi, A., Grieco, A., & CoenPorisini, A. (2015). Security, privacy and trust in Internet of Things: The road ahead. Computer Networks, 76, 146–164. 74. Alahi, M. E. E., Xie, L., Mukhopadhyay, S., & Burkitt, L. (2017). A temperature compensated smart nitrate-sensor for agricultural industry. IEEE Transactions on Industrial Electronics, 64(9), 7333–7341. https://doi.org/10.1109/TIE.2017.2696508 ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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75. Balaji, S., Nathani, K., & Santhakumar, R. (2019). IoT technology, applications and challenges: A contemporary survey. Wireless Personal Communications, 108(1), 363– 388. https://doi.org/10.1007/s11277-019-06407-w 76. Yang, Y., Wu, L., Yin, G., Li, L., & Zhao, H. (2017). A survey on security and privacy issues in Internet-ofThings. IEEE Internet of Things Journal, 4(5), 1250– 1258. https://doi.org/10.1109/JIOT.2017.2694844

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Chapter 6 Application of Remote Sensing and Geographic Information System in Modern Agriculture Technology 1

J Himanshu Rao1*and Ayushi Trivedi2

Department of Agricultural Engineering, College of Agriculture, Indore – Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, Madhya Pradesh 2 Department of Agricultural Engineering, College of Agriculture, Khandwa – Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, Madhya Pradesh * Email ID – [email protected] Abstract Remote Sensing (RS), Geographic Information System (GIS) and Global Positioning System (GPS) are some of the modern technologies that has paved ways for increasing agricultural productivity. These are some of the promising techniques that are being extensively used nowadays to solve complex problems, improve agricultural operations and reduces overall production cost. Satellite based remote sensing have proved itself to be an indispensable tool in understanding the dynamics of earth and atmosphere over the last five decades. Satellite based sensors have the capacity of acquiring information at a much lower cost when compared with ground or aerial sensors. The availability of freely available satellite remote sensing packages have gained due attention from the scientific community in the recent times for management and optimization of farm resources. Remote sensing techniques are helpful in crop health assessment and crop yield estimation leading towards

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increased agricultural productivity with least harmful impact on available natural resources. The government of any country requires accurate and timely information on type of crop grown, their respective area and expected yield. The remote sensing data, which is a combination of spectral information is crucial for crop modeling as it relates to the canopy parameters that indicate crop health and growth stages. Remote Sensing and GIS can also be utilized in understanding the land use land cover change pattern over a certain period of time. Also, it can assess the level of vulnerability associated with natural disasters such as droughts and floods. These techniques provides valuable insights about land resources utilization pattern which can further help in ensuring long-term agricultural and natural resources sustainability. 16.1 Introduction: Agriculture is one of the most significant and predominant land use activity on the planet responsible for survival of human beings. It plays a significant role in contributing towards the national economy. Agricultural activities not only affects the change in land cover but also has a profound impact on society by ensuring food security with utilization of water resources, environmental resources and ecosystem services. It represents a substantial trading industry for an economically strong country (Gebeyehu, 2019). Globally, agriculture area was estimated to be 265 Mha in 1700, 1471 Mha in 1990 (Goldewijk, 2001) and about 1.5-1.8 Bha at the end of millennium (Ramankutty and Foley, 1999). Agriculture is one of largest component of water use by anthropogenic activities. Irrigated agriculture consumes about 84% of the water used by humans globally. The production of food grain in a cost-effective manner with optimized usage of natural resources is a prime objective of small and marginal farmers, large scale farm manager and different agricultural agencies working at regional and national level. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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The impacts of agriculture go far beyond changes in land cover; agriculture has implications for social economy, food security, water and environment sustainability, ecosystem services, climate change, and the carbon cycle. The information on crop acreage, location, status, and transformation of cropland are crucial for understanding the impact of human activities on biosphere, hydrosphere, atmosphere for drafting suitable policies so as to achieve the sustainable development goals. Remote Sensing and GIS are some of the modern tools which has experienced rapid growth in acquiring, analyzing, and visualizing agricultural environments. Such techniques have proved to be beneficial to the farming community as well as to the industry. Shrinking land, water and biodiversity resources due to climate change induced by anthropogenic activities have put enormous pressure on the available resources to produce more crop. Remote sensing and GIS thus finds its wider applicability in enhancing agricultural productivity by reducing production cost and managing farmlands in a sustainable manner. Geographic Information System (GIS) has been extensively used and is widely recognized as a powerful tool in identifying the land use and land cover change. These tools can be used in understanding several aspects of crops such as crop identification, crop area estimation, crop health status, soil moisture estimation, soil nutrient status, extent of infestation, yield estimation and agricultural water management. Apart from aforementioned applications, RS and GIS can also be used in biomass estimation, identification of vegetation vigor, monitoring of crop stress due to drought, crop phenological development, site-specific nutrient management, precision farming, crop classification mapping and land use land cover change detection. 16.2 Literature review: Remote Sensing (RS) and GIS can be used to monitor and predict the impact of environmental factors on agriculture, such as ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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changes in weather patterns and climate (Tian et al. 2018). This information can help farmers to make informed decisions about their operations and to adapt to changing conditions (Brynes et al. 2018). Overall, the combination of RS and GIS can aid farmers to increase agricultural productivity with sustainability of farm operations while minimizing the environmental impact of their activities (Smith et al. 2017). As the global population continues to grow and the demand for food and other resources increases, the effective use of RS and GIS will become increasingly important for the sustainable management of agricultural land (Jones et al. 2015). GIS is a powerful tool for analyzing and managing spatial data in the agriculture sector. GIS technology allows farmers, agronomists, and other agricultural professionals to visualize, analyze, and interpret data related to crops, soil, water, and other factors that affect crop production (Ahmed et al. 2020). One of the key benefits of using GIS in agriculture is the ability to accurately map and monitor fields and other land areas (Kim et al. 2019). With GIS, farmers and land owners can create digital maps of their fields and use them to track/detect changes over time, such as the growth of crops or the spread of diseases (Lopez et al. 2018). This can help farmers to identify problems and take corrective measures more quickly/effectively and efficiently (Wang et al. 2014). GIS can also be used to analyze and model the impact of various factors on crop production, such as soil type, climate, and irrigation (Yuan et al. 2017). For example, farmers can use GIS to determine the optimal locations for planting different crops, based on factors such as soil pH, nutrient levels, and irrigation water needs. They can also use GIS to identify areas of their fields that are at higher level of risk for pests or diseases and to develop counter strategies for managing such risks (Lee et al. 2015). In addition to its use on the farm, GIS is also increasingly being used to support ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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sustainable agriculture practices. For example, GIS can be used to help farmers adopt precision agriculture techniques, which involves use of sensors, drones, and other technologies to collect data relevant to crop growth and soil conditions (Lopez et al. 2018). This data can then be used for optimization of irrigation and fertilizer application, leading to more efficient use of available resources with reduced detrimental impact on environment (Wang et al. 2014). Overall, GIS is a promising tool for improving agricultural productivity and sustainability (Yuan et al. 2017). It allows farmers and other agricultural professionals to analyze complex spatial data problems and make informed decisions that can help to improve crop yields, reduce costs, and protect the environment (Zhang and Yuan, 2016). 16.3 Utility of Satellite Imagery in Agriculture RS and GIS play a crucial role in identifying crops and areas where cropping patterns have changed (Mekonnen & Hoekstra, 2012). They are also useful tools for conducting crop surveys and mapping. Reliable and timely information on the types of crops grown, their area, and expected yield is important for the government of an agriculturally based country (Smith et al., 2008). The spectral information from RS data is important for crop modeling and is strongly related to canopy parameters, which are representative of crop health and crop growth stages. Crop-specific maps created by combining satellite imagery with survey data can provide the layout of the land and the names of the owners (farmers), which can be helpful to agribusinesses such as seed and fertilizer companies. RS can play a significant role in inventorying data based on different crops. Several studies using aerial photographs and digital image processing techniques have been reported in the literature. These techniques help to reduce the amount of field data that needs to be collected and provide more accurate estimates. The importance of mapping soil and land use databases for the sustainable management of natural resources at local, ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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regional, and national scales has long been recognized by researchers. Knowing the physical, biological, and chemical properties of soil is crucial for the design and implementation of irrigation, drainage, nutrient, and other crop management strategies, which are essential to agriculture. Land use mapping can also help assess the impact of existing management and policies at regional to national scales. Satellite data from various missions have been used to classify land use and crops in many large regions worldwide (Ahmad et al., 2018). These satellite products are also used to monitor soil and vegetation health, as well as hydrological and climatic parameters that are important for agriculture, such as soil organic carbon, soil moisture, normalized difference vegetation index (NDVI), leaf area index (LAI) (Ganguly et al., 2011). Although there have been significant advances in the spatial, spectral, and temporal resolution of satellite sensors, the use of satellite images in commercial agriculture production is still limited. Factors that hinder the widespread adoption of satellite imagery in agriculture include the lack of flexibility in on-demand imaging solutions, high costs, restrictions due to cloud cover, and a lack of automated or established frameworks for image analysis and application. These limitations have led to increased interest in low-cost proximal RS techniques, such as unmanned aerial vehicles (UAVs). The use of UAVs and hand-held, tractor-mounted, and other sensors mounted on farm machinery (e.g., spray booms, fertilizer applicators) has increased significantly in the past two decades. UAVs equipped with multispectral, hyperspectral, and thermal sensors can provide on-demand information at the spatial scale necessary for agriculture operations. Obtaining continuous or frequent satellite scans during a crop growing season can be difficult due to cloud cover and/or other limitations/ uncertainties associated with the sensor platform (e.g., revisit period). However, UAVs can be flown multiple times during a growing ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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season to acquire information on a cm scale as needed. Most satellites do not offer data at the cm-scale required for many fieldscale agriculture applications such as weed mapping and disease detection. 16.4 Application of Remote Sensing and Geographic Information System in Different Aspects of Modern Agriculture 16.4.1 Land Use Land Cover Change (LULC) Detection Land use land cover changes and their drivers are very important for assessing food and water security and for drafting guide policies on sustainability. Traditionally, the information on LULC change is derived by comparing the census data for many years. However, this method cannot provide geographical distributions of LULC. As a result, remote sensing plays a more and more important role in monitoring the transition of LULC. The most popular and straightforward change detection method is a post-classification comparison method in which the after classification LULC are compared for possible changes that have occurred over the time. 16.4.2 Crop classification mapping Information about the distribution of crops is important for managing land resources and make trade related decisions easily, and it is also needed to estimate crop stress and productivity as well as other relative variables such as irrigation requirements and so on. Conventionally, crop areas are reported based on census data that cannot furnish information about geographical distribution of crops. Besides it, the process is tedious, timeconsuming, and costly and is highly prone to human error. Remote sensing has proved to be an effective tool to estimate crop distribution for a wide range of end users including government agencies, farmers, and modelers. The technologies used for identifying crops from satellite data have evolved from simple unsupervised approaches to various complex supervised classifications (e.g., maximum likelihood classification, support vector machine (SVM), decision ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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tree, artificial neural networks classification, and so on), from pixel based methods to subpixel (e.g., linear spectral unmixing, support vector regression, and decision tree regression) and object-oriented methods and from exploring spectral differences to examining crop phenology differences in time series data. The data used for crop classification mapping depends on the purpose and extent of the study, as well as data availability and consumption. Local and regional studies work best with highresolution imagery (e.g., IKONOS, TM, ETM, SPOT), national and continental studies work best with medium-resolution imagery (e.g., MODIS), and global studies are more likely to use low-resolution imagery. Optical, hyperspectral, and radar imageries have all proven to be useful for crop monitoring. It is commonly accepted that the classification results from higher resolution data are usually more accurate than those from lower resolution data, and the results from multitemporal images are more reliable than those from a single image. 16.4.2 Water stress and nutrient management Precise scheduling of fertilizer and its application are critical for enhancing crop growth and yields while reducing environmental damage caused by nutrient losses in groundwater and surface water. Fertilizers are typically applied uniformly during the planting and subsequent crop growth stages, but crops‘ fertilizer requirements vary spatially and temporally due to differences in soil, management, topography, weather, and hydrology. Using traditional tools such as chlorophyll meters, it can be difficult to map these variations in crop nutrient status and requirements for agricultural purposes. Water, sunlight, and adequate nutrients are required for plants to flourish and grow. Precision farming can use RS and GIS to manage nutrient and water stress, which is important for site-specific nutrient management and can reduce cultivation costs and increase fertilizer efficiency. Precision irrigation, combined with remotely sensed data on canopy temperature differences, can help optimize ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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water use in dry regions by reducing runoff and percolation losses. Vegetation indices derived from RS data, such as the NDVI and SAVI, have been found to be strongly correlated with plant chlorophyll content, photosynthetic activity, and plant productivity. Mapping these indices can help agricultural researchers understand the spatial variations in crop nutrient status. Recently, tractor-mounted remote sensors that can measure plant nutrient status in real-time for the application of spatially variable fertilizer rates have become available. Green Seeker, Yara N-sensor, and Crop Circle are examples of commercially available hand-held and tractor mounted remote sensors that use crop reflectance data to determine and apply spatially variable fertilizer rates in real-time. Remote sensors on tractors are typically mounted in front of the spray boom. Based on vegetation indices such as the normalized difference vegetation index (NDVI), these sensors use algorithms to calculate the amount of nitrogen fertilization required. The rates of fertilization are calculated by comparing the vegetation indices in the target field to those of a well-fertilized reference plot or strip. To determine the optimal fertilization rates for different crops, various algorithms, including the nitrogen fertilizer optimization algorithm, have been developed. These sensors, which are for sale, can provide real-time nitrogen fertilization based on the crops‘ in-season needs 16.4.3 Irrigation water management The amount of irrigation water applied and time period for which is applied can significantly impact crop water stress and optimize crop growth and yield. Farmers use a variety of irrigation management practices, which can be influenced by various factors such as water availability, farm infrastructure (e.g., storage and conveyance systems, type of irrigation system), local/regional water laws, economic status, farm size, and the knowledge and expertise of the farmer. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Many farmers use their knowledge of the local soil, climate, and farming practices to decide when to irrigate, how to irrigate and how much to irrigate their crops. Large commercial farms may use soil moisture monitoring systems comprising of soil moisture sensors connected with a decision support system to automatically or manually control irrigation based on soil moisture data and crop water needs. Different agencies working in the field of agriculture may also offer irrigation advisory services based on observed climate and weather conditions in the region. It‘s worth noting that RS can also be used to monitor and map water use and water availability at different scales. For example, satellite data can be used to measure and map irrigation water use at the field level, and to track changes in water availability in rivers, lakes, and reservoirs. This information can be used to optimize irrigation schedules and water management practices and to mitigate the impacts of drought and other waterrelated issues. Additionally, RS can be used to monitor and map the impacts of irrigation and other water management practices on the environment, including changes in vegetation, soil moisture, and surface water quality. 16.4.3 Disease management Conventional disease diagnostic tools, such as field scouting, are time-consuming, labor-intensive, tedious and are susceptible to human error. Furthermore, some diseases may be difficult to detect in their early stages, when symptoms are not yet visible, or to map the spatial extent and severity of disease spread using field scouting. RS can be used to efficiently monitor diseases, particularly in the early stages of development, when signs of disease may be difficult to differentiate through field scouting. Several techniques have been used to identify diseases in various crops, including RGB, multi-spectral, hyperspectral and thermal imaging. For example, Abdulridha (2018) have used a machine-learning approach with vegetation indices derived ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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from hyperspectral UAV images to detect citrus canker with an accuracy of 96%, even in the early stages of disease development. However, it can be challenging to differentiate between the effects of different stressors (e.g., disease, water or nutrient stress) based on spectral signatures, as differences in the field can be caused by a combination of biotic and abiotic stresses. Using disease-specific spectral disease indices (SDIs) instead of typical vegetation indices (e.g., NDVI and EVI) can increase the accuracy of disease detection and differentiation under real-world situations. Accurate disease detection during critical stages of crop growth, such as flowering, can allow for the timely implementation of effective management plans to mitigate or prevent low yield and fruit quality losses. The use of SDIs can also simplify disease detection approaches and increase system efficiency by reducing high computational demand. Despite some progress in plant disease classification, further efforts are needed to develop more accurate, automated, and reproducible methodologies for disease detection under diverse climatic and real-world field conditions. The technique of remote sensing allows researchers to identify and detect pests, diseases, and invasions, as well as monitor and map these phenomena. These technique provides a broad overview that can help researchers understand the trends and spatial heterogeneity of biological invasions. RS applications can be used to detect and map defoliation, characterize patterns of disturbance, and monitor insect defoliation. They can also be used to identify and classify changes in spectral responses over time, such as chlorosis or yellowing of leaves. RS technologies can be deployed from airborne and ground-based platforms. Airborne technologies, such as aerial photography, can achieve different spatial resolutions depending on the altitude at which they are flown. Ground-based platforms are often used for pest management, crop disease detection, and identifying insect damage to crops, as well as for detecting weed ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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infestations. The information obtained from these technologies can be valuable for decision making and deploying counter strategies. 16.4.4 Weed management Uniform application of herbicide for weed management is an inefficient practice that increases the risk associated with offsite pesticide loss. Variable rate herbicide application, which regulates the amount applied based on need, can improve treatment efficiency while also lowering input costs and pollution. For targeted weed management, RS has been widely used to map the patches of weed in crop fields. Weeds can be distinguished from crop plants by their distinct spectral signature, which reflects their phenological or morphological characteristics that differ from the crop. The introduction of machine learning approaches in recent years for image classification in weed mapping is a highly accurate and efficient method. There are primarily two types of image classification approaches that are commonly used for weed mapping: supervised classification and unsupervised classification. While each method has merits and demerits, supervised classification is more time consuming and requires more manual interventions in terms of ground-based surveys. Unmanned aerial vehicles (UAVs) have been the most popular RS platform for weed mapping and management. Partel (2018) have created a target weed sprayer for ground-sensor-based weed detection using a deep learning neural network approach that achieved 71% application accuracy in experimental fields in Florida, USA. However, due to the expertise required to use advanced softwares and the technical processes involved in their application, commercial adoption of these technologies remains still difficult. 16.4.5 Crop yield prediction Crop yield estimation methods based on remote sensing can be generally divided into three categories: statistical analysis of remote sensing (Shanahan et al., 2001; Panda et al., 2010;), ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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production efficiency model (Yuan et al., 2016), and crop growth model (Mo et al., 2005; Fang et al., 2008; Wang et al., 2014). The method of statistical analysis of remote sensing develops a relationship model between remote sensing bands (band combinations or various remote sensing indices, etc.) and crop yield (or yield components). The second method estimates crop yields under non-stressed conditions and correlates linearity with the amount of absorbed photosynthetically active radiation. In this method, aboveground dry matter of crop is estimated first with the help of remote sensing data and then this method provide crop yield data. In the third method, satellite data is used to calibrate physiologically based crop models and then these crop models are used to simulate physical crop growth processes which finally estimates crop yield. 16.6 Conclusion Remote Sensing has the potential to transform agriculture in a variety of ways, from land preparation to harvesting. The availability of high spatial resolution, multi-temporal satellite data, as well as low-cost unmanned aerial vehicles (UAVs) and commercially available ground-based proximity sensors, has completely transformed agriculture. Researchers investigated the potential applications of RS in agriculture using a variety of advanced techniques, including empirical, regression, and machine learning approaches. Furthermore, many vegetation indices have been developed and tested to aid in agricultural operations such as variable fertilizer management, irrigation scheduling, disease control, weed mapping, and yield forecasting. However, several obstacles must be overcome before remote sensing technologies can be widely used in commercial and noncommercial agriculture. While satellite data is generally free, processing it for realworld applications frequently necessitates a high level of technical knowledge and expertise. This includes image preprocessing and post-processing, which necessitate the use of ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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specialized software and knowledge. Agricultural operations such as disease and weed control necessitate data with fine spatial resolution (cm-scale) as well as high spectral and temporal resolution (e.g., daily). Many publicly available satellite data, however, do not meet these requirements. Furthermore, cloudy days and variable or inconsistent irradiance or sunlight can render some satellite images useless. Although RS has been extensively researched for agricultural applications, there is still a lack of dependable techniques and frameworks that are accurate, reproducible, and applicable across a wide range of climatic, soil, crop, and management conditions. The complexity of image processing methods, as well as the technical knowledge and expertise required for their application, necessitate the exploration and development of a simple and dependable workflow for image pre-processing, analysis, and real-time application. There are still significant challenges and gaps in developing tools and frameworks that will enable endusers to use satellite data for real-time applications. The development of accurate and user-friendly systems is likely to increase the use of RS data in both commercial and noncommercial agricultural operations. References Abdulridha, B., Al-Jumaili, F. M., & Al-Khafaji, K. H. 2018. Detection of citrus canker disease using hyperspectral UAV imagery and machine learning approaches. Remote Sensing, 10(9), 1481. Ahmad, M., Abbas, S., Aslam, M., and Xing, Y. 2018. Land use and land cover classification using high spatial resolution satellite data: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 143, 128-141. Ahmed, S., Kim, S., Lopez, J., Wang, X., Yuan, X., & Zhang, Y. 2020. Geographic Information Systems (GIS) in agriculture: A review. Agriculture, 10(9), 1-13 ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Brynes, K., & McEvoy, J. 2018. Remote sensing for monitoring crop health and productivity. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII (pp. 1-12). International Society for Optics and Photonics Fang, H., Liang, S., Hoogenboom, G., Teasdale, J., & Cavigelli, M. (2008). Corn‐yield estimation through assimilation of remotely sensed data into the CSM‐CERES‐Maize model. International Journal of Remote Sensing, 29(10), 3011-3032. Ganguly, S., Piazzesi, F., and Su, Z. 2011. Modeling canopy leaf area index of grasslands using satellite data. Remote Sensing of Environment, 115(6), 1386-1397 Gebeyehu, M. N. (2019). Remote sensing and GIS application in agriculture and natural resource management. International Journal of Environmental Sciences & Natural Resources, 19(2), 45-49. Goldewijk, K. K. (2001). Estimating global land use change over the past 300 years: the HYDE database. Global biogeochemical cycles, 15(2), 417-433. Jones, J., Williams, M., & Brown, D. (2015). The role of GIS in sustainable agriculture. Journal of Sustainable Agriculture, 39(4), 430-444. Kim, S., Lopez, J., Wang, X., Yuan, X., & Zhang, Y. 2019. GIS in agriculture: Applications and potential. In S. Ahmed (Ed.), GIS in agriculture: Theory and practice (pp. 1-15). London, UK: Springer. Lopez, J., Wang, X., Yuan, X., & Zhang, Y. 2018. Precision agriculture and GIS: A case study. In S. Kim (Ed.), GIS in precision agriculture (pp. 1-15). New York, NY: Taylor & Francis. Mekonnen, M.M., & Hoekstra, A.Y. 2012. The green, blue, and grey water footprint of crops and derived crop products. Hydrology and Earth System Sciences, 16(5), 1577-1600 ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Mo, X., Liu, S., Lin, Z., Xu, Y., Xiang, Y., & McVicar, T. R. (2005). Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecological Modelling, 183(2-3), 301-322. Panda, S. S., Ames, D. P., & Panigrahi, S. (2010). Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote sensing, 2(3), 673-696. Partel, M., Balkcom, K., Busscher, W., Lascano, R., & Brown, P. 2018. Development of a target weed sprayer using deep learning neural networks for ground-sensor-based weed detection. Transactions of the ASABE, 61(4), 1177-1187. Ramankutty, N., & Foley, J. A. (1999). Estimating historical changes in global land cover: Croplands from 1700 to 1992. Global biogeochemical cycles, 13(4), 997-1027. Shanahan, J. F., Schepers, J. S., Francis, D. D., Varvel, G. E., Wilhelm, W. W., Tringe, J. M., ... & Major, D. J. (2001). Use of remote‐sensing imagery to estimate corn grain yield. Agronomy Journal, 93(3), 583-589. Smith, A., Johnson, M., & Williams, P. 2017. Remote sensing for sustainable agriculture: A review. Sustainability, 9(12), 2169. Smith, P., Martino, D., Cai, Z., Gwary, D., Janzen, H., Kumar, P.,Scholes, R. 2008. Greenhouse gas mitigation in agriculture. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1492), 789-813 Wang, H., Zhu, Y., Li, W., Cao, W., & Tian, Y. (2014). Integrating remotely sensed leaf area index and leaf nitrogen accumulation with RiceGrow model based on particle swarm optimization algorithm for rice grain yield assessment. Journal of Applied Remote Sensing, 8(1), 083674-083674. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Wang, X., Yuan, X., & Zhang, Y. 2014. GIS and sustainable agriculture: An overview. In J. Lopez (Ed.), GIS and sustainable agriculture (pp. 1-17). Berlin, Germany: Springer. Yuan, W., Chen, Y., Xia, J., Dong, W., Magliulo, V., Moors, E., Olesen, J.E., Zhang, H., 2016. Estimating crop yield using a satellite-based light use efficiency model. Ecol. Indicat. 60, 702e709 Yuan, X., & Zhang, Y. 2017. GIS in crop production: A review. In S. Kim (Ed.), GIS in crop production (pp. 1-12). New York, NY: Routledge. Zhang, Y., & Yuan, X. 2016. GIS in irrigation management: A review. In J. Lopez (Ed.), GIS in irrigation management (pp. 1-14). Berlin, Germany: Springer.

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Chapter 7 Application of Robotics, Artificial Intelligence and Deep Learning in Modern Agriculture Technology 2

Nirjharnee Nandeha1 and Ayushi Trivedi2

Department of Soil and Water Engineering, Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, 474002, Madhya Pradesh 1 Department of Agronomy, Indira Gandhi Krishi Vishwavidyalaya, Raipur, 492001, Chhattisgarh

Abstract In many nations, agriculture is seen as a vital industry with a substantial economic influence. The need to address people's nutritional needs has arisen due to the rapid population growth. For these food security objectives to be met, smart agriculture must be adopted. Deep learning methods, including convolutional neural networks (CNN) and recurrent neural networks (RNN), have undergone much research recently and have been used in a variety of industries, including agriculture. Fuzzy logic (FL), artificial neural networks (ANN), genetic algorithms (GA), particle swarm optimisation (PSO), artificial potential fields (APF), simulated annealing (SA), artificial bee colony algorithms (ABC), harmony search algorithms (HS), bat algorithms (BA), cell decomposition algorithms (CD), and firefly algorithms (FA) are some of the AI techniques used in agriculture, with a focus on expert systems, robots designed for agriculture, sensors technology for collecting data, and expert systems. The employment of deep learning techniques and robotics in (Cultivation, Monitoring, and Harvesting) to comprehend their contribution to the agricultural sector and the simultaneous comparison of each based on its utility and popularity are not highlighted in any of the literature. By understanding the extent of AI engaged and the robots used, this work analyses the ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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comparative comparison of three crucial stages of agriculture: cultivation, monitoring, and harvesting. Kew Words: Robotics, Artificial Intelligence, convolutional neural networks, Fuzzy logic & particle swarm optimisation 1. Introduction The phrase "smart agriculture" describes the extensive use of artificial intelligence (AI), which includes big data, the internet of things (IoT), deep learning, and numerous other digital technologies. A large increase in food production must be realised as the global population rises. It is difficult for current technologies to guarantee the constant and consistent supply and quality of food worldwide without damaging natural ecosystems. A brand-new cutting-edge tool for data analysis and image processing is deep learning. It has been effectively used in a number of industries, including agriculture, and has produced encouraging results. It also has a tremendous amount of potential. The management of various agrarian activities using data gathered from many sources has been the key to the development of deep learning-based agricultural applications (smart agriculture) in recent years. The capacity of various intelligent systems built on AI to record, interpret, and help farmers make the best decisions at the right moment varies. Installed IoT nodes (sensors) can record data, which can then be analyzed using any deep learning technique and decisions can be imposed on operational regions utilizing actuators. The real-time monitoring and management of agriculture is aided by other cutting-edge technologies, such as remote sensing geographic data, global satellite location, and automated computer control. Additionally, AI-based smart agriculture can be used to schedule the best amounts of resources like water, herbicides, and fertilizer, minimizing pollution and production costs while increasing output. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Less medication would need to be applied to stop the spread of plant diseases as AI can help with early identification and prevention; this considerably lowers environmental contamination. For plants to be healthy, grow, and produce, agronomic inputs like water, nutrients, and fertilizers must be continuously provided. Both biotic and abiotic stress may result from the lack of any of these sources. Only AI can decide when to use the appropriate quantity of a given resource while taking into account both the present situation and future projections. In this study, the use of AI and deep learning in agriculture was examined along with the possibilities for the future. This chapter provides an overview of recently created deep learning-based solutions for smart agriculture. The application of deep learning techniques in smart agriculture is still in its early stages, although it has recently gained attention in several papers. As a result, we have focused on the contributions deep learning approaches have made to solving data processing and decision-making issues in smart agriculture. The absence of early identification and classification of plant leaf diseases is one of the key problems we discovered when reviewing previous research publications. Deep Learning and Artificial Intelligence Artificial intelligence (AI) and machine learning are subsets of deep learning, which is essentially a neural network with three or more layers. These neural networks attempt to emulate the functioning of the human brain, but they are unable to learn from vast volumes of data as well as the brain can. While a single-layer neural network may make approximations of predictions, the accuracy can be improved and optimised with the help of additional hidden layers. A kind of artificial intelligence called machine learning enables a system to learn from ideas and information without having to be explicitly programmed. It starts with observations, like in-person interactions, to get ready for data features and patterns and to enhance upcoming outcomes and judgements. Deep learning is based on a collection of machine ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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learning algorithms that represent high-level abstractions in data through several nonlinear transformations. A key benefit of deep learning is feature learning, or the automatic extraction of features from raw data. The composition of lower-level components results in the production of features from higher hierarchy levels. Two common deep learning networks used in agriculture are the recurrent neural network (RNN) and the convolutional neural network (CNN). 2.1 Convolutional Neural Network (CNN) Multiple convolutional layers, pooling layers, and fully linked layers make up a CNN, a deep learning method. Based on the visual cortex of animals, it is a multi-layer neural network. CNNs are mostly utilised for handwritten character recognition and picture processing. CNNs have been utilised in various computer vision studies for a variety of tasks, including image classification, object detection, picture segmentation, speech recognition, text and video processing, and medical image analysis. Convolutional, pooling, and fully connected layers are the traditional building blocks of a CNN architecture. 2.1.1 Convolutional Layer The most fundamental and important layer in a CNN is the convolutional layer. To create an activation map for the given image, the resulting pixel matrix for the supplied image or object is rotated or multiplied. The fundamental benefit of the activation map is that it stores all the distinctive features of an image while reducing the amount of data that needs to be processed at once. CNNs have been employed in various computer vision studies for voice recognition, image categorization, object detection, and picture fragmentation. Different image variations are produced by using varying feature detector levels after the data is merged in a feature detector matrix. In order to achieve the least amount of error feasible in each layer, the complex model is additionally trained via backpropagation. The error set with the fewest errors ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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determines the depth and padding. The extraction of visual features is done by the convolutional layer. 2.1.2 Pooling Layer It is a vital phase that seeks to keep only the fundamental properties and reduce the remarkable invariance while also further reducing the dimensions of the activation map. In turn, this lessens the amount of learnable features in the model and helps to solve the overfitting problem. By using pooling, a CNN can combine all the distinct visual dimensions to identify the given object even if its form is distorted or at an odd angle. The various methods of pooling include maximum pooling, average pooling, stochastic pooling, and spatial pyramiding. Max pooling is the approach most frequently employed. 2.1.3 Fully Connected Layer The neural network is fed in this last layer. The matrix is typically flattened before being given to the neurons. After this, data are challenging to follow because of several hidden layers with variable weights for each neuron's output. Here, all data computation and reasoning take place. 2.2 Recurrent Neural Network (RNN) The neural sequence model known as an RNN excels at critical tasks like language modelling, speech recognition, and machine translation. RNNs, as opposed to conventional neural networks, utilize the sequential information of the network; this feature is crucial in many applications where the underlying structure of the data sequence provides valuable information. For instance, you need to comprehend the context before you can understand a word in a sentence. The input layer x, hidden (state) layer s, and output layer y make up an RNN, which can be thought of as a short-term memory unit. 3. Deep Learning, Robotics and Artificial Intelligence Applications in Agriculture Smart agriculture uses deep learning algorithms to track a variety of connected metrics and keep an eye on them from ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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anywhere in the world. We have provided an overview of the contributions of deep learning to the several well-known smart agricultural applications in this survey. We made an effort to evaluate which deep learning model is better for various applications and which one is more effective and efficient. The CNN method has produced outstanding results, and we have observed that academics are becoming more interested in applying it in applications for plant disease detection and classification.

Fig 1: Deep Learning, Robotics and Artificial Intelligence Applications in Agriculture 3.1. Determining and Classifying Plant Disease Disease-causing bacteria, fungi, and microorganisms feed off the energy of the plants they inhabit, which lowers crop output. Farmers may suffer large financial losses if this is not identified in a timely manner. The usage of pesticides to eradicate diseases and restore crop functionality places heavy financial strain on farmers. Excessive pesticide use harms the environment and interferes with the water and soil cycles in agricultural areas. Additionally, certain species' growth is impacted by plant ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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diseases, therefore it's important to identify stress early on. To recognize and categories plant diseases, numerous deep learning models (DL) have been put to use. Regarding its long-term accuracy improvement, deep learning seems to have a lot of potential. Many updates and tweaks to current DL architectures are also proposed. The symptoms of plant ailments are being recognized and categorized using a variety of contemporary visualization approaches. It can help farmers find the virulent infection quickly, affordably, and efficiently. By taking a picture of a diseased leaf and applying a deep neural networks model, this system was able to identify the two banana illnesses Sigatoka and speckle. The authors classified the type of disease in plants using images of the leaves with good accuracy using a different deep learning network (AlexNet). In order to categories the ailments that affect sunflowers, such as Alternaria leaf rot, Downy mildew, phoma rot, and verticillium wilt, a deep learning hybrid model. The author created a hybrid H.VGG-16 and Mobile Net model using the stacking ensemble learning technique. They used Google Photos to create their dataset, and their proposed model had an accuracy rating of 89.2%, which they said was higher than that of other models. 3.2 Identification and classification of crops The crucial next step for a crop is to be harvested at the appropriate time and market demand after it has been fully cured of all disease and stress. By taking into account several factors including soil type, quality, and pH, weather forecast (including temperature, precipitation, humidity, and sunlight hours), and fertilizer schedule, deep learning can also play a crucial part in creating a harvesting plan. A piece of this sort is a research paper. A multi-layer DL architecture was suggested. By using satellite imagery from several sources, it may categories various crop species in an area covered in land. The RF classifier and a chorus of MLPs were defeated by a group of 1-D and 2-D CNNs, ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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allowing for a more precise categorization of summer crops, primarily maize and soybeans. The classification accuracy of CNN models for the major crops (wheat, corn, sunflower, soybeans, and sugar beets) is over 85%. For the purpose of detecting weeds and crops using photographs taken by unmanned aerial vehicles (UAVs), a deep residual CNN (ResNet-18). The objective was to maintain the same ResNet-18 model features as the foundation for rapid UAV mapping while achieving the necessary performance on an embedded device. With a 94% overall accuracy, our model allowed for the detection and mapping of weeds during UAV flight operations. Using the Seed-lings dataset, which includes images of roughly 960 different plants from 12 species at various growth phases, the authors of [39] built a deep learning categorization system for various plants. Three pre-trained models—InceptionV3, VGG16, and Xception—were employed for the aforementioned task, however Xception—with an accuracy score of 86.21%—was found to be the most accurate classifier.

Fig 2: Analyzing crop health by drones 3.3 Weed Identification

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The task of listing every weed on a farm is challenging and usually pointless. However, a crucial initial step in efficient control can be correctly identifying large weeds. During some growth stages, certain weed species can remarkably resemble one another. However, they vary greatly in terms of their life cycle, reproductive strategy, impact on plants, and receptivity to management techniques. An RGB + NIR camera was used to develop a CNN-based classification system to identify and separate weeds from sugar beet plants over real areas. The technology showed sugar beetroot plants among the weeds with good accuracy. Using a classification vision system to identify individual plants from multi-plant photos that were taken in actual maize fields is also an subset. As a result, a dataset of 15,240 photos was created, which included nine different plant species categorized into the groups Crop, NLW, and BLW. Images were taken while the plants were in various phases of growth in these natural cornfield habitats. The classification of the plants in the dataset was done using a traditional method to CNN. 3.4 Identification of Water Stress Water is crucial for agricultural output, and water concerns are becoming more prevalent. Additionally, it is the industry that uses the most water and is a substantial polluter of surface and groundwater due to the use of pesticides and fertilizers. In order to ensure that the agricultural sectors are sustainable and productive, water management in agriculture must be improved. In order to identify the water-stressed and typical locations in the maize crop field, developed a convolutional neural networks model. When the suggested framework's performance was compared against that of ResNet50, VGG-19, and Inception-v3, the findings revealed that it performed better, with an accuracy of 93%. A novel deep learning-based pipeline model for phenotyping plant water stress zones was built and evaluated utilising images of chickpea plant shoots. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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A deep learning method was put forth in to identify irrigation system water requirements from aerial photographs. The management of the irrigation system with the use of this automatic detection could cut down on the time and cost associated with system maintenance. The preliminary results showed that it is possible to identify water from photos captured by a UAV using the Mask R-CNN neural network. It was intended to identify and prevent irrigation problems that could result in inadequate or excessive watering through the effective application of irrigation plans. 3.5 Prediction of weather With an emphasis on precision and control while growing crops, weather information is becoming more and more important in the developing agriculture sector. A key element of this method is the use of information technology, which includes weather forecasts and other elements like GPS guidance, sensors, drones, variable fertiliser delivery, satellite and aerial imagery, and plant health indicators. By monitoring low temperatures, created LSTM deep learning models to predict frost in plants. LSTM models produced outstanding time-series prediction results to find/expect ice in the plants despite their high computational cost. 3.6 Fruit Counting In order to estimate crop yields in agriculture, it is essential to consider how difficult it is to identify and count the fruits on trees. Manual counting takes a lot of time and labour. The automated crop counting method can help organize harvesting schedules and project yields to boost output and profit margin. In order to determine the precise amount of fruits, a simulated model of a deep convolutional neural network for yield estimate is constructed and evaluated. An average test accuracy of 91% was demonstrated by experimental findings using a modified version of the Inception-ResNet architecture. Modern technology in the agriculture sector are required to produce enough bio food to feed the world's population, which ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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is expanding quickly. Given the growing environmental issues that adversely affect this industry, it was important to switch to the usage of contemporary technologies to effectively address these problems and challenges. A wide range of topics are covered by the vast field of "smart agriculture," including sensor development, parameter monitoring, data collection, network convergence and maintenance, cluster formation of sensor nodes, cluster head selection, data compression and aggregation, security and integrity, design and development of the expert system, and artificial intelligence-based decision making, among many others. The number of projects in the field of agriculture has significantly expanded as a result of the development of artificial intelligence and deep learning over the past two decades. The analysis has demonstrated that deep learning techniques perform better than standard methods in most connected fields and produce better outcomes. Any AI or deep learning algorithm's conclusions can be significantly influenced by a few important aspects that were disregarded. We found that before using a deep learning algorithm to determine the best course of action, crop, soil, environment, and pest-related information is gathered using field sensors; however, other factors, such as weather forecasts, crop standard practices, historical crop data, farmer input, and governmental policies, are not taken into consideration. 4. Deep Learning, Robotics and Artificial Intelligence: Benefits and Drawbacks Deep learning has the benefit of automatically determining and tuning features for the desired result. The ability to use the same neural network-based method for a variety of applications and data kinds is another advantage. The deep learning architecture is additionally adaptable and can be used to solve new challenges in the future. Deep learning also performs well in terms of generalization. While deep learning models require more time to train than other conventional approaches, ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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they test very quickly. Even with the use of data augmentation techniques, deep learning has a noteworthy downside in that it requires a large number of datasets to perform well. Agriculture has few publicly accessible datasets; thus, many times researchers have to create their own sets of photos. Many hours or even days of labor may be required.

Fig 3: Robotic Harvesting Equipment for Picking Up Food 5. Future of Deep Learning, Robotics and Artificial Intelligence in Agriculture Agriculture is one of the more complicated fields of application because each region has unique climate patterns, natural features, and other characteristics. Therefore, the necessity for technology to separate the important components and analyze the data gathered is crucial. When real-time changes are taken into account, this demands a sizable amount of data to explore. As a result, deep learning is among the most significant technologies in this area because it can perform these tasks using the right algorithms, such CNN and RNN. An algorithm creates a probabilistic model before taking any decisions when it is fed ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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field data, such as climate parameters, soil kinds, weather patterns, and other variables. Tracking various ailments is crucial before suffering food or money losses due to misdiagnosis. This system can identify the kind and severity of the disease after scanning through pictures of sick plants from a decade ago. The same holds true for how the weather develops. The primary benefit of a deep learning model is that the computer programme develops the chosen characteristic on its own without input. We get more knowledgeable and prepared to work in the unpredictable and ever-changing real-time environment thanks to unsupervised learning. It is crucial since the IoT's importance is only increasing and the majority of data produced by devices and people is unstructured and unclassified. Deep learning performs better than conventional techniques like ANN, SVM, RF, etc. Deep learning models for automatic feature extraction are more effective than traditional feature extraction methods. 6. Conclusion The learning (training) procedure involved in creating an accurate vision-based artificial intelligence system necessitates the collection and photographing of several samples in a natural and dynamic environment to effectively depict the operating settings of that device. A deep learner (AI technology) often performs better as the volume of high-quality data increases, allowing the system to get past a range of imaging problems like inadequate illumination, sloppy alignment, and incorrect object cropping. These AI technologies and algorithms can be combined with mobile hardware to create a platform with the capacity to detect and find pests and diseases at a low cost, as well as to provide a prescription map (compatible with precision equipment) for the variable rate application of agrochemicals. These technologies will enable pesticide applicators to use less pesticide, pay less for it, and potentially have less of an impact on the environment by applying the right amount of pesticides just where they are required. These technologies can also be used to ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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create mechanised fruit and vegetable harvesting or trimming equipment that is precise and affordable. To create affordable and effective AI-based systems for precision agriculture applications, more research is required. Important contributions that have been published as well as difficulties that have been resolved were discussed. We took into account technical information from the datasets utilized, deep learning models, the work environment, data pretreatment, data augmentation approaches, and the outcomes shown in the reference articles when conducting this survey. According to our research, deep learning outperforms other common image processing algorithms, albeit it can do even better if certain other criteria are taken into account. Deep learning also beats current, widely used image processing algorithms and offers a high level of accuracy. Additionally, utilizing deep learning methods, we suggested a hybrid model for the early detection of plant leaf diseases. References Liu, Q.; Yan, Q.; Tian, J.; Yuan, K. Key technologies and applications in intelligent agriculture. J. Phys. Conf. Ser. 2021, 1757, 012059. Kitzes, J.; Wackernagel, M.; Loh, J.; Peller, A.; Goldfinger, S.; Cheng, D.; Tea, K. Shrink and share humanity‘s present and future ecological footprint. Philos. Trans. the Roy. Soc. Lond. B Biol. Sci. 2008, 363, 467–475. FAO. How to Feed the World in 2050; Food and Agriculture Organization of the United Nations: Rome, Italy, 2009. Kamilaris, A.; Prenafeta-Boldú, F.X. Deep learning in agriculture: A survey. Comput. Electron. Agric. 2018, 147, 70–90. Ren, C.; Dae-Kyoo, K.; Jeong, D. A survey of deep learning in agriculture: Techniques and their applications. J. Inf. Processing Syst. 2020, 16, 1015–1033. Kumar, A.; Shreeshan, S.; Tejasri, N.; Rajalakshmi, P.; Guo, W.; Naik, B.; Marathi, B.; Desai, U. Identification of waterROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Chapter 8 Role of Agroforestry in Modern Agriculture Technology 1

Disha Joshi*1 and Anshul mishra2 Ph.D. Scholar, Department of Agronomy, MPUAT, Udaipur, Rajasthan 2 M.Sc. Scholar, Department of GPB, MPUAT, Udaipur, Rajasthan Corresponding Author’s Email : [email protected]

INTRODUCTION India has critical imbalanced natural resource base with about 18 per cent human, 15 per cent livestock population of the world, which is supported on 2.4 per cent geographical area, 1.5 per cent forest and pasture lands and 4.2 per cent water resources. Because the country's agricultural sector growth has lagged behind that of the 1980s in the post-reform era, there are grave concerns about how well it is performing. Farmers are currently moving away from agriculture due to the low pay and considerable risk associated with weather fluctuations. Although India's agriculture has undergone a number of adaptations throughout the years, the current state of the sector has been significantly impacted by climate change (Mishra et al. 2011, Ram et al. 2016). As a result, it is expected that there will be a severe threat to increasing food grain output and fulfilling the needs of the growing population for food, fiber, fuel and fodder. Since ancient times, agroforestry has been practiced in India as a way of life and a source of income. Growing the amount of agroforestry in the nation can aid in addressing some of the most significant issues brought on by climate change (CAFRI Vision 2015, 2020, Dhyani et al. 2016). The relevance of agroforestry is expected to grow due to projections of decreased agricultural land use and rising need for food grain and fuel (2 times), fodder (1.5 times) and timber ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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production (3 times) (Dhyani and Handa 2014). Almost half of the demand for fuelwood is met by agroforestry, along with 65% of the demand for small timber, 70% to 80% of the demand for wood for plywood, 60% of the need for raw materials for paper pulp and 10% to 11% of the demand for green feed for livestock (NRCAF 2013). Food, fuelwood, fodder, timber, poles, etc. (provisioning), hydrological benefits and modifications of microclimate (regulatory), nutrient cycling and agro-biodiversity conservation (supporting service), recreation and aesthetics (cultural service), along with securing the livelihood of its practitioners, are just a few of the ecosystem services provided by agroforestry. Since global organized agroforestry research began, the nation has also been in the forefront. It created strong agroforestry practices, inventions and science that are gaining attention on a global scale (CAFRI Vision 2015). Particularly with the adoption of the Kyoto Protocol to the United Nations Framework Convention on Climate Change (UNFCCC), agroforestry has gained significant interest in developing countries for its potential to address a variety of issues and provide several economic, environmental and socio-economic benefits. Agroforestry can play effective role in lowering the vulnerability, enhancing resilience of agriculture and buffering households against climate extremes (Dhyani 2014). HISTORY OF AGROFORESTRY IN INDIA In India, agroforestry has historically been significant and a source of subsistence since the Mesolithic period (10,000 to 4,000 years ago). The relationship between an irrigation tank and trees is also discussed in some Vedic literature, such as the Brhat Samhita (Kumar and Sikka 2014). One of the earliest human land use activities in India is the practice of pasturing animals in forest areas. The majority of Rishis lived in harmony with nature because civilization first emerged in forests. Close to the rishi's ashram, there was a harmonious combination of meadow, trees, ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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birds and animals. Following the civilizations of Harappa and Mohanjodaro, tree worship is fairly widespread. It is possible to date the cultivation of numerous fruit trees, including banana, pomegranate, datepalm, aonla, ber and coconut, as well as numerous agricultural products and cattle rearing, to the Chalcolithic period of civilization. Many trees, including P. cineraria, Ficus religiosa and Butea monosperma, have been referenced as having religious significance in ancient Vedic literature and other scriptures. The Rigvedic hymn "the nature" demonstrates the significance of trees at the time Close to the rishi's ashram, there was a harmonious combination of meadow, trees, birds and animals (Bhatla et al. 1984). ‘May plants, the water and the sky Preserve us and woods and mountains With their trees for tresses’ (Rigveda V. 41.11) Importance of Modern Agricultural Technology Modern agricultural technology plays a pivotal role in enhancing the efficiency, productivity and sustainability of agroforestry practices. Advancements in technology, such as precision agriculture, biotechnology and data-driven decisionmaking, have revolutionized the agricultural sector. In the context of agroforestry, modern technology aids in tasks like soil analysis, crop monitoring, pest management and resource optimization. These technologies contribute to increased yields, reduced resource wastage and improved overall management of agroforestry systems. Purpose and Scope of the Chapter The purpose of this chapter is to explore the intersection of agroforestry and modern agricultural technology. The chapter aims to delve into the ways in which contemporary technological innovations are being applied to agroforestry systems,

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highlighting their benefits and challenges. The scope of the chapter encompasses the following key points: 1. Examination of different agroforestry systems and their potential benefits in terms of sustainable land use, biodiversity conservation, climate resilience and livelihood improvement. 2. In-depth analysis of various modern agricultural technologies that can be integrated into agroforestry practices. This includes precision farming techniques, remote sensing, GIS (Geographic Information Systems) and IoT (Internet of Things) applications. 3. Discussion on how these technologies contribute to enhancing productivity, optimizing resource allocation and mitigating environmental impacts within agroforestry systems. 4. Exploration of case studies and real-world examples where the marriage of agroforestry and modern technology has resulted in successful outcomes. 5. Identification of challenges and potential barriers to the widespread adoption of modern agricultural technology in agroforestry contexts. This could encompass issues related to accessibility, affordability, technical know-how and socio-economic factors. 6. Examination of future trends and possibilities for further integration and innovation at the nexus of agroforestry and technology. Agroforestry Systems and Types 1. Alley Cropping: In alley cropping, rows of trees or shrubs are planted along with alleys of annual crops. The crops are grown between the rows of trees. This system provides benefits such as improved soil fertility, reduced erosion and increased crop diversity. The trees can also provide shade and windbreak benefits to the crops. 2. Silvopasture: ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Silvopasture integrates trees, forage crops and livestock. Trees provide shade for the animals and contribute to improved forage growth by moderating temperature and reducing stress on the livestock. This system enhances biodiversity, carbon sequestration and can improve overall livestock productivity. 3. Windbreaks and Shelterbelts: Windbreaks consist of rows of trees or shrubs planted around agricultural fields to provide protection from strong winds. Shelterbelts are similar but are typically designed to protect farmsteads, livestock, or sensitive areas. These systems help reduce wind erosion, prevent soil degradation and create microclimates that benefit crops. 4. Forest and Fruit Tree Intercropping: This involves cultivating crops or fruit trees underneath the canopy of larger trees. The canopy trees provide shade and protection to the understory crops, which can include shadetolerant plants, medicinal herbs, or fruit-bearing trees. This type of agroforestry system enhances biodiversity and allows efficient land use. 5. Multistrata Agroforestry: Multistrata agroforestry involves multiple layers of vegetation, such as tall trees, shorter trees, shrubs and ground cover crops, all planted together in a single area. This system maximizes space utilization and resource efficiency, leading to increased biodiversity and improved ecosystem services. 6. Comparing Agroforestry Systems: Comparing agroforestry systems involves assessing their benefits, drawbacks and suitability for specific contexts. Factors such as climate, soil type, crop choices and intended goals play a significant role in determining the most appropriate system. Some systems might prioritize soil conservation, while others focus on enhanced crop yields or biodiversity conservation. Integration of Technology in Agroforestry ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Agroforestry is the practice of combining agricultural and forestry practices to create sustainable land-use systems. Integrating technology into agroforestry can enhance the efficiency, productivity and sustainability of these systems. Here are some ways technology can be integrated into agroforestry: 1. Geographic Information Systems (GIS): Geographic Information Systems (GIS) involve the use of spatial data to analyze, interpret and visualize relationships between different elements on a landscape. In agroforestry, GIS can be used to create maps that display the distribution of tree species, crop types, soil types and topography. This information can help farmers and land managers make informed decisions about where to plant specific trees and crops, optimize resource allocation and plan for the best land-use arrangement. 2. Remote Sensing Applications: Remote sensing involves the use of satellite or aerial imagery to collect data about the Earth's surface. In agroforestry, remote sensing can provide valuable insights into vegetation health, land use changes and crop growth patterns. By analyzing this data, farmers and researchers can monitor the growth and health of trees and crops, detect early signs of stress or disease and make adjustments to management practices accordingly. 3. Precision Agriculture Techniques: Precision agriculture techniques involve the use of technology such as Global Positioning Systems (GPS), sensors and data analytics to optimize the use of resources in agriculture. In agroforestry, precision agriculture can help tailor irrigation, fertilization and pest management practices to the specific needs of different trees and crops. This reduces resource wastage and environmental impact while maximizing yields. 4. Ag-Tech for Monitoring and Management: Agricultural technology (AgTech) can provide tools for real-time monitoring and management of agroforestry systems. For instance, sensor networks can be deployed to monitor soil ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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moisture levels, temperature, humidity and other environmental parameters. This data can be used to trigger automated irrigation, pest control, or other interventions, enhancing the health and productivity of the agroforestry system. 5. Modeling and Simulation Tools: Modeling and simulation tools allow farmers and researchers to create virtual representations of agroforestry systems and predict how different management scenarios might play out. These tools can simulate factors like tree growth, crop yields, water use and carbon sequestration. By testing various strategies in a virtual environment, stakeholders can make more informed decisions before implementing them in the real world. Digital Agriculture and Agroforestry 1. Internet of Things (IoT) in Agroforestry: The Internet of Things (IoT) involves connecting devices and sensors to the internet to gather and exchange data. In agroforestry, IoT can be applied to monitor and manage various aspects of the system. For example, sensors can be placed in the soil to monitor moisture levels, in the trees to monitor growth and in the environment to monitor temperature and humidity. These data can be collected in real-time and analyzed to optimize irrigation, predict disease outbreaks and enhance overall resource efficiency. 2. Data-Driven Decision Making: Data-driven decision making involves using data to inform and guide agricultural practices. In agroforestry, this could mean collecting data from various sources such as satellite imagery, weather stations and on-site sensors. This data can then be analyzed to make informed decisions about planting, pruning, harvesting and other management activities. By leveraging data, farmers and land managers can make more precise and efficient choices, leading to improved yields and resource utilization. 3. Smart Sensors for Tree-Crop Interaction Monitoring: ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Smart sensors play a crucial role in agroforestry by providing real-time insights into the interactions between trees and crops. These sensors can monitor factors like light intensity, soil moisture, nutrient levels and even the health of individual plants. By understanding how trees and crops interact, farmers can optimize planting configurations, improve soil health and reduce competition for resources, ultimately increasing productivity and sustainability. 4. Blockchain in Agroforestry Certification: Blockchain technology can be used to enhance transparency and traceability in agroforestry certification processes. Through blockchain, information about the origin of tree seedlings, planting practices, maintenance procedures and sustainable practices can be recorded in an immutable and transparent manner. This can help verify the authenticity of agroforestry products, such as timber or non-timber forest products and ensure that they meet certification standards for sustainability and ethical sourcing. AGROFORESTRY: SMART PRACTICE FOR CLIMATE CHANGE MITIGATION AND ADAPTATION Developing nations like India would be more affected by climate change's negative effects, particularly on agriculture. Global gatherings like COPs are ineffective at reducing the amounts of greenhouse gases (GHG) in the atmosphere. Therefore, efforts at mitigation will only partially mitigate the effects of climate change (Verchot et al 2007). Agroforestry offers a singular opportunity to combine the twin goals of mitigating and adapting to climate change. Agroforestry practices minimize greenhouse gas emissions from soils and store carbon in woody biomass and soils (Dhyani et al 2016). Recent research under various agroforestry systems in different ecological settings has highlighted how important agroforestry systems are for increasing livelihood security and reducing vulnerability to ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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climate change. Agroforestry systems increase and store carbon stocks in soil and above-ground biomass.

Figure 1: Carbon sequestration potential of different land use and management options (adapted from IPCC 2000). Out of all landuse analyzed, agroforestry has a great potential for sequestration of carbon from the atmosphere (Fig. 1). Intended Nationally Determined Contributions (INDCs), which India just submitted to the United Nations Framework Convention on Climate Change (UNFCCC), are voluntary commitments to take action to mitigate climate change. India wants to reduce its GDP's emission intensity by 33–35% from 2005 levels by 2030. The goal will be met by increasing the area covered by trees through afforestation, agroforestry and increasing the percentage of renewable energy sources like solar energy, biofuel and bioenergy, among others. Challenges and Limitations of agroforestry Agroforestry is an integrated land use management approach that combines agricultural and forestry practices to achieve multiple benefits such as enhanced biodiversity, increased crop yields, improved soil health and sustainable resource management. However, like any complex system, agroforestry ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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also comes with its own set of challenges and limitations. Here are some key challenges associated with agroforestry: 1. Knowledge and Information Gaps: Agroforestry practices can be highly context-specific, requiring knowledge about local climate, soil conditions and plant interactions. Lack of accurate and localized information can hinder the successful implementation of agroforestry systems. Scientific research on agroforestry is still evolving and there might be limited data available to guide practitioners in certain regions or for specific species combinations. 2. Access to Technology in Rural Settings: Agroforestry can benefit from modern technologies such as remote sensing, precision agriculture tools and modeling software. However, many rural areas, where agroforestry is often practiced, may lack access to these technologies, limiting their adoption and effectiveness. 3. High Initial Costs and Investment: Establishing agroforestry systems can require substantial initial investments, including costs for purchasing tree saplings, implementing proper soil management practices and setting up irrigation systems.The long-term benefits of agroforestry, such as increased crop yields and ecosystem services, might take time to materialize, making it challenging for resource-limited farmers to justify the upfront costs. 4. Technical Skills and Training: Successful agroforestry implementation demands a certain level of technical expertise and understanding of both agricultural and forestry practices. Farmers may need training in areas like selecting compatible tree-crop combinations, pruning techniques, pest and disease management and sustainable land management practices. 5. Balancing Traditional Knowledge with Technology: Integrating traditional knowledge of local communities with modern agricultural practices and technologies can be ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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challenging. Finding the right balance between these approaches is important to ensure that agroforestry systems are culturally and ecologically appropriate. Overreliance on technology might disregard traditional ecological knowledge that has been passed down through generations, potentially leading to unsustainable practices. Future Directions and Outlook of agroforestry Agroforestry, the practice of integrating trees and shrubs with crops and livestock, has gained significant attention due to its potential to address various agricultural and environmental challenges. As we look toward the future, several key directions and outlooks can be identified in the field of agroforestry: 1. Innovations on the Horizon: Advancements in technology, genetics and ecological understanding are likely to drive innovations in agroforestry. This could involve the development of tree species that are better suited to specific agroforestry systems, such as those with enhanced nitrogen-fixing abilities or increased resilience to climate change. Genetic editing techniques might enable the creation of trees that provide higher yields of fruits, nuts, or other products. Additionally, precision agriculture tools, such as drones and sensors, could be integrated into agroforestry management to monitor plant health, soil conditions and water availability. 2. Scaling Up Agroforestry through Technology: Technology will play a crucial role in scaling up agroforestry practices. Geographic information systems (GIS), remote sensing and data analytics can help identify suitable areas for agroforestry implementation. Mobile apps and online platforms might provide farmers with information about tree-crop interactions, optimal planting designs and market trends for agroforestry products. Furthermore, digital tools can facilitate knowledge sharing and networking among agroforestry practitioners, helping to disseminate best practices. 3. Policy and Institutional Support: ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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For agroforestry to flourish, supportive policies and institutions are necessary. Governments and international organizations could provide incentives such as subsidies, tax breaks and grants to encourage farmers to adopt agroforestry practices. Zoning regulations might be adjusted to accommodate mixed land use. Additionally, research institutions and extension services could receive funding to develop agroforestry-specific training programs and technical resources for farmers. 4. Sustainability and Ethical Considerations: As agroforestry expands, maintaining its sustainability and ethical dimensions will be crucial. Biodiversity conservation should be a priority, as agroforestry systems can provide habitats for various species. Careful management is essential to prevent invasive species from spreading within agroforestry systems and nearby ecosystems. Ethical considerations also extend to the rights and knowledge of Indigenous and local communities, who often have traditional agroforestry practices that should be respected and integrated into broader efforts. The Prospective Role of Agroforestry in Agriculture Agroforestry is a sustainable land management system that combines agricultural practices with the cultivation of trees and/or shrubs on the same piece of land. This integrated approach offers several prospective benefits for agriculture and the environment: 1. Biodiversity Conservation: Agroforestry systems create diverse habitats for plants, animals and microorganisms. The presence of various species of trees, crops and livestock enhances ecosystem resilience and contributes to maintaining biodiversity. 2. Soil Health and Fertility: Trees in agroforestry systems can improve soil structure, reduce erosion and enhance soil nutrient cycling. Leaf litter and organic matter from trees contribute to the enrichment of the soil, leading to increased fertility and productivity of the land. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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3. Climate Change Mitigation: `Trees in agroforestry systems act as carbon sinks, sequestering carbon dioxide from the atmosphere. This can help mitigate climate change by reducing greenhouse gas emissions and promoting carbon storage in both above-ground biomass and soil. 4. Improved Water Management: Tree roots help stabilize the soil and reduce water runoff, preventing soil erosion and promoting efficient water use. The shade provided by trees can also reduce evaporation, leading to improved water retention in the soil. 5. Crop and Livestock Diversification: Agroforestry allows farmers to diversify their income sources by incorporating both traditional crops and tree products (e.g., fruits, nuts, timber) into their operations. Livestock can also benefit from the shade and fodder provided by trees. 6. Enhanced Resilience to Climate Variability: Agroforestry systems are often more resilient to extreme weather events like droughts and floods due to the improved soil structure, water retention and protection against wind and sun that trees provide. 7. Economic Benefits: Agroforestry systems can provide a steady source of income through the sale of tree products, while also reducing production costs by improving soil fertility and reducing the need for synthetic fertilizers and pesticides. 8. Cultural and Traditional Values: Many agroforestry systems are rooted in traditional agricultural practices, often carrying cultural significance and promoting local knowledge and sustainable land use practices. 9. Habitat for Beneficial Organisms: Trees in agroforestry systems can provide habitats for pollinators, natural enemies of pests and other beneficial ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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organisms. This can lead to reduced pest pressure in crops, decreasing the need for chemical pesticides. 10. Reduced Pressure on Natural Forests: By providing alternative sources of wood, fruits and other products, agroforestry can help alleviate the pressure on natural forests and reduce deforestation rates. 11. Nutritional Diversity: Incorporating diverse tree species with edible fruits and nuts can contribute to increased nutritional diversity in local diets, improving community health. References Bhatla N., Mukherjee T. and Singh S. 1984 Plants: Traditional worshiping. Indian Journal of History of Science, 19(1): 3742. CAFRI Vision 2050 Jhansi: CAFRI, 2015. Dhyani S.K. 2014 National Agroforestry Policy 2014 and the need for area estimation under agroforestry, Current Science, 107(1) 9-10. Dhyani S.K. and Handa A.K. 2014 Agroforestry in India: Current scenario, Indian Farming 63(11) 6–8. Dhyani S.K., Asha R. and Dev I. 2016 Potential of agroforestry systems in carbon sequestration in India, Indian Journal of Agricultural Sciences 86 (9) 1103–12 Kumar B.M. and Sikka A.K. 2014 Agroforestry in South Asia: Glimpses from Vedic to present times, Indian Farming 63(11): 2-5. Mishra M., Panda G.R. and Padhi B.K. 2011 Sustaining Indian Agriculture in the Era of Climate Change in Genetics, Biofuels and Local Farming Systems, Sustainable Agriculture Reviews (ed.) E. Lichtfouse 7, DOI 10.1007/978-94-007-1521-9 8. NRCAF 2013 Vision 2050, National Research Centre for Agroforestry, Jhansi. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Ram A., Dev I., Kumar D., Uthappa A.R., Tewari R.K., Singh R., Sridhar K.B., Singh M., Srivastava M., Kumar V. and Chaturvedi O.P. 2016 Effect of tillage and residue management practices on blackgram and greengram under bael (Aegle marmelos L.) based agroforestry system, Indian Journal of Agroforestry 18(1) 90-95. Verchot L.V., Noordwijk M.V., Kandji S., Tomich T., Ong C., Albrecht A., Mackensen J., Bantilan C., Anupama K.V. and Palm C. 2007 Climate change: linking adaptation and mitigation through agroforestry, Mitigation and Adaptation Strategies for Global Change DOI 10.1007/s11027-0079105-6.

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Chapter 9 Application of Drones in Modern Agriculture Technology Shubham Dhakad1, Ghanshyam Panwar2 and Shraddha Sethi3 1 M.Tech. (FMPE) Research Scholar, College of Agriculture Engineering and Technology, Anand Agriculture University, Gujarat 2. Ph.D. (FMPE) Research Scholar, College of Agriculture Engineering and Technology, Anand Agriculture University, Gujarat 3 M.Tech. (PFE) Research Scholar, College of Agriculture Engineering and Technology, Anand Agriculture University, Gujarat * [email protected]

INTRODUCTION Agriculture is the backbone of India. As per the Land Use Statistics 2018-19, the total geographical area of the country is 328.7 million hectares, of which 139.3 million hectares are the reported net sown area and 197.3 million hectares are the gross cropped area with a cropping intensity of 141.6%. The net area sown works out to be 42.4% of the total geographical area. The net irrigated area is 71.6 million hectares. (Department of Agriculture & Farmers Welfare, Ministry of Agriculture & Farmers Welfare, Government of India, 2021). Indian agriculture is characterized by agroecological diversities in soil, Rainfall, Temperature, and Cropping system. Land preparation, Sowing, Irrigation, Weeding, Spraying, Harvesting and Threshing are the operations, which utilize most of the energy used in agriculture. The various operations such as tillage, land levelling, irrigation, sowing and planting, use of fertilizers, plant protection, harvesting and threshing need a high degree of precision to increase the efficiency of the inputs and reduce the farm losses. Availability of adequate farm power is very crucial for timely farm operations to increase production and

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productivity and handle crop production to reduce losses. The operation-wise farm mechanization level in the country is about 40% for tillage and seedbed preparation, 29% for seeding and planting, 35-45% for plant protection, 60-70% for harvesting and threshing for wheat and rice and below 15% for other crops (Tiwari et al., 2019). The overall farm mechanization in India has been lower at 40-45% compared to other countries such as the USA (95%), Brazil (75%) and China (57%). Farm mechanization will help to enhance the overall productivity and reduces input cost by 1520% savings in seeds, 15-20% savings in fertilizers, 5-20% increase in cropping intensity, 20-30% savings in time, 20-30% reduction in manual labour and 10-15% overall increase in farm production (Tiwari et al., 2019). The availability of agricultural labour is becoming a problem day by day as rural youth do not willing to work in the agriculture sector and migrate to urban areas. During peak seasons, there is a very shortage of labour available only at a higher cost per hectare or acre due to the labour shortage. The wages for labour have increased day by day. Due to the intensive involvement of labour in different farm operations, the cost of production of many crops is quite high. Thus, there is a need to enhance the level of farm mechanization in the country. Drones are the unmanned aerial vehicles that are remotely controlled by an operator. Today, drones are used for a wide range of functions, including monitoring climate change, delivering goods, aiding in search and rescue operations, and in filming and photography. Although originally built for military purposes, drones have seen rapid growth and advancements and made a break to consumer electronics. Their original use was as weapons, in the form of remotely-guided aerial missile deployers. However, today, drones have found a wide range of applications for civilian use, especially in the form of small quad copters and octocopters. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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USE OF AGRICULTURE DRONE i. Crop Monitoring and Assessment: One of the primary applications of drones in agriculture is crop monitoring and assessment. Drones equipped with high-resolution cameras and advanced sensors, such as multispectral and hyperspectral sensors, play a crucial role in this domain. By flying over agricultural fields, drones capture aerial imagery, providing realtime and high-resolution views of crops. Multispectral and hyperspectral sensors detect specific wavelengths of light reflected by plants, enabling the creation of detailed vegetation indices, such as the Normalized Difference Vegetation Index (NDVI). These indices provide valuable information about crop health, identifying areas of stress, nutrient deficiencies, diseases, and pest infestations. Early detection of such issues empowers farmers to take prompt action, optimize crop management practices, and maximize yields. Drones can cover large areas in a short time, providing comprehensive insights into the spatial distribution of crop health. The data collected by drones is then processed using sophisticated image analysis techniques and artificial intelligence algorithms, enabling the creation of detailed crop health maps and reports. Farmers can use this information to make informed decisions about irrigation schedules, fertilization, and pest control measures, ensuring timely interventions and minimizing crop losses. ii. Precision Agriculture and Variable Rate Application: Precision agriculture is a data-driven approach to farming that optimizes the use of resources and minimizes environmental impact. Drones play a central role in precision agriculture by collecting real-time data about crop health, soil characteristics, and environmental conditions. This data is then processed to create detailed field maps, highlighting variations in crop health and soil properties. With this information, farmers can implement variable rate application (VRA) techniques, adjusting the application of inputs, such as fertilizers, pesticides, and water, ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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based on the specific needs of different zones within the field. VRA ensures that each part of the field receives the precise number of resources it requires, reducing waste, enhancing nutrient uptake, and improving overall crop quality. Drones equipped with GPS technology and onboard actuators can control the release of inputs with unparalleled precision, making VRA a practical and efficient approach to agriculture. By adopting VRA, farmers can significantly reduce their input costs, minimize environmental pollution, and improve the sustainability of their farming operations. iii. Crop Dusting and Spraying: Traditionally, crop dusting and spraying were conducted using manned aircraft or groundbased machinery, which could be costly, time-consuming, and pose safety risks to pilots and farmworkers. Drones equipped with spraying systems have revolutionized this practice. They can navigate with precision, delivering chemicals and nutrients directly to target areas, minimizing chemical drift and ensuring the precise application of inputs. This level of accuracy reduces chemical usage, minimizes environmental contamination, and decreases the exposure of farmers to potentially harmful substances. Moreover, drones can access hard-to-reach areas, such as steep terrain or densely planted fields, which were challenging for traditional methods. Autonomous and remotely piloted drones equipped with spraying systems can cover large areas efficiently and safely. Additionally, drones can be programmed to follow pre-defined flight paths, ensuring complete coverage of the field without missing any spots. The ability of drones to execute precise spraying operations improves the efficacy of pest control measures and contributes to sustainable and responsible agriculture. iv. Soil Analysis and Mapping: Healthy soil is fundamental for productive and sustainable agriculture. Drones equipped with advanced sensors, such as electromagnetic induction or electrical resistivity sensors, perform soil analysis and mapping tasks. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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These sensors measure properties like soil moisture content, texture, and nutrient levels. By generating detailed soil maps, farmers gain insights into soil variability across their fields. Sitespecific soil data allows for the implementation of tailored soil management strategies, including precise fertilizer application, liming, and erosion control measures. With this information, farmers can optimize their use of fertilizers and other soil amendments, reducing input costs and minimizing nutrient runoff into water bodies. Improved soil health leads to enhanced crop growth, water retention, and nutrient cycling, ultimately contributing to sustainable farming practices. v. Irrigation Management: Water scarcity is a major challenge in agriculture, especially in regions facing drought and erratic rainfall patterns. Efficient irrigation management is critical to ensuring optimal crop growth and water use. Drones equipped with thermal cameras can assess crop water stress levels and identify variations in soil moisture content. By gathering this information, farmers can make informed decisions about irrigation schedules and water application. Precision irrigation, enabled by drone technology, involves applying water only where and when it is needed. This approach conserves water resources, reduces water wastage, and prevents overwatering, which can lead to waterlogging and soil degradation. Moreover, drones can be used to monitor the efficiency of irrigation systems, identify leaks or malfunctions, and improve overall water use efficiency on the farm. vi. Planting and Seeding: Drones are increasingly being utilized for autonomous planting and seeding operations. Equipped with GPS-guided systems and seed dispensers, drones can precisely drop seeds at predetermined locations and depths, ensuring optimal spacing and uniform germination. This technology enhances planting accuracy, reduces labor requirements, and minimizes seed wastage, leading to improved crop establishment and higher yields. Autonomous planting is ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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particularly advantageous in large-scale farming operations, where efficiency is critical. By enabling efficient and accurate planting, drones contribute to improved crop productivity and overall farm profitability. Moreover, drones can be programmed to operate during specific weather conditions, ensuring that planting operations occur under optimal environmental conditions for the best possible outcomes. vii. Crop Health Assessment: Drones equipped with advanced imaging techniques and sensors can conduct comprehensive crop health assessments. Using NDVI and other vegetation indices, drones analyze the reflectance of light from crops, providing detailed information about plant health, biomass, and stress levels. This data can be used to generate highresolution crop health maps, highlighting areas of concern and indicating the overall health and growth status of the crops. By monitoring crop health regularly, farmers can detect early signs of stress, diseases, and pest infestations, allowing them to take timely action to mitigate the impact of these factors on crop yield and quality. Proactive crop health assessment facilitates precise pest and disease management, minimizing the need for broadspectrum chemical applications and promoting sustainable pest control strategies. viii. Livestock Monitoring: Beyond crop-focused applications, drones also find practical applications in livestock farming. Farmers can use drones equipped with cameras and thermal sensors to monitor large grazing areas and herd behavior. These aerial surveys help identify sick or injured animals, track herd movement patterns, and assess livestock health more effectively. The data collected by drones can be used to detect abnormal behaviors or health issues early, facilitating timely intervention and reducing the risk of disease outbreaks. ix. Crop health & stress analysis: Drones furnished with Multispectral camera sensors can identify disease and stress in the initial stages, sometimes before it is even evident from the ground ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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or with standard color cameras. Drone surveys also provide realtime imagery of the fields for precision agriculture. The data obtained is processed and analyzed during Irrigation Monitoring, Corp Health Monitoring, and other fundamental elements to encourage farmers to focus on treatment plans. x. Field soil analysis: The soil quality of crops can either make or break a farmer's productivity. Soil Analysis is a crucial step to be taken by farmers during the crop-cycle. Drones, such as the DJI Phantom 4 Pro, provide real-time and precise analysis of soil's overall health. Through Precision Agriculture, one can discern issues surrounding soil quality, nutrient management, or dead soil zones. This data supports farmers in determining the most effective farming patterns of planting, managing crops, and soil. xi. Drone spraying: The sprayer drone is one piece of equipment that is trying to make its place in the industry. Here we try to beat the ill-effect of the pesticides on farmers and use to spray pesticides over large area briefly intervals of your time compare to standard spraying by using automatic fertilizer sprayer and to discover the possibility for sprayer drones to become a competing mode of applying pesticide and insecticide to agricultural fields. It could be something in the future that could help farmers spray crops in a much improved and more efficient manner. However, some factors may reduce the yield, or even cause damage (e.g., crop areas not covered in the spraying process, overlapping spraying of crop areas, applying pesticides on the outer edge of the crop). Climatic condition, such as the intensity and direction of the wind while spraying add further complexity to the control problem. This device is essentially combination of spraying mechanism on a quad copter frame. This model is employed to spray the pesticides content to the areas that cannot easily accessible by humans. The GPS can be use automatically guide the quad copter and remotely control in large areas. A quad copter controlled by autopilot controller and ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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payload is controlled by RF Transmitter and motors. The process of applying the chemicals is controlled by means of the feedback obtained from the wireless sensor network (WSN) deployed at ground level on the crop field. The aim of this solution is to support short delays in the control loop so that the UAV spraying can process the information from the sensors. Classification of drones Drones are classified on many bases such as size, range of flight, number of rotors, etc. some of the important classifications are given below;

i) Classification based on sizes No

Size

Weight

1.

Micro

250g or less

2.

Very small

250g to 2 kg

3.

Small

2kg to 25 kg

4.

Medium

25kg to 150 kg

5.

Large

More than 150 kg

ii) Classification based on Range No

Size

Range

1.

Very Close Range

5 km

2.

Close Range

50 km

3.

Short Range

150 km

4.

Mid-Range

650km

iii) Classification based on number of rotors They are classified as single rotor and multirotor drones a) Single rotor b) Multirotor (Quadcopter) ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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c) Multirotor (Hexacopter) d) Multirotor (Octocopter) MOTION CONTROLLED IN DRONE BY 3 MOVEMENTS. i) Yaw Rotation (ψ): Yaw is outlined as movement of drone either to left or right and it is controlled by throttle stick of transmitter. Yaw decides the direction of quad copter. ii) Pitch Rotation (θ): Pitch is outlined as the whole movement of drone either in forward direction or in backward direction. It is additionally controlled by throttle of receiver. Moving the throttle in forward direction moves hexacopter in forward direction whereas moving the throttle backward moves quad copter in backward direction. iii) Roll Rotation (ɸ): The movement regarding the longitudinal axis of drone is understood as roll motion. Left or right motion of throttle stick is followed by drone, it moves in towards right once when throttle move to right and moves to left once when throttle stick moves in left direction. DIFFERENT TYPES OF SENSORS USED IN DRONE i) Barometer: A barometer working principle is to convert atmospheric pressure into altitude. Pressure sensor can detect earth‘s atmospheric pressure. The data from Barometer helps in drone navigation and achieve desired altitude. Very good estimation of ascend and descend speeds is very vital for drone‘s flight control. STMicroelectronics has introduced pressure sensors, LPS22HD, with 200Hz of data rate to address this requirement of altitude estimation. ii) Accelerometer: Accelerometer is used to provide the acceleration force which the drone is subjected to in all three axis X, Y and Z. It also determines the tilt angle of drone in stationary position. Accelerometer is also used to give linear acceleration in horizontal and vertical direction. This data can be used to calculate velocity, direction and even rate of change of altitude of the drone. Accelerometer is also used to detect the vibration which the drone is experiencing. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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iii) Gyroscope: Gyroscope sensor detects angular velocity in three axis. So it can detect rate of change of angle in pitch, roll and yaw. Gyroscope is a critical sensor even in regular craft. The change in angle information is used to provide stability to drone and to prevent it from wobbling. The information from gyroscope is fed to motor control drivers to control the speed of motors dynamically to provide the stability to motor. Gyroscope also ensures that drones rotate at exact angle which is expected by user controls. iv) Magnetic Compass: Magnetic compass as the name suggests gives the sense of direction to the drone. It gives data of magnetic field in three X, Y and Z which the device is subjected to. This data is then fed into algorithm in the microcontroller to give heading angle w.r.t magnetic north. This information is then used to detect geographical directions. v) Flight controller: Flight controller board is used to control the functions of the drone such as movement, lifting, positioning, etc. FCB will be programmed for handling different sensors such as GPS, barometer, accelerometer, gyroscope etc. and components such as motors. It is built with a micro controller and communicates to the six brushless motors. BLDC motors connect with the rotors in directions of the UAV configuration model. These BLDC motors are controlled by the Electronic Speed controllers (ESC). The UAV controlled by the Radio channel transmitter and receiver. RC transmitter has number of channels for individual activity to control the UAV and based on the GPS coordinates, the microcontroller navigates the UAV. Following is some of the functions of the Flight Controller in drone. a) Altitude control: Initially drone will be hovered at required altitude, and then it is switched to Altitude Hold mode, which maintains the same altitude until it is switched back. For sensing the current altitude barometer is used. For more accurate altitude measurements additional sensor such as sonar can be used. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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b) Stability Control: The stability of the drone is maintained by the sensors such as accelerometer and Gyroscope through the program. c) GPS control: It is used only in autonomous mode. In this mode, the FCB through feedback mechanism compares stored GPS parameters with the acquired GPS; to change speed of motors for navigating the given path. After reaching the last location drone is landed safely. d) Brushless DC motors: BLDC (Brushless DC) motors do not use brushes for commutation. They are electronically commutated & the benefits are higher speed vs. force characteristics, High potency with quiet operation & terribly high speed vary with longer life. As there are not any brushes to wear out the lifetime of BLDC motor is very much longer. There is no sparking and far less electrical noise. e) Propeller: The propeller is responsible for the converting the power supplied by the engine into useful power for the flight. Here we need to use 3 clockwise propellers and 3 anti-clock wise propellers according to BLDC used. The power developed by the propellers depends on various parameters such as RPM of the BLDC, diameter, and pitch of the propeller. f) Electronic speed control: An electronic speed controller or ESC is an electronic circuit that controls and regulates the speed of an electrical motor it is used to vary the Revolution per Minute (RPM) of the motor. It is going to additionally offer reversing of the motor and dynamic braking. Miniature electronic speed controls are utilized in electrically powered-driven radiocontrolled models. g) Battery: The battery used should fulfill the power requirement of the whole system. It should be carefully selected based on power requirement for lifting the Hexacopter and on time of flight. h) Frame: There is no specification for choosing of the frame, but the frame used should be strong enough to hold all the ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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materials mounted on it. It should also provide sufficient space to place various components such as battery, electronic speed control, motors, sprayer, and pump, etc. i) Transmitter and Receiver: Transmitter and Receiver are the main components of the drone sprayer. These are needed for the sending and receiving the signals that are necessary for the proper working of the drone sprayer. SPRAYING MECHANISM: Spraying system is attached to the lower region off the UAV which as a nozzle beneath the pesticide tank to sprinkle the pesticide towards downstream. Sprayer module has two sections, they are 1) Transmitter section (Remote controller), 2) Sprayer with controller. Transmitter section used to control the actuator of sprayer module. The nozzle of sprayer module will be activated by remote control. Wherever we need to activate the sprayer, just make use of remote RF transmitter. Sprayer module contains two sections, spraying module and controller. Spraying module contains the spraying content i.e., pesticide or fertilizer and the controller section used to activate the nozzle of sprayer. The command is received from remote controller which is activated manually. Tank contains the chemical content which is going to spray on crops that may be a pesticide or fertilizer. A pressure pump is a component of the sprinkler system which pressurizes the pesticide to flow through the nozzle. To this pump, a splitter is connected which spits the pesticide to the two nozzles which are connected at the two opposite ends and spraying is achieved. A motor driver integrated circuit is used to pressure the pump as per the requirement. The Nozzle of the sprayer module will be activated by GPS device. This GPS module having the preloaded GPS coordinated, Liquid Pump Motor with Tank.

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References: Ahirwar, S., Swarnkar, R., Bhukya, S., & Namwade, G. (2019). Application of drone in agriculture. International Journal of Current Microbiology and Applied Sciences, 8 (1), 2500-2505. Department of Agriculture & Farmers Welfare, Ministry of Agriculture & Farmers Welfare, Government of India (2021). Annual Report 2020-2021. Retrieved from https://agricoop.nic.in/en/annual-report. Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare, Government of India (2022). agricultural statistics at a glance 2022, 5-year food grain. Retrieved from https://eands.dacnet.nic.in/APY_96_To_06 .htm. Kabra, T. S., Kardile, A. V., Deeksha, M. G., Mane, D. B., Bhosale, P. R., & Belekar, A. M. (2017). Design, development & optimization of a quad-copter for agricultural applications. International Research Journal of Engineering and Technology, 4 (7), 1632-1636. Tiwari, P. S., Singh, K. K., Sahni, R. K., & Kumar, V. (2019). Farm mechanization trends and policy for its promotion in India. Indian Journal of Agricultural Sciences, 89 (10), 1555–62. Yallappa, D., Veerangouda, M., Maski, D., Palled, V., & Bheemanna, M. (2017, October). Development and evaluation of drone mounted sprayer for pesticide applications to crops. Paper presented at the IEEE Global Humanitarian Technology Conference, San Jose, California, USA. Retrieved from https://ieeexplore.ieee.org/abstract/document/8239330.

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Chapter 10 Application of Modernised Irrigation Techniques Gottam Kishore,1 Rooha Blessy2 and S. C. Haokip3

1,2&3

Department of Soil and Water Conservation Engineering, Central Institute of Agricultural Engineering 462038, Madhya Pradesh

Abstract Modern irrigation technologies have greatly advanced agricultural practices by improving water efficiency, crop yield and sustainability. These technologies encompass a range of methods and tools designed to deliver water precisely to crops while minimizing waste. Modern irrigation methods utilize cloudautomated and timed sprinkler systems, drip systems and subsurface water lines. Using precise and high-resolution ET, weather data and smart irrigation controllers can create specific schedules to maintain landscape health, irrigating when necessary based on daily, site-specific runtime adjustments. Recent technologies are GSM, Wireless sensor networks, ARM, Arduino and IoT coupled with solar energy. Various sensors monitor parameters like humidity, soil moisture, temperature, water level in the tank, etc. Websites and Android applications are developed to enable remote monitoring and controlling of irrigation. Introduction The Indian economy is the largest developing economy in the present world. The agriculture sector makes the most significant contribution to the Indian economy. To utilize the maximum manpower and obtain the maximum profit, we need to develop various advanced engineering techniques in agriculture. Hence, we must follow efficient water management practices for irrigation systems. Water quantity (moisture) in the soil should be ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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adequately maintained to obtain a good harvest, which is the source of different types of nutrients. The agriculture sector in India faces failure due to natural phenomena like drought. Due to this reason, the groundwater level is decreasing drastically. So, it's high time to utilize water wisely and precisely. This has increased the demand for developing advanced and efficient technologies to use renewable sources in the best way possible. The process of artificially watering land that does not receive enough rainfall is known as irrigation. It is also the artificial water applied to the soil through tubes, pumps and sprayers. Modern irrigation methods utilize cloud-automated and timed sprinkler systems, drip systems and subsurface water lines. Using precise and high-resolution ET, weather data and smart irrigation controllers can create specific schedules to maintain landscape health, irrigating when necessary based on daily, sitespecific runtime adjustments. Irrigation is frequently used in areas where rainfall is unreliable or dry spells are expected. Water is evenly dispersed across the entire field using various irrigation techniques. The source of irrigation water includes groundwater from springs or wells, surface water through rivers, lakes, or reservoirs and even treated wastewater or desalinated water. Importance of Modern Irrigation Techniques Now that we know about modern irrigation methods, we should also better understand why proper, innovative irrigation techniques are so important. Some of the top reasons we should invest in modern irrigation techniques include: a. Ability to grow several crops or plants and enhance productivity b. Proper irrigation ensures healthy plants c. Irrigation saves the crop from drought or insufficient rainfall Modern irrigation techniques are impressive and necessary for any residential or commercial property. Modernized irrigation techniques have significantly transformed agricultural practices ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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by improving water efficiency, crop yields and resource sustainability. Some of the key applications of updated irrigation techniques include: Sprinkler System It is an easy and simple irrigation application method. Irrigation is given to the crop by spraying water in droplets, like rain, through pipes. There is very little water loss in the sprinkler irrigation system, but the waterlogging problem needs to be addressed. The groundwater balance is also maintained. Sprinkler irrigation is adopted in areas with less water availability and undulating topography. The main pipeline is laid in the field. They are joined to perpendicular channels with rotating nozzles. Water from the tube wells is passed through these pipelines under pressure, which escapes from the rotating nozzles and water is sprinkled on the crops. Sprinkler irrigation is primarily practiced in high temperatures and high humidity areas. The continuous sprinkling of water improves the physical conditions and composition of the soil. The key advantages are: Beneficial for sandy soil and undulating surface Protects the crop from extreme frost or temperature Fertilizer and insecticide can be applied through a sprinkler system d. Helps with soil conservation e. Increases soil productivity f. The physical condition and composition of the soil are maintained. a. b. c.

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Fig.1. Sprinkler layout and its components (Ref. SSWM.info.)

Fig.2 Sprinkler irrigation system in the field (Ref. Daily civil) Drip System This irrigation method is gaining popularity in water scarcity regions. Narrow pipes called laterals punched with small holes to fix the emitters are laid on the field. Water flows through the emitter drop by drop at the crop's root zone. The roots absorb the water and supply it to all the parts of the ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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plant. This method has no wastage of water; less water is evaporated, minimizing runoff and wind erosion. The key advantages are: The water reaches directly to the root of the plant. It saves a lot of water, which benefits irrigating large agricultural areas and meets other farm needs. a. b.

There is no chance of weed growth in this method as water is applied only to the root of the plant. d. Increase in crop yield. e. The area near the plant is dry, so bacterial growth is limited. f. High water use efficiency. Subsurface drip System: Plastic drip tubes or trickle emission devices are buried below the soil surface within plant root zones when the water table is deep below the ground surface. This low-pressure irrigation method can reduce water use by 25% compared to above-ground sprinkler irrigation and works well in irregularly shaped fields and on slopes. Some advantages are: c.

Saves water and improves plant health by eliminating surface water runoff and evaporation b. Reduces weed growth and disease infestation c. Helpful in hot, dry, windy climatic areas with limited water supply a.

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Fig. Subsurface drip It is also critical to know how moist or dry the soil is below the ground surface. Here's where another modern irrigation method will save landscaping from being under-watered or overwatered. When buried below the soil surface, the soil moisture sensors will transmit to the Smart irrigation controller how wet or dry the soil is in the root zone and how much water it needs in real time. These sensors can read soil moisture within ±3 per cent of the actual volumetric soil moisture content. Another cloud-based modern irrigation method is mobile apps, which control the smart irrigation system in the palm of your hand from anywhere, literally with a tap of the finger, from timed sprinkler systems to managing your cloud-based smart irrigation system on your mobile app. Automation of irrigation system Today, the IoT (Internet of Things) impacts virtually every industry, including irrigation. Countless types of irrigation systems rely on IoT to collect data and information about the soil and water levels and give updates about the status of the grass and plants in the area. With this smart irrigation, the system will ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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automatically stop or start when moisture levels reach a certain point. Modern irrigation systems can be automated using sensors, weather forecasts and soil moisture data. These systems adjust irrigation schedules based on real-time information, ensuring that crops receive the right amount of water at the right time. This reduces over-irrigation and conserves water resources.

Fig.. Automated irrigation system Smart Irrigation Management Integrated systems use data analytics, machine learning and remote sensing techniques to optimize irrigation practices. These systems analyze soil moisture levels, weather conditions and crop requirements to make precise irrigation decisions, leading to increased crop productivity and reduced water waste.

Fig. Smart Irrigation system (Ref. Renkeer.in) ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Aquaponics and Hydroponics These are soil-less cultivation methods which utilize nutrient-rich water solutions to grow crops. In aquaponics, fish waste provides nutrients to the plant and the water is recirculated between the fish and plant systems. Hydroponics involves delivering nutrients directly to plants in a controlled environment. Both methods use significantly less water compared to conventional soil-based agriculture.

Fig. Hydroponics (Ref. urban agriculture) Central Pivot Irrigation system Irrigation with a pivot point is a technology often employed in large farms. A rotating sprinkler system on a wheeled tower pivots around a central point, which irrigates in a rotating motion. Central pivot irrigation is efficient and provides even coverage over a large area. Before irrigation, the field is correctly levelled with laser-guided machinery, which keeps water from ponding in low-lying regions and ensures even water distribution throughout the field.

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Fig. Center Pivot system Soil Moisture Sensors These sensors assess the moisture content of the soil and give data to farmers, allowing them to precisely determine the irrigation demands of their crops. This prevents both over- and under-watering.

Fig. Soil Moisture sensor

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Weather-based controllers Weather-based controllers modify irrigation schedules based on weather conditions, evapotranspiration rates and projected rainfall, which provides irrigation only when necessary. Conclusion Facts have proved that smart irrigation technology can reduce water waste by 20%-40% compared to traditional irrigation technology. Based on the current regional weather data and soil moisture conditions, it can scientifically judge when to irrigate plants and how much to irrigate. There is flexibility in adjusting the irrigation time and the irrigation place at the crop's root. Reduces water wastage and increases irrigation efficiency while maintaining plant health and quality. In summary, modernized irrigation techniques offer various applications that enhance agricultural efficiency, sustainability, and productivity while conserving water resources and minimizing environmental impact. References BimalMahato, Anish Kumar Yadav, SudilThapa, ―Solar Based Automatic Irrigation Using Soil Moisture Sensor‖, International Research Journal of Engineering and Technology (IRJET), Volume 05, Issue 05, May-2018, p. 1781-1784. N.D. Pergad, Y.P. Patil Assistant Professor, ―GSM based Water Management in Irrigation System Using ARM7‖, International Journal of Science and Research (IJSR). Volume 4 Issue 12, December 2015. Sanjay Kumawat, ApurvaNagare, MayurBhamare, AshwiniKapadnis, ―Sensor Based Automatic Irrigation System and Soil pH Detection using Image Processing‖, International Research Journal of Engineering and

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Technology (IRJET), Volume: 04 Issue: 04, 2017, p.36733675 Tran AnhKhoa, Mai Minh Man Tan-Y Nguyen, VanDung Nguyen and Nguyen Hoang, ―Smart Agriculture Using IoT Multi-Sensors: A Novel Watering Management System‖, Journal of Sensor and Actuator Networks, MDPI, 2019, 8, 45; doi:10.3390/jsan8030045, p. 1-22. V. Ramachandran, R. Ramalakshmi, and SeshadhriSrinivasan, ―An Automated Irrigation System for Smart Agriculture Using the Internet of Things‖, 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Singapore, November 18-21, 2018. Veeramma Yatnalli; Shivaleelavathi B. G; Saroja S Bhusare; Sheetal C; Reshma B; Swetha Mand Yashaswini H N. A Review on Modern Irrigation Technologies for Water Management.International Journal for Modern Trends in Science and Technology 2021, 7, 0707039, pp.150-159 (8)

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Chapter 11 Functional dynamics of the microbiome through Metagenomics and Transcriptomics approach Shanu Ph.D. Scholar, Amity University, Manesar, Haryana, India.

ABSTRACT The world around us is full of microbes affecting our lifestyle directly or indirectly. The same case is studied by researchers for crops to assess the microbes present on (epiphytes) and (endophytes) in the plant body. The plantmicrobe relationship is affected by biotic as well as abiotic factors and thus the diversity varies from one location to another. The proper ideal connection between the positive as well as negative impact of an organism on agricultural crops is a vast subject to learn, investigate, and explore. Metagenomics deals with the study of the organism‘s diversity or presence and absence in the case of disease severity. There are different approaches to fulfill the task depending on the experimental design, sample to be studied as well as the objective of the study completed by the intense role of highly advanced bioinformatics tool required for the analysis. In transcriptomics, the major role is to identify the genes regulated in the sample system, which can be a host or the organisms present on the host depending on the type of interaction to be studied. Keywords: Microbiome, Metagenomics, Transcriptomics, ClC, RnaSeq, Plant-microbe interactions & Bioinformatics INTRODUCTION Microbial metagenomics and transcriptomics have emerged as powerful tools for unraveling the functional dynamics ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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of the microbiome. They provide valuable insights into microorganisms' composition, activities, and interactions within their ecological niche. By combining metagenomics and transcriptomics approaches, researchers can comprehensively analyze the microbiome‘s genetic content and understand how it functions in its environment. The study enables the identification and quantification of genes contributed by the microbial community, allowing for a deeper understanding of the functional processes. Moreover, researchers can gain a more holistic understanding of the gut microbiome by integrating metagenomics with other omics techniques such as metabolomics, proteomics, and phenomics. METAGENOMICS Microbial metagenomics is the study of the genomes of a mixed population of microorganisms, including bacteria, viruses, fungi, and other organisms, present in a given environment. There are several methods used in microbial metagenomics, including: 1. Shotgun metagenomics: This method involves randomly sequencing fragments of the environmental DNA (eDNA) from a mixed population of microorganisms and using computational tools to assemble them into contigs (small fragments of contiguous sequences) and annotate the functional element such as genes and metabolic pathway. 2. Targeted metagenomics: This method focuses on specific genes or functional elements of interest within the microbial community to study specific functional pathways or genes involved in important processes such as nutrient cycling, symbiotic interactions, and pathogen response. 3. Single-cell metagenomics: This method involves isolating and sequencing the genomes of individual microbial cells within a community, allowing for a more detailed understanding of the genetic diversity and ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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functional potential of specific microorganisms within the microbiome. 4. Metatransciptomics: Metatranscriptomics, on the other hand, focuses on the study of the expressed genes (transcripts) within a microbial community. 5. Meta proteomics: This method involves identifying and quantifying the proteins produced by the microorganisms in a given environment in order to study their functions and interactions with other organisms and the environment. These methods can be used individually or in combination, depending on the research question and objectives, and can provide valuable insight into the microorganisms present in a given environment and their functional contributions to the overall ecosystem. There are several ways in which plant microbiome can affect the plant's vigor health and functional characteristics: 1. Nutrient accretion: It helps plan to acquire the micro and macronutrients from the environment in soluble form for easy plant intake and utilization like phosphorous, potassium, calcium, iron, etc. 2. Disease resistance: The microbiome can be either epiphytic or endophytic on the plant body through various pathways and can interact with plant pathogens to inhibit its growth and establishment and enhance the plant resistance to it hence protecting the plant from the pathogenesis effect. 3. Stress tolerance: It plays a crucial role in helping the plant to withstand environmental stresses like drought, salinity, temperature fluctuations, and heavy metal toxicity. 4. Allelopathy: The microbiome can also influence allelopathic interactions between plants, where certain microorganisms produce chemicals that can inhibit the ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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growth of nearby plant species and provide a competitive advantage for the host plant. There are several applications of this technique to study plantmicrobe-environment interaction: 1. Plant-microbe interactions: it can be used to study the relationship being the mutualistic relationship, symbiotic association, or pathogenic interactions between the microbes and the plant sample taken under consideration (Kumar et al. 2021) . 2. Plant disease and health: The technique can be used to understand the impact of pathogenesis caused by single or multiple pathogens helpful to study the causal agent as well as to develop disease-resistant crops. 3. Plant growth and productivity: It can imply how the plant is behaving in response to the plant-microbe interactions including the role of the nutrient cycle and the microbes responsible for the carrying out of these biological functions. 4. Agricultural practices: The soil from the agricultural land as well as the rhizosphere soil can be used to analyze the soil health and hence plant health useful to mitigate any forthcoming abiotic or biotic stresses and optimize agricultural practices to improve the yield from the crop and land. 5. Soil health: The soil plays a major role in plant health and its growth balance thus, studying the soil microbiome can help to understand the soil structure and the naturally residing microbes that contribute to nutrient cycling, organic matter decomposition, and overall soil health.

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Fig. 1 The flowchart depicting the generalized workflow of metagenomics. 2.1 Methodology: 1. During preparation for sequencing: eDNA was extracted from the sample and amplified by PCR to enrich fragments that contain the expected 5´ and 3´ adapter sequences using specific primers. 2. The amplified product was checked on 2% agarose gel and gel purified to remove non-specific amplifications. 3. 5 ng of amplified product was used to prepare library which can be done using kits like NEBNext Ultra DNA library preparation kit which was then quantified and qualified. 4. The prepared library is then processed for sequencing through different methods like PacBio or Nanopore in Illumina HiSeq. The bioinformatics analysis is carried out to perform the analysis on the raw reads received from the Illumina in the following sequence: 1. Fastq quality checking: Base quality, base composition, GC content ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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2. Filtering and identification of amplified region from paired-end data 3. Operational Taxonomy Unit (OTUs), Taxonomy classification and Relative abundance a. Identification of OTUs b. Assignment of taxonomy to each OTU c. Identification of read and OTU abundance 4. Alpha diversity with samples and rarefaction curves a. Shannon b. Chao1 c. Observed species 5. Beta diversity between samples and beta diversity plots (only for multiple samples) a. Distance matrix calculation using different approaches b. Principal component analysis 3 TRANSCRIPTOMICS In order to understand the gene-level expression of the plantmicrobe association and to study the effect of their residence on the host plant, the transcriptome is a powerful tool. It is based on the expression analysis of the genes present in the host plant delivering the positive and negative regulation of important genes responsible for biological activities like disease resistance, and growth of the plant stress forbearance power. It was in the 1990s when the term ―transcriptome‖ was first time introduced to the world. From 1995 to the mid-1990s and 2000s, several techniques came to market like one of the earliest sequencing-based transcriptomic methods, Serial Analysis of Gene Expression (SAGE), which works on the principle of Sanger sequencing of concatenated random transcript fragments. Transcripts were quantified by matching the pieces to genes that already exist in the public database. A variant of SAGE using high-throughput sequencing techniques, called digital gene expression analysis, was also briefly used but all of these ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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primitive methods were overshadowed by high throughput sequencing of entire transcripts, which provided information on transcript structure, e.g., splice variants. The dominant techniques which are currently in use by researchers all over the world are microarrays and RNA-Seq. The first technique in publication was Microarrays introduced in 1995, which measures the abundances of transcripts of a defined set via their hybridization to an array of complementary probes. This technology allowed assessing thousands of transcripts together with a vast cut of cost per gene and labor-saving whereas RNA-Seq provided a better and vaster platform where the sequencing of transcript cDNAs was performed and abundance is derived from the number of counts from each transcript. The earliest RNA-Seq work was published in 2006 with 105 transcripts. This study was sufficient enough to quantify relative transcript abundance. RNA-Seq became popular only after 2008 when new Solexa/Illumina technologies (San Diego, CA) allowed 109 transcript sequences to be recorded sufficiently to accurately quantify the entire human transcriptome. Data gathering and generating data on RNA transcripts is carried out via either of the two main principles: sequencing of individual transcripts (ESTs, or RNA-Seq) There are several applications of this technique to study plantmicrobe-environment interaction: 1. RNA-Seq of plants infected with pathogens has become an established method for quantifying gene expression changes, identifying novel virulence factors, predicting resistance, and unveiling host-pathogen immune interactions. A primary aim of this technology is to develop measures for disease management for targeted pathogen and to study the effect of treatment provided to plant and to study its ill or positive traits. 2. Transcriptomics allows for the identification of genes and pathways that respond to and counteract biotic and abiotic environmental stresses. The non-targeted nature of ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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transcriptomics allows for the identification of novel transcriptional networks in complex systems. 3. All transcriptomics techniques have been particularly useful in identifying the functions of genes and identifying those responsible for particular phenotypes. The only demerit in this technique is the identification of a novel gene which is present but is not in the gene annotation file used for mapping the transcripts hence, it is important to use the updated and well managed reference files for carrying out the transcriptomics of the sample. 3.1 RNA-Seq Principles: This technique infers to the combination of a high-throughput sequencing methodology combined with computational methods to analyze and quantify transcripts present in an RNA extract. The RNA amounts used as input for RNA-Seq (nanogram quantity) compared to microarrays (microgram quantity) are much lower, which allowed deep understanding of cellular structures, along with linear amplification of cDNA. The background signal is very low for 100 bp reads in non-repetitive regions and theoretically, there is no upper limit of quantification in RNA-Seq. It may be used to identify genes within a genome or genes which are active at a particular time-point, and read counts can be used to accurately model the relative gene expression level.

Fig. 2 The flowchart depicting the generalized workflow of transcriptomics. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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3.2 Methodology: 1. During preparation for sequencing, cDNA copies of transcripts may be amplified by PCR to enrich for fragments that contain the expected 5´ and 3´ adapter sequences. Amplification is also used to allow sequencing of very low-input amounts of RNA, down to as little as 50 picograms in extreme applications. 2. Unique molecular identifiers (UMIs) are short random sequences that are used to individually tag sequence fragments during library preparation so that every tagged fragment is unique. UMIs provide a scale for quantification and are a tool to correct for subsequent amplification which gets introduced during library construction and estimates correctly the initial sample size (Lowe et al. 2017). 3. Once the transcript molecules have been prepared, they can be sequenced in just one direction (single-end) or both directions (paired-end). A single-end sequence is usually faster and easier to produce, than paired-end sequencing, and is sufficient for quantification of gene expression levels and studies. But paired-end sequencing enables one to produces more accurate alignments and/or assemblies, required for gene annotation and transcript isoform discovery. 4. The sensitivity and accuracy of an RNA-Seq experiment are dependent on the number of reads obtained from each sample. A large number of reads are required to successfully cover the length of transcripts, enabling detection of transcripts with low abundance. 5. RNA-Seq studies can produce >109 of short DNA sequences, which must be aligned to reference genomes comprised of millions to billions of base pairs. 6. De novo assembly of reads within a dataset requires the construction of highly complex sequence graphs. ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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7. Data analysis usually requires a combination of bioinformatics software tools that vary according to the experimental design and goals. The process can be divided into the following four steps: quality control, alignment, quantification, and differential expression. Most popular RNA-Seq programs are run from a command-line interface, either in a Unix environment or within the R/Bioconductor statistical environment or an in-silico all-in-one platform like CLC Workbench can also be used to perform the analysis according to the experimental set-up. 1. Quality control: Sequences are read to the accuracy of each base estimated for downstream analyses. Raw data are examined for high-quality using base calls score, guanine-cytosine content, the over-representation of short sequence motifs also called k-mers, and for high read of duplication rate. There are several options for sequence quality analysis, including the most commonly used FastQC and FaQCs software packages. These impurities or abnormalities can be easily removed by trimming or tagged for special treatments during later processes. 2. Alignment: In order to link sequence, read abundance to expression of a particular gene, transcript sequences are aligned to a reference genome, or de novo aligned to one another if no reference is available. The key challenges for alignment software include sufficient speed to permit >109 of short sequences to be aligned in a meaningful timeframe, and correct assignment of reads that map to multiple locations. Software advances have greatly addressed these issues, and increases in sequencing read length are further reducing multi-mapping reads. De novo assembly tool can be used to align reads to construct a full-length transcript sequences without the use of a reference genome. Challenges particular to de novo assembly include larger computational requirements ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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3.

4.

5.

6.

compared to a reference-based transcriptome, additional validation of gene variants or fragments, and additional annotation of assembled transcripts. Annotation-based metrics: They are better assessments of assembly completeness, such as contig reciprocal best-hit count. Once assembled de novo, the assembly can be used as a reference for subsequent sequence alignment methods and quantitative gene expression analysis. Quantitation: Quantitation of sequence alignments may be performed at the gene, exon, or transcript level. The output generally includes a table of read counts for each feature supplied to the software. Differential expression: Once quantitative counts of each transcript are available, differential gene expression is then measured by normalizing, modeling, and statistically analyzing the data. As input, the program takes a table of genes and read counts, but some softwares like such as cuffdiff, accepts binary alignment map format read alignments as input. The final outputs obtained after these analyses are gene lists with pair-wise association for differential expression between treatments. Validation: Transcriptomic analyses may be validated using an independent technique, for example, quantitative PCR (qPCR), which is recognizable and statistically assessable. Gene expression is measured by standardizing both the files for the gene of interest and control genes. The measurement by qPCR is similar to that obtained by RNA-Seq analysis where the value can be calculated for the concentration of a target region through the application of fluorescence in a given sample. Functional validation of up and down-regulated genes is an important task for posttranscriptome planning because these observed gene expression patterns may be linked functionally to a phenotype shown by the organism.

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7. Applications Diagnostics and disease profiling: Transcriptomic strategies have seen broad application across diverse areas of agricultural research, including disease identification, profiling, and studying pathways responsible for disease management. References: Kumar, Mukesh et al. 2021. ―Deciphering Core-Microbiome of Rice Leaf Endosphere: Revelation by Metagenomic and Microbiological Analysis of Aromatic and Non-Aromatic Genotypes Grown in Three Geographical Zones.‖ Microbiological Research 246(October 2020): 126704. https://doi.org/10.1016/j.micres.2021.126704. Lowe, Rohan et al. 2017. ―Transcriptomics Technologies.‖ PLoS Computational Biology 13(5): 1–23.

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Chapter 12 Crop Monitoring Through Modern Agriculture Smita Agrawal1*and Ayushi Trivedi2, Amit Kumar3 R.S. Dangi4 1

Department of Horticulture, College of Agriculture, Khandwa – Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, Madhya Pradesh 2 Department of Agricultural Engineering, College of Agriculture, Khandwa – Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, Madhya Pradesh 3 Department of Horticulture, College of Agriculture, Khandwa – Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, Madhya Pradesh 4 Department of Agronomy, College of Agriculture, Khandwa – Rajmata Vijayaraje Scindia Krishi Vishwa Vidyalaya, Gwalior, Madhya Pradesh * Email ID [email protected]

Abstract Modern agricultural practices have succeeded in replacing and enhancing traditional agricultural practices. The agriculture crop Monitoring System is one such technological innovation. It uses wireless sensor networks and the Internet of Things (IoT). With the help of this technology, agricultural productivity can be raised while unanticipated and unnecessary losses are reduced. Wrong estimates, incorrect calculations, equipment maintenance, inappropriate pest control, overhead costs, etc. are some examples of agriculture losses. However, farmers can gain a lot from ACMS because it offers maximum output, increased quality, predictability, and control, as well as higher sales prices and less pesticide use. The system for crop monitoring consists of a network of wireless sensors. These sensors gather information then, specialist or local farmers analyze this data. The data can be use to make number of inferences about weather patterns, soil ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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fertility, crop quality etc. Automation can also be added to crop monitoring to provide further advantage. Keyword: ACMS (Agriculture crop monitoring system), IoT, Smart Agriculture, Automation, UAV(Unidentified Aerial Vehicle) Introduction Crop monitoring has taken on a new dimension in the changing world of agriculture, one that is driven by data, precision, and technology. The capacity to precisely monitor and assess crop growth at all phases, from planting to final harvest, has emerged as a crucial skill for farmers and agronomists. The World Economic Forum claims that IoT technologies, which are frequently used for crop monitoring, can reduce water use by 20 to 30% and pesticide use by 15 to 20%. In this chapter, we explore the cutting-edge techniques, devices, and technologies that have altered the means by which we cultivate and harvest our crops as we delve into the intriguing world of crop monitoring. modern technology for crop monitoring helps in addressing how real-time data, remote sensing, and advanced analytics are enabling efficient and sustainable agricultural practices in addition to optimizing yields. Crop monitoring is highly valued in agriculture because it enables the early detection of pests and diseases, maximizes resource usage, and supports sustainable practices. Monitoring aids farmers in decision-making, productivity growth, and environmental impact reduction, all of which lead to better economic outcomes and long-term agricultural sustainability. Information from monitoring is crucial for understanding crop health, growth, and environmental factors. Modern crop monitoring significantly relies on technology that provides cutting-edge tools and methodologies for data collection, analysis, and decision-making. Thanks to technological advancements like satellite imaging, drones, IoT sensors, and AIROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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powered analytics, it is now possible to monitor crop health, soil conditions, and weather patterns in real-time. This technology enables farmers to practice precision agriculture, make the most use of their resources, and respond swiftly to emergencies, leading to increased productivity, decreased costs, and more environmentally friendly agricultural practices. Components of Crop Monitoring  Remote sensing and satellite photography are effective techniques for gathering crucial information about the Earth's surface at a distance. Satellites equipped with a variety of sensors take pictures and gather data about the condition of the land, sea, and atmosphere. In agriculture, soil moisture levels are checked, soil pests and diseases are identified, and crop health is monitored using remote sensing and satellite imagery. 

Field IoT (Internet of Things) devices are a collection of networked objects that have internet access and sensors for real-time data collection and exchange. In agricultural, internet of things (IoT) sensors are used to monitor a range of factors, such as crop health, soil moisture, temperature, and humidity. These tools enable farmers to implement precision agriculture practices, make data-driven decisions, optimize resource use, and access critical information remotely, all of which ultimately lead to improved yields, cheaper costs, and more environmentally friendly farming practices.

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Tools that are used for agricultural data collection and its analysis are essential components of modern agriculture. These resources cover a broad range of technological areas, including drones, satellite images, IoT sensors, and analytics powered by AI. They make it possible for farmers to monitor factors including crop health, soil

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quality, and weather patterns. The analyzed data is then put to use to encourage sustainable agriculture practices, implement precision farming methods, and boost crop yields and waste reduction. It also aids in resource allocation optimization. 

Monitoring of the weather and climate requires constant observation, analysis, and research of atmospheric conditions and long-term climatic patterns. This monitoring makes use of satellites, weather stations, and other cutting-edge equipment. To schedule planting, irrigation, and crop management, agriculture needs accurate weather and climatic information. By planning more effectively for extreme events, responding to changing conditions, and improving agricultural methods, farmers can increase crop yields and farm resilience

Field monitoring

Irrigation Automation

Smart Agricultu re

Maintainence Crop Productivity

Crop Selection

Fig -1 Crop Monitoring System

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What is Crop monitoring System? Crop monitoring is the process of routinely examining, evaluating, and gathering information about crops over the course of their growth cycle. It entails systematic and routine crop observation to learn more about their growth, development, and health. Its objectives are to optimize resource use, maximize yields, and make accurate decisions about crop management techniques. Usually, it involves the following actions:  Visual examination  Observations on phenology  Soil Inspection  Weather Observation  Analysis of Sensor-Based Monitoring Data How Crop Monitoring system Works? Various technologies are incorporated in smart crop monitoring s ystem in order to gather, analye, and use data for effective crop m anagement. 1. The Use of Sensors Sensors are initially placed in the field. These sensors are capable of measuring variables like light intensity, temperature, humidity, and soil moisture. In addition, they might have weather sensors to record information on precipitation, wind speed, and solar radiation. To collect accurate data, the sensors are carefully positioned across the area. 2. Data Collection The field data is continuously gathered by the placed sensors. Either wired or wireless connections can be used for this. Since wireless sensors offer versatility and are simple to deploy, they are frequently employed. A central system receives the gathered data and processes and analyzes it further. 3. Transmission of Data The data is sent to a gateway or central server by wireless sensors. Various wireless communication technologies, including cellular ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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networks, Wi-Fi, and specialized radio systems, can be used for this. Depending on how the system is configured, the data transmission may occur in real-time or at predetermined intervals. 4. Data Processing and Storage For subsequent examination, the gathered information is kept in a database. To glean useful insights and patterns from the data, advanced data processing techniques, such as machine learning algorithms and statistical models, are used. Correlations, trends, and anomalies in the agricultural conditions are found by this approach. 5. Alerts and Decision Support The system helps farmers or agronomists make decisions based on the evaluated data. When important events occur, including when soil moisture levels fall below a certain level or when pests or diseases are present, it generates alerts and messages. These messages are sent out via web-based dashboards, smartphone apps, or email/SMS notifications, allowing for quick interventions. 6. Reporting and Visualization Through visualizations and reports, the system provides the analyzed data in a straightforward way for users. To communicate details regarding crop health, growth trends, and environmental factors, graphs, charts, and maps are frequently employed. This makes it simple for farmers to understand the data and make wise judgments. 7. Control and Automation It can be connected with fertigation systems, machinery, or automated irrigation systems. The device can automatically alter irrigation schedules, modify nutrient application rates, or initiate pest management strategies based on the data gathered and analyzed. Through this connectivity, crop management activities may be precisely controlled in real-time using data. By giving farmers accurate and timely information for decision-making, a smart crop monitoring system ultimately aims to maximize ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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resource consumption, increase crop output, and lower costs. Such methods offer more effective and environmentally friendly crop management techniques in contemporary agriculture by utilizing technology. Evolutionary aspect

10000 BC

Agriculture 1.0

Strategic features Usage of simple tools manpower and animal

Upgradation of steam engines 19th century Invention of computers And robotics in Agriculture 20th century

Agriculture 2.0

Agriculture 3.0

IoT, Big data, cloud computing, Ai Today

Agriculture 4.0

Usage of Chemicals Agricultural machineries

Utilization of robots and computer programming

Smart devices, Precision agriculture

Advantage of Effective Crop Monitoring 

Farmers can anticipate future crop yields more precisely for the coming season by using historical data, weather trends, and crop monitoring data. As a result, they can effectively manage their resources and operations, improve planting schedules, and modify cultivation techniques to increase productivity and profitability while lowering the risks related to unpredictable crop results.

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Early pest and disease detection is crucial in agriculture to avoid significant crop damage and the need for excessive pesticide use. Using remote sensing, IoT sensors, and other monitoring technology, farmers can detect the first signs of diseases and infestations. Timely diagnosis also promotes more eco-friendly and sustainable farming practices by enabling the protection of crop health and the optimization of production potential. Effective resource management in agriculture, including the use of fertilizers and water, is made possible by precision agriculture techniques and data-driven decisionmaking. Through the use of IoT sensors, satellite imaging, and AI analytics, farmers can monitor soil moisture levels, crop nutritional requirements, and weather conditions in real time. Utilizing this knowledge enables accurate and strategic resource allocation that maximizes water efficiency and reduces fertilizer waste.

Fig-2 Role of IoT in Crop Production ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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Tools for Crop Monitoring 1. Agricultural Drones- Aerial remote sensing is one of the most crucial technologies for Precision Agriculture (PA) and smart farming. Drones are used in aerial remote sensing, which uses photos captured at various wavelengths and measures vegetation indices to identify the various crop situations. The use of drone monitoring systems by farmers allows them to observe aerial views of the crop. This provides details about the water system, different types of soil, pests, and fungus infestations. Dronecollected crop photos provide information in the infrared and visible spectral ranges. It is possible to extract various elements from these photos, which provides information about the health of plants in a way that is invisible to the naked eye. Another crucial aspect of this technology is its capacity to regularly monitor the yield, such as every week or even every hour. Crop information is frequently available, enabling farmers to make necessary adjustments for better crop management. Farmers monitor their crops on a daily basis for potential dangers like diseases, pests, and poor development. Color and infrared photography taken on various platforms for more than 50 years has been used to track crop progress. Using cutting-edge image data analysis methods, a drone with a camera identifies crops with diseases or deficits. In the agriculture sector, drones are mostly utilized for crop monitoring and field mapping.

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Geo fencing crop spraying

Check crop health

Monitor growth

Soil and field analysis crop monitoring

Fig.3- Drones in Smart Farming Types of Drones A) Fixed wing drone- These UAVs have aero foil-shaped stationary wings that provide the lift required when the vehicle reaches a particular speed. 

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Application in agriculturei) large-scale surveillance ii) monitoring the growth of a lot of crops iii) monitoring of crop health iv)spraying of fertilizer and pesticides Advantagesi) More basic architecture ii) Simplified maintenance procedures iii)Long range and endurance iv) Increased flight speed v)Higher average vi)Extra energy Disadvantagesi) Challenges with accessibility ii)Lower wind resistance

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iii) Challenges with launch iv)Issues with landing v) Difficult to hover vi) Flying is harder v) More education is required vi)High initial and maintenance cost B) Helicopter- It produces lift and thrust by means of a single set of horizontally revolving blades that are mounted to a central mast. A helicopter has the ability to take off and land vertically, fly forward and backward, and hover in one spot. Due of these characteristics, helicopters can be used in crowded and inaccessible regions where fixed-wing aircraft cannot fly. 

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Application in Agriculturei) large-scale pesticide application ii) Estimates of crop height iii) examination of soil and fields iv)classification of crops Advantagesi) Extended flight time ii) Faster speed iii) Sturdy and long-lasting iv) Access to distant locations v) The capacity to take off and land vertically vi) Fly backward and forward while hovering Disadvantagesi) While spraying, some areas of the crop field are not properly covered. ii) heavier iii)Expensive setup iv)High altitude flight v)Noise and trembling

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vi)Issue with stability

C) Multi-copters: Rotorcraft with multiple rows of horizontally revolving blades, typically, four to eight, can lift and drive unmanned aerial vehicles (UAVs). 

Application in Agriculturei) Spot pesticide application ii) small-field surveillance iii) crops' estimated heights iv) examination of soil and fields v)Calculating water stress

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Advantagei) site-specific maintenance ii)The ability to fly at low altitude iii)Increased stability iv) Capable of stable fixed flight v)Ability to fly at low speeds vi)Capability of vertical takeoff and landing

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Disadvantagei) Complex structures ii) Complex maintenance procedure iii) Limited range and duration of flight Crop Monitoring Using Remote Sensing Crop growth is aided by remote sensing, and certain of its characteristics make crop health monitoring possible. The ability to view beyond visible wavelengths into the infrared, where wavelengths are very sensitive to crop vigor, is remote sensing's main advantage. Additionally, the necessary spatial overview of the agricultural field is provided through remote sensing photography. Recent communication advancements enable a farmer to view photographs of fields to manage crops. It can help ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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identify crops that have been harmed by fungal issues, insect, weed, or weather-related damage, as well as crops that have been adversely affected by excessively dry or rainy weather. It is available all during the growing season and can be used to not just identify issues but also to evaluate how well a therapy is working. Crop monitoring with the use of satellite imaging A number of advancements in the agriculture sector came as a result of crop monitoring by using satellite imaging. One of the most important data sources for tracking large-scale crop conditions which focuses on vegetation index analysis along with other advancements is gathered from satellite monitoring data. The key benefit of satellite crop monitoring is invisible to the human eye. It primarily assists the grower in making a sound decision regarding feasible options affecting farming operations. Crop development can be monitored using satellite photos to determine how well preventive measures are working. Next, satellite photos are valuable because they serve as early warning signs of crop stress. Early farm yields provide the grower with information about any marketing choices to be made and the resources that should be devoted to those choices. Importance of Satellite crop monitoring technology  Satellite imagery improves strategic fertilization;  Wage costs have been greatly reduced.  To keep an eye on a sizable portion of an agricultural farm, a minimum number of personnel are needed;  Accuracy will increase,  input costs like fuel will go down,  data collecting will be improved for future research and reference. Conclusion Steps towards sustainable agriculture and a more resilient and open food system involves improving smallholder farmers' accessibility and affordability, employing AI and ML applications, use of blockchain for traceability, and monitoring ROLE OF MODERN TECHNOLOGY IN AGRICULTURE

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climate-resilient crops. These techniques and instruments could boost productivity, give farmers more power, promote trust and responsibility, and open the door to a more promising and secure future for farming and food production. References N. Gondchawar and R. S. Kawitkar, ―IoT based smart agriculture,‖ International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 6, pp. 838–842, 2016. N. Suma, S. R. Samson, and S. Saranya, ―IOT based smart agriculture monitoring system,‖ International Journal on Recent and Innovation Trends in computing and communication, vol. 5, no. 2, pp. 177–181, 2017. G. N. Rameshaiah, J. Pallavi, and S. Shabnam, ―Nano fertilizers and nano sensors–an attempt for developing smart agriculture,‖ International Journal of Engineering Research and General Science, vol. 3, no. 1, pp. 314–320, 2015. P. Newell and O. Taylor, ―Contested landscapes: the global political economy of climate-smart agriculture,‖ Journal of Peasant Studies, vol. 45, no. 1, pp. 108–129, 2018. H. Channe, S. Kothari, and D. Kadam, ―Multidisciplinary model for smart agriculture using internet-of-things (IoT), sensors, cloud-computing, mobile-computing & big-data analysis,‖ Int. J. Computer Technology & Applications, vol. 6, no. 3, pp. 374–382, 2015. L. Scherer and P. H. Verburg, ―Mapping and linking supply-and demand-side measures in climate-smart agriculture A review,‖ Agronomy for Sustainable Development, vol. 37, no. 6, pp. 1–17, 2017. J. Liu, Y. Chai, Y. Xiang, X. Zhang, S. Gou, and Y. Liu, ―Clean energy consumption of power systems towards smart agriculture: roadmap, bottlenecks and technologies,‖ CSEE

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