Research on intelligent maize targeted fertilisation method based on BPNN PID adaptive position feedback regulation

Abstract

Given problems, such as low accuracy of fertiliser application control, large positioning errors, and poor fault monitoring effects in targeted fertilisation operations, this study proposes an intelligent maize-targeted fertilisation method based on a Backpropagation Neural Network (BPNN) Proportional-Integral-Derivative (PID) adaptive position feedback regulation. With the STM32 microcontroller as the master-slave controller, an intelligent maize-targeted fertilisation system was developed through the design of multi-sensor fusion, control parameter calculation and optimisation, construction of a fertilisation drive device, and fault monitoring system. BPNN PID adaptive optimisation was used to control the angular displacement of the fertiliser applicator, and automatic control technology drove the targeted fertilisation mechanism. By integrating dual photoelectric sensors to detect the target maize, an encoder collects the angular displacement of the fertiliser applicator, a ranging sensor monitors the fertiliser amount in the fertiliser box, a pressure sensor monitors the status of the fertiliser pipe, a positioning sensor monitors the operation speed, and multi-machine communication processes the fertilisation operation data. Targeted control and fault monitoring of fertilisation operations under multi-sensor fusion were realised. The adjustment time of the optimisation algorithm is 0.9 s, and the response is fast. Experiments show that the accuracy of fertiliser application control is greater than 95 %, the average positioning error of fertilisation is less than 28.1 mm, the fault alarm success rate reaches 97 %, and the average response time of fault alarm is less than 0.45 s. The intelligent maize-targeted fertilisation method in this study can achieve precise fertilisation control in maize-targeted fertilisation operations.