Adapted Approach for Fruit Disease Identification using Images

TL;DR

This paper presents an adaptive image processing approach combining K-Means clustering, feature extraction, and SVM classification to accurately identify apple diseases with up to 93% accuracy.

Contribution

It introduces a novel integrated method for fruit disease detection that enhances accuracy using image segmentation and machine learning.

Findings

Achieved 93% classification accuracy for apple diseases

Effective segmentation and feature extraction improve detection reliability

Supports automatic and accurate fruit disease identification

Abstract

Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, an adaptive approach for the identification of fruit diseases 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 state of the art features are extracted from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine. We have considered diseases of apple as a test case and evaluated our approach for three types of apple diseases namely apple scab, apple blotch and apple rot. Our experimental results express that the proposed solution can significantly support accurate detection and automatic identification of fruit diseases. The classification accuracy for the proposed solution is achieved up to 93%.