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Fruit Disease Recognition and Automatic Classification using MSVM with Multiple Features
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 181 - Number 10 |
| Year of Publication: 2018 |
| Authors: A. S. M. Shafi, Md. Bayazid Rahman, Mohammad Motiur Rahman |
10.5120/ijca2018916773
|
A. S. M. Shafi, Md. Bayazid Rahman, Mohammad Motiur Rahman . Fruit Disease Recognition and Automatic Classification using MSVM with Multiple Features. International Journal of Computer Applications. 181, 10 ( Aug 2018), 12-15. DOI=10.5120/ijca2018916773
Abstract
Image processing and machine learning play an important role in fruit disease identification and classification by means of image segmentation and pattern recognition. Traditional fault detection in the fruit surface is carried out manually by means of human inspection which is very time consuming and laborious. In this paper we have proposed a method for fruit disease identification using segmentation techniques and use a supervised learning technique for classifying images based on data analyzed from RGB colored images. Three types of common apple diseases are taken into considerations in this paper. The experimental results demonstrate that the proposed approach is promising and effective by showing the classification accuracy which has achieved more than 94% using several features.
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