In agricultural production, precision fertilization is affected by fertilizer drift and over fertilization causing low fertilizer use and extreme environmental pollution. Conventional fertilization systems cannot identify the location of crops in real time and precisely control the application of fertilizers. The proposed paper is a drift and over-fertilization prevention model, which is founded on the principle of visual recognition and variable operation. To begin with, a better YOLOv5 algorithm is applied to identify and position crops in real-time in the field, whereby the feature extraction networks and attention mechanisms are enhanced to increase the accuracy of the detection. Second, fertilizer drift prediction model is determined to compute fertilization offset by using wind speed, nozzle height and crop location, which dynamically changes spraying angle and position. Third, the variable fertilization control system is built to modify the rate of fertilizer application in real-time according to the prescription map (PM) and crop growth condition with an accurate fertilization by opening the solenoid valve by pulse width modulation (PWM). Lastly, there is a PID (Proportional-Integral-Derivative) controller, which aims at optimizing the speed of response. Field trials showed that the model achieved an average crop identification accuracy of 93.9%, reduced the average fertilization drift rate to 9.0%, and reduced both over-fertilization and under-fertilization to less than 5.5%.
Chen et al. (Thu,) studied this question.