Overview of emerging technologies in sprinkler irrigation to optimize crop production

Li, Hong; Issaka, Zakaria; Jiang, Yue; Tang, Pan; Chen, Chao

doi:10.25165/j.ijabe.20191203.4310

Cited by 32 publications

(8 citation statements)

References 19 publications

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“…Furthermore, the precise application of fertilizers through point placement reduces fertilizer requirements and enhances FUE and energetics by maximising crop nutrient uptake ( Shafagh et al, 2008 ; Nayak et al., 2019 ; Gebre and Earl, 2021 ). Studies have reported that sprinkler irrigation will enhance the WUE of the crops by 20%–30% and saves water by 30%–45% ( Zhao et al, 2009 ; Li et al., 2019 ; Solgi et al., 2022 ). Nevertheless, most of the studies focus on yield and soil fertility status wherein there is not much focus on basic physiological relationships, which finally defines the yield of crop.…”

Section: Introductionmentioning

confidence: 99%

Sensor-based precision nutrient and irrigation management enhances the physiological performance, water productivity, and yield of soybean under system of crop intensification

Sachin,

Dass,

Dhar

et al. 2023

Front. Plant Sci.

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Sensor-based decision tools provide a quick assessment of nutritional and physiological health status of crop, thereby enhancing the crop productivity. Therefore, a 2-year field study was undertaken with precision nutrient and irrigation management under system of crop intensification (SCI) to understand the applicability of sensor-based decision tools in improving the physiological performance, water productivity, and seed yield of soybean crop. The experiment consisted of three irrigation regimes [I1: standard flood irrigation at 50% depletion of available soil moisture (DASM) (FI), I2: sprinkler irrigation at 80% ETC (crop evapo-transpiration) (Spr 80% ETC), and I3: sprinkler irrigation at 60% ETC (Spr 60% ETC)] assigned in main plots, with five precision nutrient management (PNM) practices{PNM1-[SCI protocol], PNM2-[RDF, recommended dose of fertilizer: basal dose incorporated (50% N, full dose of P and K)], PNM3-[RDF: basal dose point placement (BDP) (50% N, full dose of P and K)], PNM4-[75% RDF: BDP (50% N, full dose of P and K)] and PNM5-[50% RDF: BDP (50% N, full P and K)]} assigned in sub-plots using a split-plot design with three replications. The remaining 50% N was top-dressed through SPAD assistance for all the PNM practices. Results showed that the adoption of Spr 80% ETC resulted in an increment of 25.6%, 17.6%, 35.4%, and 17.5% in net-photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), and intercellular CO2 concentration (Ci), respectively, over FI. Among PNM plots, adoption of PNM3 resulted in a significant (p=0.05) improvement in photosynthetic characters like Pn (15.69 µ mol CO2 m−2 s−1), Tr (7.03 m mol H2O m−2 s−1), Gs (0.175 µmol CO2 mol−1 year−1), and Ci (271.7 mol H2O m2 s−1). Enhancement in SPAD (27% and 30%) and normalized difference vegetation index (NDVI) (42% and 52%) values were observed with nitrogen (N) top dressing through SPAD-guided nutrient management, helped enhance crop growth indices, coupled with better dry matter partitioning and interception of sunlight. Canopy temperature depression (CTD) in soybean reduced by 3.09–4.66°C due to adoption of sprinkler irrigation. Likewise, Spr 60% ETc recorded highest irrigation water productivity (1.08 kg ha−1 m−3). However, economic water productivity (27.5 INR ha−1 m−3) and water-use efficiency (7.6 kg ha−1 mm−1 day−1) of soybean got enhanced under Spr 80% ETc over conventional cultivation. Multiple correlation and PCA showed a positive correlation between physiological, growth, and yield parameters of soybean. Concurrently, the adoption of Spr 80% ETC with PNM3 recorded significantly higher grain yield (2.63 t ha−1) and biological yield (8.37 t ha−1) over other combinations. Thus, the performance of SCI protocols under sprinkler irrigation was found to be superior over conventional practices. Hence, integrating SCI with sensor-based precision nutrient and irrigation management could be a viable option for enhancing the crop productivity and enhance the resource-use efficiency in soybean under similar agro-ecological regions.

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Section: Introductionmentioning

confidence: 99%

Sensor-based precision nutrient and irrigation management enhances the physiological performance, water productivity, and yield of soybean under system of crop intensification

Sachin,

Dass,

Dhar

et al. 2023

Front. Plant Sci.

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“…With the automated technology of irrigation, human intervention can be reduced [75,76]. Some advantages of this innovation can be discussed as follows.…”

Section: Advantages Of Automated Sprinkler Irrigationmentioning

confidence: 99%

Review of Sensor Network-Based Irrigation Systems Using IoT and Remote Sensing

Awais

et al. 2020

Advances in Meteorology

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The motivation for this review paper came from the developing countries where the economy is mostly dependent on agriculture and climate conditions. Based on current conditions and historical records, profitability in production farming depends on making a right and timely operational decision. Precision farming is a systematic program designed to maximize the productivity of agriculture by carefully tailoring the soil and crop management to meet the specific requirements in each field while preserving environmental quality. This review paper highlights the development of an automated irrigation system with portable wireless sensor networks and decision support methods to remotely measure the environmental parameters in an agriculture field. Radio satellite, mobile phones, sensors, internet-based communication, and microcontroller capture the ecological parameters such as soil moisture, temperature, humidity, and light intensity. The knowledge gained from the sensors is transferred directly to the cloud server by using IoT technology. Users from anywhere in the world can display them through an internet-enabled device. Development of sensor-based application in modern agriculture makes it cost-effective and potentially productive and increases the efficiency through precision agriculture farming. Different limitations have been reported in the previously reviewed publications like the shortage of power in the field that can be solved by using a solar panel that recharges the battery at the same time using electricity. Bluetooth application in the agriculture sector is mainly improved by design system optimization. Problems related to transmission and radio range frequency can be solved by using a power class upgraded antenna.

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“…It poses challenges to most large-scale overhead phenotype platforms. Consequently, our primary focus lies in the development of compact, agile, and side-view-capable phenotyping robots as the optimal solution [5,[23][24][25] . In order to better reduce the size of the robot and reduce the cost of experiment time, we should also choose efficient computer vision models to match smaller edge devices.…”

Section: Introductionmentioning

confidence: 99%

Method for the real-time detection of tomato ripeness using a phenotype robot and RP-YolactEdge

Yuanqiao,

Wenbo,

Chuanyu

et al. 2024

International Journal of Agricultural and Biological Engineering

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In order to address the challenge of non-destructive detection of tomato fruit ripeness in controlled environments, this study proposed a real-time instance segmentation method based on the edge device. This method combined the principles of phenotype robots and machine vision based on deep learning. A compact and remotely controllable phenotype detection robot was employed to acquire precise data on tomato ripeness. The video data were then processed by using an efficient backbone and the FeatFlowNet structure for feature extraction and analysis of key-frame to non-key-frame mapping from video data. To enhance the diversity of training datasets and the generalization of the model, an innovative approach was chosen by using random enhancement techniques. Besides, the PolyLoss optimization technique was applied to further improve the accuracy of the ripeness multi-class detection tasks. Through validation, the method of this study achieved real-time processing speeds of 90.1 fps (RTX 3070Ti) and 65.5 fps (RTX 2060 S), with an average detection accuracy of 97% compared to manually measured results. This is more accurate and efficient than other instance segmentation models according to actual testing in a greenhouse. Therefore, the results of this research can be deployed in edge devices and provide technical support for unmanned greenhouse monitoring devices or fruit-picking robots in facility environments.

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Overview of emerging technologies in sprinkler irrigation to optimize crop production

Cited by 32 publications

References 19 publications

Sensor-based precision nutrient and irrigation management enhances the physiological performance, water productivity, and yield of soybean under system of crop intensification

Sensor-based precision nutrient and irrigation management enhances the physiological performance, water productivity, and yield of soybean under system of crop intensification

Review of Sensor Network-Based Irrigation Systems Using IoT and Remote Sensing

Method for the real-time detection of tomato ripeness using a phenotype robot and RP-YolactEdge

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