农业工程学报2025，Vol.41Issue(10)：51-60,10.DOI:10.11975/j.issn.1002-6819.202408134
基于多层感知机模型的稻麦双变量精准施肥机排肥策略
Research on fertilizer application strategy for rice-wheat dual-variable precision fertilizer applicator based on MLP
摘要
Abstract
Variable fertilization is an important technical approach in implementing precision agriculture.The method of external groove wheel-type variable fertilization with dual regulation of speed and aperture is a typical operation method for crop production(planting)in rice-wheat rotation areas.In response to current issues with variable fertilizer applicators such as slow control system response,inaccurate prediction models,large fertilizer amount errors,and insignificant effectiveness,this study,based on a self-developed dual-variable precision fertilizer applicator for rice and wheat,proposed a method for constructing a fertilizer amount prediction model based on a multilayer perceptron artificial neural network using mathematical statistics and machine learning methods,and verified its effectiveness and applicability.By analyzing the algorithm mechanisms of the levy flight algorithm(LFA),particle swarm optimization(PSO),and multilayer perceptron(MLP)neural network models,and combining the dual-variable fertilization method of aperture-speed,a fertilizer amount prediction model based on LFA-PSO-MLP(LPM)was constructed.The model incorporated the aperture-speed-fertilizer amount relationship,improved algorithm structure through normalization,regularization,etc.,conducted parameter optimization and model training,and compared the MLP and PSO-MLP models to obtain the optimal LFA-PSO-MLP fertilizer amount prediction model.Furthermore,an inverse LFA-PSO-MLP(ILPM)prediction model was constructed to quickly calculate the required aperture and speed based on the target fertilizer amount.Experimental results showed that the LFA-PSO-MLP model converged in about 50 iterations,with an R2 value of 0.999 after 500 iterations and a average relative error(ARE)of 1.83%,which was better than the other two models.Validation tests of the LPM model yielded an average relative error of 2.47%between predicted and validation values,while field experiments showed an average relative error of 3.49%between predicted and measured values.For the ILPM model,the average relative error for rotation speed prediction was 1.82%,and in field experiments,the maximum relative error between target and actual fertilization rates was 7.26%,with an average relative error of 6.09%.This indicated that the fertilizer applicator equipped with the ILPM model performed well in fertilizer application.The study demonstrated that the proposed model construction method can ensure the accuracy of fertilizer amount prediction while improving computational efficiency,achieving fast,precise,and efficient variable fertilization,and improving ecological and economic benefits.关键词
算法/粒子群/莱维飞行/多层感知机神经网络/双变量排肥策略Key words
algorithm/particle swarm/levy flight/multilayer perceptron neural network/dual-variable fertilization strategy分类
农业工程引用本文复制引用
施印炎,辛亚鹏,汪小旵,郑恩来,沈成,张昭..基于多层感知机模型的稻麦双变量精准施肥机排肥策略[J].农业工程学报,2025,41(10):51-60,10.基金项目
江苏省农业科技自主创新资金项目(CX(23)3029) （CX(23）
中央高校基本科研业务费专项资金资助(YDZX2024033) （YDZX2024033）
国家重点研发计划项目(2023YFD200100204 （）
2023YFD2000305-04) （）
