Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

Early Detection of Plant Diseases Using Image Processing and Machine Learning Techniques

Document Type : Persian Original Article

Authors
Dept. Computer Engineering, University of Mazandaran, Babolsar, Iran.
Abstract
Early detection of plant diseases is crucial for timely prevention of pest infestation. In this paper plant diseases are accurately identified through image processing of leaves and extracting various features such as color, texture, and shape. Generally, plant diseases cause significant changes in the color and appearance of leaves. The color feature can be used to determine the type of plant disease. This paper employs moment and Hu moment features for extracting color characteristics from leaves, and area and perimeter features for detecting shape changes. To enhance the proposed features, additional texture features such as Local Binary Pattern (LBP), co-occurrence matrix, and vein patterns are utilized. Finally, to prevent model complexity and overfitting, the mRMR feature selection method is applied to eliminate irrelevant features. After developing the final model, healthy and diseased samples are classified using Random Forest and Support Vector Machine (SVM) classifiers. The proposed method has been evaluated on the PlantVillage dataset with 87867 samples. The average accuracy of the proposed method in diagnosing diseases in 9 different plant samples using Random Forest and SVM classifiers is 97.63% and 94.98%, respectively, which surpasses existing methods.
Keywords

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