Research and Design of Hybrid Optimized Backpropagation (BP) Neural Network PID Algorithm for Integrated Water and Fertilizer Precision Fertilization Control System for Field Crops

China’s field crops such as cotton, wheat, and tomato have been produced on a large scale, but their cultivation process still adopts more traditional manual fertilization methods, which makes the use of chemical fertilizers in China high causes waste fertilizer resources ecological environmental damage. To address above problems, hybrid optimization genetic algorithms particle swarm (GA–PSO) is used to optimize initial weights backpropagation (BP) neural network, optimization-based BP network PID controller designed realize accurate control flow integrated water precision system for crops. At same time, STM32 microcontroller-based application was developed performance verified experimentally. The results show that has an average maximum overshoot 5.1% adjustment time 68.99 s, better than based (BP–PID) controllers; among them, algorithm by algorithm(GA–PSO–BP–PID) best-integrated when rate 0.6m3/h.