Comprehensive Measurement and Evaluation of Modern Paddy Cultivation with a Hydroganics System Under Different Nutrient Regimes Using WSN and Ground-based Remote Sensing

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Journal Pre-proofs Comprehensive Measurement and Evaluation of Modern Paddy Cultivation with a Hydroganics System Under Different Nutrient Regimes Using WSN and Ground-based Remote Sensing Bayu Taruna Widjaja Putra, Wahyu Nurkholis Hadi Syahputra, Rusdiamin, Indarto, Khairul Anam, Tio Darmawan, Bambang Marhaenanto PII: DOI: Reference:

S0263-2241(21)00410-3 https://doi.org/10.1016/j.measurement.2021.109420 MEASUR 109420

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Measurement

Received Date: Revised Date: Accepted Date:

16 January 2021 31 March 2021 10 April 2021

Please cite this article as: B. Taruna Widjaja Putra, W. Nurkholis Hadi Syahputra, Rusdiamin, Indarto, K. Anam, T. Darmawan, B. Marhaenanto, Comprehensive Measurement and Evaluation of Modern Paddy Cultivation with a Hydroganics System Under Different Nutrient Regimes Using WSN and Ground-based Remote Sensing, Measurement (2021), doi: https://doi.org/10.1016/j.measurement.2021.109420

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Comprehensive Measurement and Evaluation of Modern Paddy Cultivation with a Hydroganics System Under Different Nutrient Regimes Using WSN and Groundbased Remote Sensing Bayu Taruna Widjaja Putraa,*, Wahyu Nurkholis Hadi Syahputraa, Rusdiamina, Indartoa, Khairul Anamb, Tio Darmawanc, Bambang Marhaenantoa a) Laboratory of Precision Agriculture and Geo-informatics, Faculty of Agricultural Technology, Jember University, Jember 68121, East Java, Indonesia b) Department of Electrical Engineering, Jember University c) Department of Computer science, Jember University *Corresponding author: [email protected] Abstract Several studies have corroborated the potential applications of advanced technologies such as wireless sensor networks (WSNs) and remote sensing to cultivation, especially in open fields. However, there are limited studies related to plant properties and yield monitoring, as well as to comprehensive measurements attainment when using a modern cultivation method, such as hydroganics. The hydroganics method utilizes a combination of hydroponic and organic matter as a planting medium for paddies. In this study, we evaluated the use of hydroganics on paddies by applying a smart farming system incorporated with advanced technologies that include WSN and ground-based remote sensing. These technologies were developed for comprehensive measurements, such as environmental conditions, plant allometry monitoring, and grain yield under different nutritional treatments and water needs. The imaging and water applications were controlled by the host analysis system on the basis of sensor data. For validation, the plant nitrogen status was analyzed using chemical analysis (the Kjeldahl method) and was compared using a spectrometer and an imaging system. The results showed that data obtained from the imaging system were highly correlated with the allometric parameters, such as plant height, leaf length, width of the leaf, plant canopy, total leaves, and panicles, with R2 values of 0.95, 0.92, 0.93, 0.87, 0.80, and 0.67, respectively. Additionally, water demands, nutrient applications, and plant roots were highly correlated with grain yields with R2 values of 0.89, 0.83, and 0.89, respectively. The present study shows that the application of WSN can be used to estimate plant allometry, water requirements, and environmental information that supports decision making in determining nutrient applications and water needs through an automated system. Keywords: paddy, hydroganics, water, nutrients, precision agriculture, Internet of Things 2. Introduction Water affects the productivity of staple/cereal crops, such as paddies. Paddy is a plant that has a water consumption rate that is two to three times higher than that of other cereal crops [1]. Inadequate water supply which may be due to drought is a major problem in paddy cultivation. Water loss can be caused by several factors, including evaporation, transpiration, evapotranspiration, runoff, and percolation [2]. Additionally, uncertain changes in climate conditions can affect the availability of water for irrigation, which can impact paddy cropping patterns. Efficient water management can minimize water loss. Consequently, irrigation/water efficiency is an essential factor in crop productivity and water 1

management [3]. Measuring the efficiency of irrigation can provide a basis for improving the management of water resources [4]. Several technologies, such as hydroponics, enable water efficiency. Hydroponic technology allows for the efficient use of water because the used water will be circulated for re-use, and the technology can be applied to limited land [5,6]. Additionally, a new age method called hydroganics can be used for paddy cultivation. Hydroganics is a method for growing crops involving hydroponics and organic matter, which can be obtained from soil or nutrient-rich aquaculture water, which can also be utilized as a planting medium for paddies. Besides water, crops rely upon nutrients (macronutrients and micronutrients) for growth. Proper nutrient implementation can increase the productivity of paddy crops. Macronutrients include nitrogen (N), phosphorus (P), and potassium (K), which are required in different concentrations at each phase of growth. However, N has a more dominant influence on the growth and development of rice plants, especially during the vegetative growth phase. The lack of N during the vegetative phase causes leaves to turn yellowish. However, excessive application of N fertilizer is a common problem in the field. The excessive use of N fertilizer can inhibit the formation of flowers and grains, which can reduce crop productivity. In developing countries, fertilizer applications on paddy crops are usually conducted by spraying above the crops using a manual sprayer or broadcaster. These methods can reduce the efficiency of fertilizer use. The N element in crops can be adjusted to meet the optimal nutritional requirements according to the type of plant commodity and plant stages [7]. Typically, the N content is measured by laboratory analysis using the Kjeldahl method. However, this method can be both expensive and lengthy. Several technologies, such as geographic information system, global positioning systems, remote sensing (RS), cloud computing, big data, internet of things (IoT), wireless sensor networks (WSN), computer vision (CV), and unmanned aerial vehicles have become increasingly popular in agriculture. In gathering agronomic data on plant conditions, automation becomes crucial for effective and efficient data collection and recording. Data collected via WSN can be used for analysis and decision making. Several studies on WSN implementation have been conducted [8] with a specific focus on using WSN to prevent crop damage caused by wild animals. [9] implemented a WSN for irrigation system management by implementing RFID and QR codes integrated with sensors to collect information related to irrigation. Presently, WSN technology has been applied in various agricultural areas, such as irrigation management systems, agricultural system monitoring, and pest predictions [10]. WSN comprises two main components, namely sensor nodes and sensors. Sensor nodes are components that enable storage, processing, and communication on technological devices. Similarly, a sensor is a component that can measure or sense phenomena or physical properties in the environment where the sensor is placed [11]. Moreover, to implement a WSN, an adequate network infrastructure is required. Generally, there have been several uses of WSN in agriculture, but it is limited to monitoring local areas. However, for controlling or actuating purposes from the cloud, the implementation may be underrated due to a block by firewall/network address translation. Additionally, limited use of a public internet protocol (IP) on WSN devices can be a reason for being unable to control over the internet. As such, we must obtain knowledge regarding adequate architecture, protocols, and computer networks [12,13]. Conversely, RS and CV technologies are closely related [14]. Both technologies use a camera to capture visual data, such as the greenness of leaves, height, canopy, phenology, and yields of the crops. For continuous crop monitoring through a camera and other sensors for environmental monitoring, it is necessary to develop a proper IoT architecture.

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Furthermore, food security has become a crucial issue in the recent years and is particularly a concern in regions where food scarcity is currently a problem. The effective management of resources contributes to optimal output, whereby production is increased while costs are mitigated. To achieve optimal output, cultivation is enhanced with modernized systems that can determine the most effective ratio of nutrients for stimulating plant growth and endurance under harsh conditions. Advanced technologies can evaluate the ratio of water and nutrients at various phases of the cultivation process to determine where improvements should be made. Additionally, it can evaluate the implementation of lowcost methods to determine their efficacy and scalability. Thus, utilizing the low-cost technologies that provide accurate methods for obtaining and analyzing data can assist small- and large-scale farms to develop more effective systems for the production of food. By improving production yields, food security can be achieved even in areas where food scarcity is a critical problem. In several studies, researchers focused on only one plant parameter, such as chlorophyll and nitrogen contents [15,16], plant/tree height [17], plant water requirements [9,18], or evaporation and transpiration [19]. To fill the research gap and to provide a real solution for sustainable agriculture, studies on the use of low-cost technology for comprehensive measurement of plant properties and time series monitoring should be conducted. In this study, multiparameters were measured using advanced technologies to estimate the physical properties of plants, including plant height, canopy, panicle, plant water requirements, canopy area, the relationship between physical parameters and nutritional needs, and the yield. Advanced technologies, namely, RS, IoT, CV, and a high-tech method for growing crops, were integrated, especially for paddy monitoring as part of an automated system. This system comprises several sensors, microcontrollers, microcomputers, a router, cameras, and an internet connection. This paper aims to 1) describe the system, 2) report its performance in terms of water and nitrogen demands at different stages, and 3) evaluate the use of direct-leaf measurements and above-canopy measurements in modern paddy cultivation utilizing hydroganics. 3. Materials and Methods 2.1. Experimental Setup This study was conducted in an experimental field located at Jember University, Indonesia (−8.1617393, 113.7093914) by deploying five hydroganics installations. Each installation measured 1 m × 1 m with a capacity to hold 40 plant pots (Figure 1). The hydroganics substrate was a part of a hydroponic method for using a planting medium in the form of solids as a root growth medium. The substrate used in this study was soil taken from the paddy field, which had been treated through several processes between smoothing and drying to neutralize the remaining nutrients in the soil. The paddy variety used in this study was the Ciherang variety. Since the use of existing organic matter in the soil did not meet the nutritional needs of the paddy throughout all growth stages, the application of a fertilizer treatment was considered. According to [20], urea fertilizer should be administered three times during each planting season to achieve optimum growth. For Ciherang rice, the doses of urea, SP-36, and KCl fertilizer per hectare were 300, 75, and 50 kg, respectively [21]. Since the plot size was 1 m × 1 m, these recommended doses of fertilizer application were divided by 10,000. In this study, the application of urea fertilizer was conducted three times. Several treatments were carried out for each plot with different doses of fertilizer. Table 1 presents several treatments utilizing fertilizers.

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Table 1. Experimental setup of fertilizer application in plots Plot no.

Treatments of applied urea

Urea (g) (46% N)

SP-36 (g)

KCl (g)

DAT 7 (25 % from treatment)

DAT 30 (50% from treatment)

DAT 40 (25% from treatment)

7 (25% from treatment)

DAT 30 (50% from treatment)

DAT 40 (25% from treatment)

DAT 7 (25% from treatment)

DAT 30 (50% from treatment)

Water (ml/pot) DAT 40 (25% from treatment)

1

0%

0 (0 N)

0 (0 N)

0 (0 N)

7.5

0

0

2.5

0

2.5

50

2

40%

3 (1.38 N)

6 (2.76 N)

3 (1.38 N)

7.5

0

0

2.5

0

2.5

100

3

70%

5.25 (2.4 N)

10.5 (4.8 N)

5.25 (2.4 N)

7.5

0

0

2.5

0

2.5

150

4

100%

7.5 (3.5 N)

15 (6.9 N)

7.5 (3.5 N)

7.5

0

0

2.5

0

2.5

200

5

150%

11.25 (5.2 N)

22.5 (10.4)

11.25 (5.2 N)

7.5

0

0

2.5

0

2.5

250

Water treatment administered to the plant is based on the field capacity. In this study, the field capacity of the medium used in this study was 200 ml. The distributions of water to each pot in plots 1– 5 were 50, 100,150, 200, and 250 ml/day, respectively; the total water quantities given to each plot were 2,000, 4,000, 6,000, 8,000, and 10,000 ml/day, respectively. However, not all of the water given to the plants was absorbed by the roots; some was channeled to the reservoir through pipes to be channeled back into each pot. Water was distributed automatically to each pot in the morning and evening. The daily water consumption needed by plants was calculated on the basis of the difference between the initial volume of water given and the volume of water stored in the bucket after circulation. To determine the water demands of the plant when using the hydroganics system, other parameters were considered, including evaporation and evapotranspiration. Evaporation is a factor that influences the evapotranspiration process in plants. The value of the evaporation rate was automatically obtained by calculating the difference in the volume of water collected in the container for 1 day (Figure 1). The amount of water for each container was determined based on the water treatment used for each plot. The amounts of water for containers 1–5 were 50, 100, 150, 200, and 250 ml/day, respectively.

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Figure 1. Experimental setup of each plot: (a) construction and placement of a pot and soil moisture sensor, (b) experimental setup of each plot, and (c) a custom platform of evaporation measurement 2.2. IoT System Preparation We introduced six major subsystems of IoT deployment, namely, environmental monitoring, plant monitoring, an imaging system, actuation system, networking, and host analysis system (HAS). This system was an extension of what was used in the study conducted by [22], which included three major subsystems. Several parameters (e.g., temperature, humidity, light intensity, and rain intensity) were measured and served as environmental monitoring. For crop monitoring, we used soil moisture sensors, ultrasound sensors, and a flow meter, which were placed in the pots, as well as a water and nutrient tank and an outlet for the water reservoir. An RGB imaging system was used to capture each plot and was placed at a particular height above the crop canopy. Actuation systems were deployed in the irrigation system and at the outlet of the water reservoir; these actuators corresponded to the soil moisture sensor and ultrasound sensor, respectively. Actuators were controlled by HAS on the basis of an analysis of the collected sensors. The network used in this study was a low-end network peripheral that had multiple features (i.e., routing, firewall, DHCP, and virtual private network [VPN]). The communication between sensor/actuator nodes and HAS was facilitated by a VPN connection with a sensor/actuator node serving as the VPN client and HAS functioning as the VPN server. HAS comprised web services, web applications, and a database, which were used to analyze the information obtained from sensor nodes and for sending response signals back to the decision actuators. HAS had a public IP with two functions. First, it served as a VPN server and received the VPN dial from the client node. Second, it was vital for establishing a connection for public users. Additionally, authorized users could control the sensor/actuator nodes. Hence, within this architecture, the system was easily scaleable. Figure 2 shows the overall schematic of the IoT-based monitoring and control system. 5

Figure 2. Internet of Things system architecture and preparation

2.3. Measurement Methods In this study, parameters related to plants and the environment were measured using several methods, namely, nondestructive and destructive measurements (Figure 3). The nondestructive measurement was divided into two systems. The first system worked by using IoT-based sensors, which were used to measure the soil moisture, temperature, humidity, water level, water flow, weight, light intensity, and vegetation indices (from the above-canopy measurement); these sensors had been calibrated before use. The second system, which was separated from the IoT-based system, operated a handheld spectrometer to obtain direct-leaf measurements. For destructive measurement, we used the Kjeldahl method to determine the nitrogen status of plants.

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Figure 3. Ground-based remote sensing: (a) above-canopy measurements obtained using a camera, (b) direct-leaf measurement obtained using a handheld spectrometer, (c) chemical analysis using the Kjeldahl methods to determine the nitrogen contents 2.3.1. Above-canopy Measurement The paddies in each plot were captured using a custom pole placed 2m above the plot. This measurement method was conducted at 1 DAT, 18 DAT, 32 DAT, 46 DAT, 60 DAT, 74 DAT, and 81 DAT by using a custom system that incorporated an RGB camera (Canon IXUS 160) and a light intensity sensor. The light sensor was used as a trigger for capturing the plot. The plots were captured automatically at a particular light intensity (500 lux). 2.3.2. Direct-leaf Measurement An optical measurement was conducted to obtain the reflectance value of the paddy leaves [23]. The third leaf of each paddy in each pot for a total of 1,240 leaves, which were measured using a handheld spectrometer (ASEQ Instrument) with halogen as the light source (corresponding to Fig. 3b). The reflectance value of 40 leaves per plot was averaged to obtain the reflectance value of the plot. Additionally, before obtaining a direct-leaf measurement, we used the 99% Spectralon diffuse reflectance standard. Spectralon is a reference white panel that is used to calibrate the spectrum and standardize the reflectance values [24,25]. 2.3.3. Vegetation Indices Vegetation index (VI) is a spectral transformation of two or more bands combined mathematically to describe the nature of vegetation (i.e., the greenness level), which enable us to know the spatial and temporal variation of the plant canopy/leaves vegetation. [26]. The selected VIs were used to evaluate the digital number of the captured JPEG image obtained by using an RGB camera and spectral information from the spectrometer. Table 2 shows the detailed information on the VIs obtained from RGB. Table 2. Vegetation indices Vegetation index

Formulas

Instrument

Remark

GMR (blue minus red)

G−R

RGB Camera

Broadband

VARI

(G − R)/(R + G − B)

RGB Camera

Broadband

NDRE (normalized difference red edge) SRRE

(NIR − RE)/(NIR + RE)

Spectrometer

NIR = 800–810 nm and RE = 715–730 nm

NDVI (normalized difference vegetation index) SRVI

(NIR − R)/(NIR + R)

Spectrometer

Narrowband NIR = 770–785 nm and R = 660–675 nm

2.3.4.

Kjeldahl Analysis 7

In this study, we conducted a destructive analysis test using a chemical analysis (Kjeldahl method) on the third leaves, which was collected and measured using a spectrometer. Sampling for analysis was conducted every 2 weeks (five samples from five different plots every 2 weeks). Analysis using the Kjeldahl method was performed on samples taken from 40 leaves in a plot and a leaf taken from the third leaf of each paddy in each pot. Overall, a total of 31 samples were collected during the season, which consisted of 30 samples obtained from plots and another set of samples obtained from the remaining plants, which were ready to be transplanted (1 DAT). 2.3.5. Water Requirements The water requirements were influenced by several factors such as plant type, plant growth stages including transpiration and evaporation. In this study, evaporation measurements were conducted on the basis of water treatment in each plot. The pots set in each plot planted with paddy were used to measure the amount of plant water used and loss due to evapotranspiration. Since we used the closed-loop system of plant cultivation, the water requirements (A) can be determined by measuring the difference between the water given to each plot including the rain (B) and the water stored in a water and nutrient tank (C) on daily basis (A = B - C). 2.4. Statistical Analysis The software Jupyter Notebook was used for data analysis. To monitor the model, the collected data were analyzed using regression analysis to investigate the correlation coefficient (R2) and the root-mean-square error (RMSE). The RMSE was calculated using the following formula: 1 𝑅𝑀𝑆𝐸 = × 𝑛

𝑛

∑(𝑃 ― 𝑂 ) 𝑖

𝑖

2

𝑖=1

Pi and Oi are the predicted and observed values, respectively, and n is the number of samples. 3. Results and Discussion In the experiment, the environmental data and plant properties of each plot were collected and analyzed. The environmental data (temperature, humidity, and soil humidity) were used to estimate the evaporation and evapotranspiration in the paddy. Additionally, plant properties such as plant height, panicles, number of leaves, and greenness level were used to evaluate the impact of different water and nutrient regimes on modern paddy cultivation using hydroganics. 3.1. Direct-leaves and Above-canopy Measurement Continuous monitoring of plant properties, especially leaf greenness, can be used to determine the level of plant health. Study plots 1–5, treated using different N amounts of 0%, 40%, 70%, 100%, and 150%, respectively, were monitored using optical sensors, namely, the spectrometer and RGB camera. A spectrometer was used to estimate the leaf greenness levels, which were correlated with chlorophyll and nitrogen uptake [27,28]. Figure 4 shows that different N treatments can affect the greenness of leaves in plots 1–5, as evidenced by the gradual difference in the reflectance value of each plot, especially at the green and near-infrared areas. According to a study conducted by [28], the higher N content in the leaves provided, the lower reflectance value in the green wavelength area and the higher reflectance value at the near-infrared wavelength area. 8

Figure 4. Averaged spectral information of the third leaf of paddy in different plots at (a) 18 DAT and (b) 46 DAT Compared with using a camera for the above-canopy measurement, the use of a spectrometer for directleaf measurement had a limited ROI, which limited the amount of information that could be extracted. By contrast, for the above-canopy measurements, information on the extracted plant properties included more properties, namely, leave greenness, canopy area, and background availability. Figure 5 shows the condition of the paddies as captured using the RGB camera on different DATs.

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Figure 5. Time series RGB images extraction acquired in different plots The growth of paddies in each plot can be determined by monitoring two parameters, namely, (1) the ratio of the leaf area and the frame to (2) the greenness level, as determined by vegetation indices. In this study, the leaf area in each plot showed an obvious difference (Figure 6). The leaf greenness depended on the vegetation indices used. Two methods of extracting RGB value of captured images to be used as vegetation indices were evaluated in this study. The first method was extracting RGB value of the paddy leaves only in each plot. It showed that the treatment in each plot generated a clear difference when using the VARI index compared with when 10

using the GMR (Figure 7a-b). During the ripening phase (~80 DAT), excessive use of nitrogen (150% N), as shown in plot 5, can cause leaves to have a higher greenness level than those treated with the 100% N (plot 4). According to [29], excessive N application may cause environmental pollution and slow the ripening process. This greenness level was revealed using the VARI index. According to [30], incorporating more than two bands in a VI may yield better results than using only two bands especially for extracting RGB value of the leaves only. The second method was extracting RGB value of the paddy leaves and segmented background in each plot. It showed that extracting RGB value of the frame image incorporated with segmented background provides better estimating phenomenon of each plant stage (Figure 7c-d). Compared with the first method, the GMR provides more gradual index values of each plot than VARI. Based on the study conducted by [31], GMR is highly correlated with the leaf area index and above-ground biomass. For the next analyses, the second method used for estimating plant allometry such as plant height, leaf length, width of the leaf, plant canopy, total leaves, and panicles.

Figure 6. Ratio of plant pixel and frame

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Figure 7. Temporal profile of paddy vegetation indices (GMR and VARI) obtained using a digital camera; (a,b) RGB values were extracted only from the paddy leaves; (c,d) RGB values extracted from the paddy leaves and segmented background.

3.2. Environmental Data Acquisitions Daily environmental data, namely, evaporation, rain intensity, average temperature, and humidity, were collected through IoT. Based on the results, environmental parameters, namely, temperature, humidity, and evaporation, were relatively constant (Figure 8) because the site was located in a tropical area. Therefore, the duration of sunlight exposure was relatively similar each day. Rain also occurs occasionally during the planting period. For the experiment, the plots were placed in an open field to achieve real environmental conditions, including a rainy condition. Rain pouring down on the plants or pots was collected into a water tank to be recirculated into each plant in each plot. Hence, by using a closed system [32,33], no water was lost because of infiltration into the soil.

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Figure 8. Environmental data throughout the whole season 3.3. Morphological Characteristics of Different Treatments Plant growth status can be monitored using the allometric approach to estimate the nitrogen uptake, fertilizer applications, water needs, and biomass [34,35]. In this study, an allometric measurement of rice was conducted throughout the whole season using six different parameters comprising plant height, leaf length, leaf width, canopy width, number of leaves, and number of panicles. Figure 9 shows the characteristics of plant growth in different treatments ranging from 0 to 81 DAT. The different applications of N fertilizer affect rice growth, although the same quantity of P and K fertilizer applications were administered. Most of the parameters gradually increased from plots 1–5 because of different N applications. These morphological characteristics were used as comparative data for evaluating a series of parameters germane to ground-based RS, water needs, and nitrogen status of rice plants in a closed system using hydroganics for the next subsection.

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Figure 9. Plant properties obtained by using manual measurement. 3.4. Correlation between Data Obtained Using Ground-based Remote Sensing and Plant Properties The estimation of canopy cover and plant density for predicting crop yields can be obtained via vegetation index analysis [36]. Plant growth can be described by comparing the pixel ratio between plants and frames within an image. Each pixel contains information related to the image in terms of the color of the RGB component, which can be measured and analyzed using a computer to identify the plant pixels. The pixel ratio is directly proportional to the physical plant growth (Figure 10). A comparison between the frame pixel and plant characteristics, such as plant height, leaf length, leaf width, canopy cover, the total number of leaves and panicles, demonstrated a high correlation with R2 of 0.95, 0.92, 0.93, 0.87, 0.80, and 0.67, respectively. This was in line with the study conducted by [37]. Visually, plant growth can be observed by looking at an increase in the number of plant organs, which occurs simultaneously. In this study, two vegetation indices were compared with the actual value of plant allometric to estimate the physical plant growth. Physical plant growth has a significant effect on the plant canopy cover. The GMR has a high correlation with the physical growth of R2 0.87. The GMR value also increased along with the growth of paddy, which was consistent with the study conducted by [31].

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Figure 10. Data obtained using ground-based remote sensing and plant properties. 15

3.5. Water Needs and Yield Estimation Evaluation Although paddies are not aquatic, they require more water. Their level of water demand is closely related to their growth rate, which is highly correlated with the given amount of N (corresponding to Figures 9 and 10). The water and nutrients provided to paddies were recirculated into the pot. These conditions are optimum for determining the yield due to no loss of water or fertilizer via infiltration. Figure 11 shows the water requirements of plants under different treatments during one growing season. In this study, each plot (plots 1–5) required a water volume of 171.3, 278.8, 356.7, 431.3, and 556.2 L to produce a yield of 70, 122, 370, 553, and 559 g, respectively.

Figure 11. Water needs of different nutrient treatments / growing season For yields in the experimental design, the amount of grain produced was closely related to the amount of water needed for RMSE 108.7 g (Figure 12). Additionally, nutritional factors play an essential role in increasing the amount of grain produced.

Figure 12. Correlation between the yield and water needs

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According to the Indonesian Center for Rice Research [38], the production of 1 kg of unhulled rice requires an average of 1,432 L of water. Additionally, the ratios of productivity and water requirements of paddies using pipe irrigation in conventional and SRI methods are 820g/1,000 L and 1,120g/1,000 L, respectively [39]. Conversely, research on water demands of rice plants has been conducted by the International Rice Research Institute focusing on potted rice plants cultivated in greenhouses. The results showed that rice plants needed 500–1,000 L of water to produce 1 kg of coarse (not milled) rice [40]. 3.6. Correlation Between Nitrogen Status and Vegetation Indices The greenness level of paddy leaves is a parameter often used for determining the nitrogen status of plants [41,42]. In this study, the paddy nitrogen status in the experimental setup was compared with that using the VIs obtained via a spectrometer to evaluate the potential use of VIs using the direct-leaf measurement method. Figure 13 shows the correlation between the N status of paddies using Kjeldahl analysis and nondestructive measurements using VIs. Normalized difference vegetation index (NDVI) generates better correlation estimates than normalized difference red edge (NDRE) in determining the N status of paddies at different stages under treatments with R2 values of 0.66 and 0.46. However, the RMSE obtained by NDRE was relatively better than the NDVI in estimating the N status at 0.58% and 0.69%, respectively. This study is in congruence with a study conducted by Aranguren et al. (2020), which emphasized that the nitrogen status was exponentially correlated with NDVI.

Figure 13. Correlation between N Lab and different vegetation indices 3.7. Evaluation of the Grain Yield Toward the Root Weight and Fertilizer Application Applying appropriate fertilizers to plants can reduce fertilizer costs and minimize the negative environmental impact due to excessive use of fertilizers. Apart from fertilization, the rice variety also affects productivity. According to a study conducted by [44], fertilization management affects the rice grain yield. However, not all N fertilizers can be absorbed by plants, as some fertilizers remain in the soil and gradually disappear through an evaporation process. Based on Table 3, the grain yield in each plot was processed through different fertilization treatments, which were dried under the sun for 2 days. The higher amount of N fertilizer significantly affected the yield.

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Table 3. Yield of several plots Parameter Yield (g) Yield and panicles (g)

Plot 1 70

Plot 2 122

Weight Plot 3 370

140

606

1072

Plot 4 553

Plot 5 559

1525

1790

Roots are part of plant organs that play a role in the absorption of nutrients and water, in plant support, and in plant hormone synthesis. The high rate of photosynthesis by plant shoots will lead to a large amount of photosynthate for the roots [45]. Figure 14 shows the plant root condition of different plot based on the different water and nutrients application. The resultant dry weight of roots was significantly influenced by the volume of water applied. The average dry weights of the roots produced in plots 1–5 were 4, 9.7, 13.2, 14.9, and 22.7 g, respectively.

Figure 14. Plant root condition of different plots The weight of the paddy root was highly correlated with the grain yield. This finding is in line with the study conducted by [46], who found that nitrogen uptake is linearly correlated with grain yield. Figure 15 demonstrates that grain yield is highly correlated with the weight of the paddy root and urea application, as the RMSE was 136.8 and 106.6 g, respectively. Root growth is linear with plant growth, as indicated by the urea application (corresponding to subsection 3.3).

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Figure 15. Correlation between the weight of the paddy root, urea application in the whole season, and grain yield Additionally, root activity in the absorption of nutrients and water from the soil plays an essential role in plant metabolic processes. These two things substantially affect plant productivity. According to a study conducted by [47,48], the root diameter, root length, and root weight are significantly influenced by nutrition and water treatments. An irrigation system using an alternate wetting and drying method can increase the root weight and root length, according to the results of the analysis performed in this study. 4. Conclusion The government attempts to maintain and improve sustainable agriculture and food security by incorporating advanced technologies and modern cultivation methods. Several studies have corroborated the potential use of advanced technologies, such as a WSN and RS, especially in the open field. However, studies related to plant properties and yield monitoring, as well as comprehensive measurements using the closed-loop system, such as hydroganics, are scarce. In the present study, low-cost measurement methods utilizing advanced technologies (sensor networks, CV, and ground-based RS) were developed and these can be used by smallholder farmers or the industry for real-time monitoring and control of the industrial system. A series of measurements was obtained and evaluated using quantitative analysis. Data obtained from the imaging system were highly correlated with allometric parameters, such as the plant height, leaf length, leaf width, plant canopy, and total number of leaves and panicles, with R2 values proven to reach 0.95, 0.92, 0.93, 0.87, 0.80, and 0.67, respectively. Additionally, water needs, nutrients application, and plant roots are highly correlated with grain yields, as indicated by R2 magnitudes of 0.89, 0.83, and 0.89, respectively. The implementation of WSN and ground-based RS can be used as an integrated monitoring and control system as an approach to increase plant productivity. By utilizing closed systems, the monitoring of crops, and yields can be performed on a limited scale to increase the time-and-cost efficiency. Acknowledgment This article is part of the 2020 IsDB Project of Jember University.

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a. b. c. d. e.

Advanced technologies are implemented in the measurement systems The system is applicable for closed-loop cultivation system Periodically monitoring plant allometry and environmental measurements Water used is controlled by the system The developed system can predict the use of the different nutrient application

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Credit Author Statement Bayu Taruna Widjaja Putra: Conceptualization, Resources, Methodology, Software, Investigation, Writing – Review & Editing Wahyu Nurkholis Hadi Syahputra: Investigation Rusdiamin: Investigation Indarto: Visualization, Supervision Khairul Anam: Software Tio Darmawan: Software Bambang Marhaenanto: Visualization, Supervision

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