农业工程2026，Vol.16Issue(3)：14-20,7.DOI:10.19998/j.cnki.2095-1795.202511031
基于PSO-BP神经网络的大豆播种机智能排肥系统设计
Design of intelligent fertilization system for soybean seeder based on PSO-BP neural network
摘要
Abstract
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.关键词
PID控制/粒子群优化/大豆播种机/排肥系统/深度学习Key words
PID control/particle swarm optimization/soybean seeder/fertilization system/deep learning分类
农业科技引用本文复制引用
董稼祥,张惠莉,王筱玮,郭鹏,夏超..基于PSO-BP神经网络的大豆播种机智能排肥系统设计[J].农业工程,2026,16(3):14-20,7.基金项目
山东省重点研发计划(重大科技创新工程)项目(2021CXGC010813) （重大科技创新工程）
山东省基地和人才计划项目(WSR2024092) （WSR2024092）
东营市科技成果转化专项(2024CGZH14) （2024CGZH14）
烟台市科技计划项目(2023ZDCX029) （2023ZDCX029）
