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Automatic Fruit Disease Classification Using Images

Automatic Fruit Disease Classification Using Images

Shiv Ram Dubey, Anand Singh Jalal
Copyright: © 2014 |Pages: 19
ISBN13: 9781466660304|ISBN10: 1466660309|EISBN13: 9781466660311
DOI: 10.4018/978-1-4666-6030-4.ch005
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MLA

Dubey, Shiv Ram, and Anand Singh Jalal. "Automatic Fruit Disease Classification Using Images." Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, edited by Muhammad Sarfraz, IGI Global, 2014, pp. 82-100. https://doi.org/10.4018/978-1-4666-6030-4.ch005

APA

Dubey, S. R. & Jalal, A. S. (2014). Automatic Fruit Disease Classification Using Images. In M. Sarfraz (Ed.), Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies (pp. 82-100). IGI Global. https://doi.org/10.4018/978-1-4666-6030-4.ch005

Chicago

Dubey, Shiv Ram, and Anand Singh Jalal. "Automatic Fruit Disease Classification Using Images." In Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, edited by Muhammad Sarfraz, 82-100. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-6030-4.ch005

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Abstract

Diseases in fruit cause devastating problems in economic losses and production in the agricultural industry worldwide. In this chapter, a method to detect and classify fruit diseases automatically is proposed and experimentally validated. The image processing-based proposed approach is composed of the following main steps: in the first step K-Means clustering technique is used for the defect segmentation, in the second step some color and texture features are extracted from the segmented defected part, and finally diseases are classified into one of the classes by using a multi-class Support Vector Machine. The authors have considered diseases of apple as a test case and evaluated the approach for three types of apple diseases, namely apple scab, apple blotch, and apple rot, along with normal apples. The experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. The classification accuracy for the proposed approach is achieved up to 93% using textural information and multi-class support vector machine.

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