Forecasting the rice stem borer occurrence tendency based on support vector machine

Author

Li-Bing, Zhang

Author_Institution

Sch. of Math. & Comput. Sci., Harbin Univ., Harbin, China

Volume

3

fYear

2009

fDate

8-9 Aug. 2009

Firstpage

356

Lastpage

358

Abstract

Support vector machine (SVM) which overcomes the drawbacks of neural networks has been widely used for forecasting and pattern recognition in recent years. In the study, the proposed SVM model is applied to pest degree forecasting of rice stem borer, and the structure of SVM forecasting system of pest degree is presented. The real data sets are used to investigate its feasibility in pest degree forecasting. The forecasting results indicate that SVM has higher forecasting accuracy than that of RBFNN in pest degree forecasting.

Keywords

agriculture; pattern recognition; pest control; support vector machines; SVM forecasting system; SVM model; pattern recognition; pest degree forecasting; rice stem borer occurrence tendency; support vector machine; Artificial neural networks; Communication system control; Computer network management; Computer networks; Mathematics; Neural networks; Pattern recognition; Predictive models; Support vector machine classification; Support vector machines; degree forecasting; forecasting accuracy; rice stem borer; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on

Conference_Location

Sanya

Print_ISBN

978-1-4244-4247-8

Type

conf

DOI

10.1109/CCCM.2009.5267913

Filename

5267913