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Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks
Joint Authors
Li, Hao
Yang, Dazuo
Cao, Chenchen
Leng, Weijia
Zhou, Yibing
Xiu, Zhilong
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-12-07
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Li, Hao& Leng, Weijia& Zhou, Yibing& Cao, Chenchen& Xiu, Zhilong& Yang, Dazuo. 2014. Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1049761
Modern Language Association (MLA)
Li, Hao…[et al.]. Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks. The Scientific World Journal No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1049761
American Medical Association (AMA)
Li, Hao& Leng, Weijia& Zhou, Yibing& Cao, Chenchen& Xiu, Zhilong& Yang, Dazuo. Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1049761
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049761