基于LM-BP神经网络的耕地土壤养分等级划分模型──以皖南山区为例

研究旨在通过BP神经网络方法，构建起LM-BP网络结构(5-M-1)模型，达到对土壤养分等级划分的目的，为合理的土壤养分管理提供可靠依据。采用Levenberg-Marquardt (LM)训练算法，构建3层网络模型：一个输入层、一个隐含层、一个输出层，利用3层网络作为耕地土壤养分等级划分模型。利用土壤养分各级评价标准作为模型的训练样本和测试样本，以此来对BP神经网络进行训练和测试，并对歙县土壤养分进行综合评价。结果表明：LM-BP网络结构对测试样本输出的预测值和实际参考值是一致的。最终通过灰色关联模型和主成分分析方法对歙县土壤养分的综合评价结果与BP神经网络的模拟结果相对比，发现也是基本一致的。LM-BP网络结构应用于土壤养分等级划分中，得到了很好的预测效果，为智能算法应用于农业领域奠定了良好的基础。

Model of Grading Farmland Soil Nutrient Based on LM-BP Neural Network: A Case of South Anhui Mountainous Areas

The study aims to build LM-BP network structure (5-M-1) model by the BP neural network method, so as to achieve the division of soil nutrient levels and provide reliable basis for optimum soil nutrient management. Levenberg-Marquardt (LM) training algorithm was used to construct a three-layer network model (input, hidden, and output layer), which was used for soil nutrient evaluation. Criteria for soil nutrient evaluation were used as model samples to train and test BP neural network, and comprehensive evaluation of soil nutrient levels in Shexian County was made. The results showed that the output predicted value by LM-BP network structure coincided with the actual reference value. Comprehensive evaluation result of soil nutrient levels in Shexian County which based on gray correlation model and principal component analysis was basically the same with the simulation result of BP neural network. LM-BP network structure achieved favorable predicted results in its application to the grading of soil nutrient levels, which could provide a solid foundation for the application of intelligence algorithm in agriculture.