基于PSO-BP神经网络的大豆播种机智能排肥系统设计OA

To address issues of traditional soybean seeder fertilizer systems,such as inability to dynamically adjust fertilizer rates ac-cording to soil conditions and low fertilizer utilization efficiency,an intelligent soybean fertilization system integrating fuzzy PID control with PSO-BP neural network has been designed.Soil pH,electrical conductivity,volumetric water content,and operating speed were introduced as control variables.A predictive model taking multi-source soil data as input was constructed to achieve real-time soil state perception and dynamic fertilizer application rate regulation along seeding path.Controller was implemented on an STM32 platform with integrated hardware and software,adopted a predictive-feedback dual-loop architecture verified through Matlab/Simulink simulations.Results demonstrated that system exhibited excellent robustness and adaptive adjustment capability,providing an effective approach for precision agricultural fertilization.Under conditions of simulation step size of 0.1 s and total duration of 1 000 s,model maintained mean fertilization error within±0.03 units,with short response time and minimal fluctuation amplitude,significantly improv-ing system's stability and control accuracy.