A Weighted Group Learning Model for Classification of Grape Leaf Disease Using Image Processing and Machine Learning

Articles in Press

Document Type : Research Article-en

Authors

• M. Najafabadiha ¹
• D. Mohammad Zamani ¹
• M. Gholami Par-Shokohi ²

¹ Department of Mechanization Engineering, Tak.C., Islamic Azad University, Takestan, Iran

² Department of Mechanical Engineering, ShQ.C., Barench, Islamic Azad University, Tehran, Iran

https://doi.org/10.22067/jam.2025.92388.1347

Abstract

This study proposes a novel method for identifying grape leaf diseases through RGB image analysis combined with weighted group decision-making. The investigation focused on five disease types, Black Measles, Black Rot, Leaf Blight, Powdery Mildew, and Downy Mildew, along with healthy leaves. Three machine learning classifiers, namely support vector machine (SVM), random forest (RF), and k-nearest neighbor (k-NN), were employed individually and in a weighted ensemble. Each classifier was assigned a weight based on its accuracy, and the final disease classification was determined using a majority voting strategy. To determine the most discriminative features related to texture, color, and shape, the Relief feature selection algorithm was applied, which identified the top five effective features in diagnosing grape leaf diseases. Experimental results indicated that the classification accuracies of SVM, RF, and k-NN were 88.33%, 80.08%, and 75%, respectively. Furthermore, the proposed weighted group decision-making approach improved the overall classification performance, achieving an accuracy of 91.67%.

Keywords

• Feature selection
• Grape disease
• Group decision making
• Image processing
• Majority voting

Main Subjects

• Image Processing

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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