基于BP-LCO的大豆种植密度和施肥量优化  

Optimization of Soybean Planting Density and Fertilizer Application Rate Based on BP-LCO

作　　者：卢珊 王福林[1] LU Shan;WANG Fu-lin(College of Engineering,Northeast Agricultural University,Harbin 150010,China)

机构地区：[1]东北农业大学工程学院,黑龙江哈尔滨150010

出　　处：《大豆科学》2023年第2期204-211,共8页Soybean Science

基　　金：国家重点研发计划(2018YFD0300105)。

摘　　要：为解决传统回归模型对大豆种植密度及施肥量进行优化时存在的结果不准确的缺陷,本研究提出基于BP神经网络的线性约束优化方法(BP-Linear Constrained Optimization, BP-LCO)。以黑河43为试验材料,进行四因素五水平正交旋转试验,试验因素为大豆种植密度,N、P2O5和K2O施用量,评价指标为大豆产量,采用BP-LCO算法对种植密度、施肥量与产量关系构建拟合模型,并进行全局寻优及验证试验。结果显示:通过模型分析得到最优种植密度36.67×10^(4)株·hm^(-2)、施N量77.98 kg·hm^(-2)、施P2O5量93.79 kg·hm^(-2)、施K2O量24.34 kg·hm^(-2),大豆产量相应为3 679.56 kg·hm^(-2)。验证试验结果表明,最优配比下大豆实际产量为3 702.29 kg·hm^(-2),实际产量与理论产量的相对误差为0.62%。结果说明该方法的优化结果准确,是一种行之有效的大豆种植密度及施肥量优化方法。In order to solve the problem of inaccurate results in the traditional regression model for optimization of soybean planting density and fertilizer application, a BP-Linear Constrained Optimization(BP-LCO) method based on BP neural network was proposed in this study. We used Heihe 43 as experimental material to carry out four-factor and five-level orthogonal rotation test. The experimental factors were planting density of soybean, application amount of N, P2O5and K2O, and the evaluation index was yield of soybean, BP-LCO algorithm was used to construct a fitting model for the relationship between planting density, fertilizer application and yield, and we carried out global optimization and validation experiments. The results of the model analysis showed that the optimal planting density was 36.67×10^(4) plants·ha^(-1), N application rate was 77.98 kg·ha^(-1), P2O5application rate was 93.79 kg·ha^(-1), K2O application rate was 24.34 kg·ha^(-1), and the corresponding soybean yield was 3 679.56 kg·ha^(-1). The verification test showed that the actual yield of soybean was 3 702.29 kg·ha^(-1)under the optimal ratio, and the relative error between the actual yield and the theoretical yield was 0.62%, which proved that the method had accurate optimization results and was an effective optimization method.

关 键 词：BP神经网络 线性约束优化 大豆 种植密度 施肥量 

分 类 号：S565.1[农业科学—作物学]