title: Evaluation models for soil nutrient based on support vector machine and artificial neural networks.
abstract: 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.
authors.affiliation: 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.
authors.forename: Hao
authors.initials: H
authors.lastname: Li
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, Sichuan 610064, China.
authors.forename: Weijia
authors.initials: W
authors.lastname: Leng
authors.affiliation: Key Laboratory of Marine Bio-Resources Restoration and Habitat Reparation in Liaoning Province, Dalian Ocean University, Dalian 116023, China.
authors.forename: Yibing
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Key Laboratory of Marine Bio-Resources Restoration and Habitat Reparation in Liaoning Province, Dalian Ocean University, Dalian 116023, China.
authors.forename: Fudi
authors.initials: F
authors.lastname: Chen
authors.affiliation: College of Life Science and Technology, Dalian University of Technology, Dalian 116021, China.
authors.forename: Zhilong
authors.initials: Z
authors.lastname: Xiu
authors.affiliation: Key Laboratory of Marine Bio-Resources Restoration and Habitat Reparation in Liaoning Province, Dalian Ocean University, Dalian 116023, China ; College of Life Science and Technology, Dalian University of Technology, Dalian 116021, China.
authors.forename: Dazuo
authors.initials: D
authors.lastname: Yang
mesh_terms: D016014:Linear Models; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000032:analysis*; D010758:Phosphorus / Q000032:analysis*; D012987:Soil / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D012987:Soil; D010758:Phosphorus; D009584:Nitrogen