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A comparative study to detect rice plant disease using convolutional neural network (CNN) and support vector machine (SVM)

DOI: https://doi.org/10.1234/4.2020.5679

Author(s):

Arsha Sugathan, S. Sruthi, Fousia M. Shamsudeen

Recieved Date: 2020-01-10, Accepted Date: 2020-03-26

Abstract:

The identification and diagnosis of paddy diseases could be an interesting field within the research area of agriculture. At this deep learning could even be a prominent research topic in pattern recognition and analysis. During this study, we compared 3 rice diseases like hispa, brown spot and leaf blast supported deep convolutional neural network (CNN) and support vector machine (SVM) model. For experimental purpose 500 images of healthy and diseased dataset is used. In this paper, we only compared 2 machine learning techniques and implemented the simplest model to classify the disease supported its speed of classification. CNN achieved a high classification accuracy of 97.8% while SVM achieved only 44%. The simulation results for the detection of rice disease showed the pliability and effectiveness of the proposed system.

Keywords:

Deep learning, classification, image processing, CNN, support vector machine

Journal: Journal of Food, Agriculture and Environment
Year: 2020
Volume: 18
Issue: 2
Category: Agriculture
Pages: 79-83

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A comparative study to detect rice plant disease using convolutional neural network (CNN) and support vector machine (SVM)

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A comparative study to detect rice plant disease using convolutional neural network (CNN) and support vector machine (SVM)