Robotics-automation and sensor-based approaches in weed detection and control: A review

Accurate estimation of reference evapotranspiration (ET₀) is crucial for a multitude of applications, encompassing drought detection, irrigation scheduling, water resource management, and disaster risk reduction. This investigation utilized the FAO-PM equation for ET₀ estimation and subsequently incorporated meteorological variables as input variables with machine learning (ML) models to enhance ET₀ predictions. The dataset was bifurcated into training and testing data segments. Four distinct machine learning models were deployed in this study, namely Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Linear Regression (LR). The performance of these models was evaluated using various statistical indices, including Mean Absolute Error (MAE), Mean Sum of Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the coefficient of determination (R²), to pinpoint the most efficacious ML algorithm. After conducting a comprehensive analysis involving both training and testing data, the results unequivocally identify GBM with MAE values of 0.054 and 0.077, MSE values of 0.005 and 0.011, MAPE values of 0.014 and 0.022, RMSE values of 0.072 and 0.107, and an R2 value of 0.096 and 0.092 during training and testing, respectively. This model has been selected as the optimal choice for precise ET0 estimation within the study region. Subsequently, SVM, RF, and LR follow as alternatives in terms of performance, in descending order.

Precision irrigation with IoT-based decision-making technologies has proven effective in optimizing agricultural production irrigation. The Internet of Things (IoT) is crucial for monitoring the real-time data from sensors and automatically activating irrigation systems. This study evaluated the effectiveness of a drip irrigation system based on IoT and soil moisture sensors in a field experiment with sweet corn between 2020 and 2022. There were nine treatments with three replications: ETc-based drip irrigation (ETc 100%) and IoT-based drip irrigation scheduling with two soil moisture levels under three mulches: black plastic mulch, silver plastic mulch, and control (bare soil). IoT-based drip irrigation scheduling (100% FC) applied irrigation when soil moisture reached a lower threshold (≤ 33.1%) and ended when the field capacity was reached (≥ 43.5). With IoT-based drip irrigation scheduling (80% FC), irrigation was applied when the soil moisture content reached the threshold (≤ 33.1%) and ended when the field capacity reached 80% (≥ 34.8). Growth variables (root biomass, yield, corn length, cob weight, and water productivity) were compared for each irrigation method. Results showed that the ET-based irrigation method was easier to implement with less infrastructure and could result in lower yields than the IoT-based drip irrigation method with 100% FC. Grain and stalk yields increased by more than 12.05% and 14.97% for the IoT irrigation with 100% FC. It was found that IoT-based drip irrigation with 100% FC and 80% FC used 12.7% and 24.5% less irrigation water, respectively, and provided IoT-based drip irrigation with 100% FC, there was a 12.8% increase in marketable yield than ETc and IoT-based drip irrigation with 80% FC. The results show that the developed IoT system can potentially monitor the microclimate of plants in real time under different conditions of using plastic mulch. The IoT system is rugged and water-resistant, making it suitable for outdoor agriculture. Solar panels power the system, so there is no need for cabling and sensor nodes can be efficiently monitored. Research conducted on the IoT system shows that it can record and display environmental parameters to users via the cloud (ThingSpeak).

Paddy (rice) is considered as the most important staple food-cereal crop in south-east Asian countries. It is the majorly consumed food in India. Transplanting of paddy is a highly labor-intensive operation and is still done manually. It takes around 182 to 250-man hours a day to manually transplant rice. Also, many a time due to shortage of labour, transplanting is delayed which causes a progressive decrease in yield. The scarcity of labour during the peak season of transplanting creates a problem to complete the transplanting operation in time. Manually operated paddy transplanter can be useful for small and marginal landholdings. Here we intended to develop equipment that should be of low cost, fabricated locally, versatile in utility, reducing drudgery by making transplanting possible without bending and useful for small farmers. The developed two row rice transplanter prototype can transplant 20 to 25-day old seedlings up to 5 cm depth with adjustable row spacing of about 15-20 cm. It requires two operators, one for filling the tray while walking behind and another for pulling the transplanter while walking in front of the machine. The actual filed capacity of 0.2 ha per day (eight hour working day) was achieved with the machine by considering a 5%- and 3%- time loss due to turning and filling tray respectively.