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

Message:
Article Type:
Research/Original Article (دارای رتبه معتبر)
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.
Language:
Persian
Published:
Journal of Soft Computing and Information Technology, Volume:13 Issue: 1, 2024
Pages:
39 to 51
https://www.magiran.com/p2782800  
سامانه نویسندگان
  • Sekineh Asadi Amiri
    Corresponding Author (1)
    Assistant Professor Faculty of Engineering & Technology, University of Mazandaran, Babolsar, University of Mazandaran, Babolsar, Iran
    Asadi Amiri، Sekineh
  • Hassan Katal
    Author (2)
    Masters Student Artificial Intelligence, Dept Computer Engineering, University of Mazandaran, Babolsar, Iran
    Katal، Hassan
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