Rice Plant Disease Detection using Twin Support Vector Machine (TSVM)

  • Bikash Chawal Department of Computer Engineering, Khwopa Engineering College, Purbanchal University
  • Sanjeev Prasad Panday Department of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Kathmandu
Keywords: TSVM, Image Processing, Binary Conversion, Image Recognition, Classification

Abstract

Crop disease epidemics can cause severe losses and affect agricultural products and food security especially in south Asian countries and Nepal where rice is enjoyed as a staple throughout the year. To achieve automatic diagnosis of crop disease the proposed system aims to develop a prototype system for detection of the paddy disease. Image recognition of the disease would be conducted based on Image Processing techniques to enhance the quality of the image and Twin Support Vector Machine (TSVM) technique to classify the paddy disease. The methodology involves image acquisition, pre-processing, analysis and classification of the paddy disease. All the paddy sample images will be passed through the RGB calculation before it proceeds to the binary conversion. If the sample is in the range of normal paddy RGB, then it is automatically classify as normal. Then, all the segmented paddy disease sample will be converted into the binary data in data base before proceed through the TSVM for training and testing. The proposed system is targeted to achieve better recognition results.

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Abstract
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pdf
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Published
2019-12-22
How to Cite
Chawal, B., & Panday, S. (2019). Rice Plant Disease Detection using Twin Support Vector Machine (TSVM). Journal of Science and Engineering, 7, 61-69. https://doi.org/10.3126/jsce.v7i0.26794
Section
Research Article