Embedded system for regulating abiotic parameters for
            <i>Capsicum</i>
            cultivation in a polyhouse with comparison to open-field cultivation

Jat, Dilip; Rajwade, Yogesh Anand; Chandel, Narendra Singh; Dubey, Kumkum; Rao, K. V.

doi:10.1080/19315260.2019.1654588

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…The polyhouse was covered with UV stabilized poly film of 200 µm thickness. The other structural features of the polyhouse are given in Jat, Rajwade, et al (2020). Irrigation was applied using inline drippers with a discharge of 2 Lph at an operating pressure of 1–1.5 kg/cm 2 based on crop evapotranspiration.…”

Section: Methodsmentioning

confidence: 99%

“…Recent developments in the domain of automated systems include mechatronics, robotics and decision support systems which ensure utmost precision in polyhouse operations like weeding, spraying, harvesting, and so on (Chakraborty et al, 2023; Mehta et al, 2020). Polyhouse cultivation is an integral part of precision horticulture which envisages precise control of climate, water, fertilizer, and so on (Reddy, 2016), by technological interventions such as automatic climate control (Jat, Rajwade, et al, 2020), Internet‐of‐Things‐based irrigation, wireless data transfer using ZigBee (Jonnala & Sathyanarayana, 2015), site‐specific input management (Chandel et al, 2016, 2021). The proven benefits of these technologies include offseason cultivation, higher input use efficiency, higher production and productivity, better quality of the agricultural produce; all these lead to an improved livelihood compared to open field cultivation (Bisbis et al, 2018; Chakraborty et al, 2022; Pachiyappan et al, 2022; Rao et al, 2022; Spehia, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Development of an automated mobile robotic sprayer to prevent workers' exposure of agro‐chemicals inside polyhouse

Jat

Dubey

Potdar

et al. 2023

Journal of Field Robotics

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Automated spraying practices are inevitable for modern polyhouse management to attain a broader objective of minimizing human exposure to agrochemicals. In the present study, an automated mobile robotic sprayer (AMRS) was developed to combat the increased human intervention and safeguard agricultural workers from potential health hazards. The system mainly comprises embedded sensors (ultrasonic, proximity, XBee) and controllers (Arduino, PLC). The controller drives the system on a piping track between the rows as well as on the head space achieving end-to-end automation for spraying operations. The system performance was evaluated on the tomato crop with respect to the physiological traits, yield and economics. Additionally, the study leveraged response surface methodology to optimize forward speed, spray distance, and working pressure of AMRS on the responses, droplet density, coverage, volume mean diameter (VMD) and application rate. Optimization of forward speed (0.79 km/h), spray distance (250 mm) and working pressure (0.40 MPa) resulted in 90.7 droplets/cm 2 droplet density, 47.1% coverage, 170.2 μm VMD and 86.0 mL/m 2 of application rate. Ergonomic aspects of AMRS were assessed by the parameters, human exposure, discomfort and postural assessment with respect to knapsack sprayer. The working heart rate of 103 beats/ min, work pulse of 12 beats/min, oxygen consumption rate of 916 mL/min and energy expenditure rate of 18.7 kJ/min recorded during the ergonomic evaluation of the AMRS were 25%, 75%, 42%, and 41% lower compared to manual spraying, respectively. Moreover, three-six times higher work pulse, cardiac cost, body part discomfort score and overall discomfort rating were observed which indicated more drudgery involved in manual spraying. State-of-the-art system developed for polyhouses would minimize the drudgery and health hazards, significantly. The system's resilience and effectiveness pave the way for its wider deployment where the use of agrochemicals are prevalent, particularly in small-size polyhouses.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of an automated mobile robotic sprayer to prevent workers' exposure of agro‐chemicals inside polyhouse

Jat

Dubey

Potdar

et al. 2023

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…To solve these issues, some modern farming techniques are being used which include protected cultivation and recently developed soilless cultivation such as aeroponic and hydroponic system. Polyhouse is a protected cultivation structure which provides an opportunity to grow crop throughout the year [1]. Polyhouse cultivation is a very complex, non-linear and thermodynamic system.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Deep Learning based MODWT-LSTM and Machine Learning Model to Predict Environmental Parameters of Polyhouse

Dubey

Jain²,

Sharma

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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The accurate prediction of environmental parameters inside the polyhouse is prerequisite for reducing the potential yield and financial losses which might occur due to variations in weather conditions. A precise predictive model to forecast the internal climate of the polyhouse several hours beforehand on the basis of present and past weather conditions is needed for an hour. In this study, a new hybrid approach has been proposed which coupled artificial intelligence based deep learning model (Long-Short Term Memory (LSTM)) with Maximum Overlap Discrete Wavelet Transform (MODWT) to forecast the polyhouse environment. The wavelet transform method improved 10 to 20% accuracy of deep leaning model (LSTM) by filtering input data and extracting significant information. A comparative performance analysis of three machine learning models (MLP, SVM, GPR) with deep learning model (Long Short Term Memory (LSTM)) have been evaluated for predicting the temperature and relative humidity of polyhouse. This study also explores the ability of newly proposed hybrid model MODWT-LSTM and compares accuracy with LSTM. The findings of this study shows that MODWT-LSTM performs better than other models for predicting temperature (R 2 =0.95, RMSE=0.64°C) and relative humidity (R 2 =0.97, RMSE=2.43%) of polyhouse. The outcome of proposed work also states that deep learning can be applied more precisely for monitoring and controlling the environment of polyhouse.

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Deep convolutional neural network models for weed detection in polyhouse grown bell peppers

Subeesh

Bhole

Singh

et al. 2022

Artificial Intelligence in Agriculture

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Embedded system for regulating abiotic parameters for Capsicum cultivation in a polyhouse with comparison to open-field cultivation

Cited by 6 publications

References 12 publications

Development of an automated mobile robotic sprayer to prevent workers' exposure of agro‐chemicals inside polyhouse

Development of an automated mobile robotic sprayer to prevent workers' exposure of agro‐chemicals inside polyhouse

A Comparative Study of Deep Learning based MODWT-LSTM and Machine Learning Model to Predict Environmental Parameters of Polyhouse

Deep convolutional neural network models for weed detection in polyhouse grown bell peppers

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