Plant Disease Forecasting Based on Wavelet Transformation and Support Vector Machine

Select Volume / Issue:

• Volume 5,Issue 2, Mar. 2018

Year:

2018

Type of Publication:

Article

Keywords:

Plant Disease Prediction, Environment Information, Wavelet Transformation, SVM

Authors:

Wang, Hong; Zhang, Shanwen; Shao, Yu; Zhang, Yunlong

Journal:

IJRAS

Volume:

5

Number:

2

Pages:

90-94

Month:

March

ISSN:

2348-3997

Note:

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. CC BY-NC-SA 4.0 Creative Commons License: https://creativecommons.org/licenses/by-nc-sa/4.0/

Abstract:

A forecasting method of plant disease based on wavelet transformation (WT) and Support Vector Machine (SVM) is introduced. The environment information data is essentially an unstationary time sequence, which can be decomposed into different frequency channels by WT and obtain the forecasting features. The disease can be forecasted by SVM. The average forecasting precision was over 86%. Experimental results on three common kinds of cucumber diseases show that the proposed method is more effective for plant disease forecasting

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