Li Hao, Leng Weijia, Zhou Yibing, Chen Fudi, Xiu Zhilong, Yang Dazuo
College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China ; Key Laboratory of Marine Bio-Resources Restoration and Habitat Reparation in Liaoning Province, Dalian Ocean University, Dalian 116023, China.
College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China.
ScientificWorldJournal. 2014;2014:478569. doi: 10.1155/2014/478569. Epub 2014 Dec 7.
Soil nutrient is an important aspect that contributes to the soil fertility and environmental effects. Traditional evaluation approaches of soil nutrient are quite hard to operate, making great difficulties in practical applications. In this paper, we present a series of comprehensive evaluation models for soil nutrient by using support vector machine (SVM), multiple linear regression (MLR), and artificial neural networks (ANNs), respectively. We took the content of organic matter, total nitrogen, alkali-hydrolysable nitrogen, rapidly available phosphorus, and rapidly available potassium as independent variables, while the evaluation level of soil nutrient content was taken as dependent variable. Results show that the average prediction accuracies of SVM models are 77.87% and 83.00%, respectively, while the general regression neural network (GRNN) model's average prediction accuracy is 92.86%, indicating that SVM and GRNN models can be used effectively to assess the levels of soil nutrient with suitable dependent variables. In practical applications, both SVM and GRNN models can be used for determining the levels of soil nutrient.
土壤养分是影响土壤肥力和环境效应的一个重要方面。传统的土壤养分评价方法操作难度较大，在实际应用中存在很大困难。在本文中，我们分别使用支持向量机（SVM）、多元线性回归（MLR）和人工神经网络（ANNs）提出了一系列土壤养分综合评价模型。我们将土壤有机质、全氮、碱解氮、速效磷和速效钾的含量作为自变量，而将土壤养分含量的评价等级作为因变量。结果表明，支持向量机模型的平均预测准确率分别为77.87%和83.00%，而广义回归神经网络（GRNN）模型的平均预测准确率为92.86%，这表明支持向量机和广义回归神经网络模型可以有效地用于评估具有合适因变量的土壤养分水平。在实际应用中，支持向量机和广义回归神经网络模型均可用于确定土壤养分水平。
