Design and Implementation of Deep Learning Based Model Predictive Controller to Automatically Adjust Nutrient of Solution for Hydroponic Crop

Smart farming is the future of agriculture sector and brings a new era in agriculture; it enables farmers to increase the production and quality of crops with minimal use of resources. In current scenario land availability decreases enormously, hence soilless hydroponic cultivation is considered as the fastest growing sector of agriculture. However, in hydroponic system it is a very challenging task to manage nutrient for crop. To solve these issues this study was conducted which could control robustly EC and pH of hydroponic solution with the help of deep learning model long short-term memory (LSTM). A model predictive controller (MPC) using LSTM was designed and simulated to control EC and pH in hydroponic farm. The predicted outcome of LSTM was operating time of pH buffer solution pump (Ton_pH) and nutrient solution pump (Ton_EC).  The proposed MPC adjust these operating times to control EC and pH with an RMSE of 0.24 and 0.27s, respectively. Furthermore, proposed system improves the predicting accuracy of Ton_pH and Ton_EC of 77% and 61%, respectively, as compared to fuzzy logic controller. This study provides a smart and efficient way to predict and estimate the optimum value for robustly manage the nutrient as per crop requirements

Artificial Intelligence in Agriculture

Not AvailableHand transplanting of vegetable seedlings is always been a time consuming and labourious activity which often leads to muscular fatigue. Use of hitech instrumentation increased to achieve precision and automation in agricultural operations. At present the transplanting is done manually which accounts for large amount of hand labour and time. To ensure precision and timeliness in operation, an automatic transplanting based on embedded system for use in seedling transplanters was developed. The developed system consists of feed roller, pro-tray belt, a pair of L-shaped rotating fingers, embedded system, DC and stepper motor. The plug seedlings were released into the furrow with use of developed embedded system by actuating DC as well as stepper motor. The performances of the developed system was tested rigorously at four different operating speeds (1.0, 1.5, 2.0 and 2.5 km/h) and three angles of pro-tray feed roller (00, 300, 450) for attaining optimum plant to plant spacing in soil bin. The result indicated that percent transplanting and plant to plant spacing was found optimum at 2.0 km/h forward speed and 300 angle of pro-tray feed roller. The average plant spacing, transplanting efficiency, furrow closer, angle of inclination and miss planting were 600 mm, 91.7%, 90.3%, 18.30 and 2.1%, respectively. The developed system ensures the precision by sigulating the placement of seedlings at optimum spacing for sustainable agriculture production. It also enabled the optimum transplanting rate, the ability to transplant at higher speeds and maintaining proper plant to plant spacing

Automatic Ejection of Plug-type Seedlings using Embedded System for use in Automatic Vegetable Transplanter

This paper presents an automatic ejection mechanism using micro-controller based system for various plug-type vegetables seedlings grown in pro-trays. The developed system consisted of an electro-mechanical unit for actuating metering shaft through stepper motor and feed roller through DC (Direct Current) motor by computer programme. Both the stepper and DC motor were linked to the micro-controller via universal asynchronous receiver-transmitter protocol using an Arduino Uno. Here, computer programme was used to integrate with electro-mechanical unit using drivers which controls the stepper motor as well as DC motor. The main objective of this paper is to provide an automatic ejection mechanism to automate the transplanting operation for plug-type seedlings directly from pro-trays. The mechanism was tested under actual field conditions for various performance parameters like plant spacing, miss planting, transplanting efficiency, effective field capacity, and field transplanting efficiency. Also, the effective field capacity was compared with conventional method of transplanting (manual transplanting). The spacing between consecutive plants was found in the range of 564 mm to 599 mm, and the miss planting was about 4.5 to 5%. Also, the transplanting efficiency and field transplanting efficiency was observed to be 90.0–92.6% and 74.1–75.6%, respectively. The effective field capacity with developed automatic vegetable transplanter was 0.093 ha/h whereas it was 0.027 ha/h with manual transplanting for both type of seedlings. The results indicated that automatic ejection mechanism can be used for automatic planting of plug-type seedlings viz. tomato, eggplant, chilli, etc. as an alternative to hand transplanting by providing a better transplanting efficiency, optimum plant spacing and ensuring timeliness in operation. Additionally, due to automation it will considerably reduce the man power requirement and enhance the productivity as compared to manual transplanting

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Not AvailablePolyhouse cultivation enables year-round production of bell pepper (Capsicum annuum L.). Maintaining a favorable micro-climate inside a polyhouse is mostly through manual operation of environmental control systems in developing countries, and to varying degrees, with automation in developed countries. In this study automation of micro-climate control through sensors and controllers was examined. Soil moisture, relative humidity, and air temperature sensors were installed at different locations inside a polyhouse for the operation of irrigation, foggers, and fan-pad cooling systems. Threshold values of these parameterswere set as input to the programmable logic controller and systems. Bell pepper is an economically important crop rich in nutrients. The efficacy of the use of an automated system to control the growing environment for this crop needs to be clarified. The experiment was conducted using cv. Swarna, grown under open-field and polyhouse culture, at irrigation levels of 80% or 100% of crop evapotranspiration. Type of growing environment affected yield and fruit size, with production in the polyhouse being better, but irrigation level did not. The programmable logic controller-based automation system worked well for micro-climate control leading to 93% and 53% higher yield and fruit weight, respectively, in the polyhouse than open-field cultivation. The programmable logic controller-based automation can aid in maintaining a favorable microclimate inside a greenhouse leading to better bell pepper yield.Not Availabl

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Timely crop water stress detection can help precision irrigation management and minimize yield loss. A two-year study was conducted on non-invasive winter wheat water stress monitoring using state-of-the-art computer vision and thermal-RGB imagery inputs. Field treatment plots were irrigated using two irrigation systems (flood and sprinkler) at four rates (100, 75, 50, and 25% of crop evapotranspiration [ETc]). A total of 3200 images under different treatments were captured at critical growth stages, that is, 20, 35, 70, 95, and 108 days after sowing using a custom-developed thermal-RGB imaging system. Crop and soil response measurements of canopy temperature (Tc), relative water content (RWC), soil moisture content (SMC), and relative humidity (RH) were significantly affected by the irrigation treatments showing the lowest Tc (22.5 ± 2 °C), and highest RWC (90%) and SMC (25.7 ± 2.2%) for 100% ETc, and highest Tc (28 ± 3 °C), and lowest RWC (74%) and SMC (20.5 ± 3.1%) for 25% ETc. The RGB and thermal imagery were then used as inputs to feature-extraction-based deep learning models (AlexNet, GoogLeNet, Inception V3, MobileNet V2, ResNet50) while, RWC, SMC, Tc, and RH were the inputs to function-approximation models (Artificial Neural Network (ANN), Kernel Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Long Short-Term Memory (DL-LSTM)) to classify stressed/non-stressed crops. Among the feature extraction-based models, ResNet50 outperformed other models showing a discriminant accuracy of 96.9% with RGB and 98.4% with thermal imagery inputs. Overall, classification accuracy was higher for thermal imagery compared to RGB imagery inputs. The DL-LSTM had the highest discriminant accuracy of 96.7% and less error among the function approximation-based models for classifying stress/non-stress. The study suggests that computer vision coupled with thermal-RGB imagery can be instrumental in high-throughput mitigation and management of crop water stress

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Not AvailableThe identification of water stress is a major challenge for timely and effective irrigation to ensure global food security and sustainable agriculture. Several direct and indirect methods exist for identification of crop water stress, but they are time consuming, tedious and require highly sophisticated sensors or equipment. Image processing is one of the techniques which can help in the assessment of water stress directly. Machine learning techniques combined with image processing can aid in identifying water stress beyond the limitations of traditional image processing. Deep learning (DL) techniques have gained momentum recently for image classification and the convolutional neural network based on DL is being applied widely. In present study, comparative assessment of three DL models: AlexNet, GoogLeNet and Inception V3 are applied for identification of water stress in maize (Zea mays), okra (Abelmoschus esculentus) and soybean (Glycine max) crops. A total of 1200 digital images were acquired for each crop to form the input dataset for the deep learning models. Among the three models, performance of GoogLeNet was found to be superior with an accuracy of 98.3, 97.5 and 94.1% for maize, okra and soybean, respectively. The onset of convergence in GoogLeNet models commenced after 8 epochs with 22 (maize), 31 (okra) and 15 (soybean) iterations per epoch with error rate of less than 7.5%.Not Availabl

Not Available

Not AvailableThe identification of water stress is a major challenge for timely and effective irrigation to ensure global food security and sustainable agriculture. Several direct and indirect methods exist for identification of crop water stress, but they are time consuming, tedious and require highly sophisticated sensors or equipment. Image processing is one of the techniques which can help in the assessment of water stress directly. Machine learning techniques combined with image processing can aid in identifying water stress beyond the limitations of traditional image processing. Deep learning (DL) techniques have gained momentum recently for image classification and the convolutional neural network based on DL is being applied widely. In present study, comparative assessment of three DL models: AlexNet, GoogLeNet and Inception V3 are applied for identification of water stress in maize (Zea mays), okra (Abelmoschus esculentus) and soybean (Glycine max) crops. A total of 1200 digital images were acquired for each crop to form the input dataset for the deep learning models. Among the three models, performance of GoogLeNet was found to be superior with an accuracy of 98.3, 97.5 and 94.1% for maize, okra and soybean, respectively. The onset of convergence in GoogLeNet models commenced after 8 epochs with 22 (maize), 31 (okra) and 15 (soybean) iterations per epoch with error rate of less than 7.5%.Not Availabl