Potatoes Leaf Disease Detection through Naïve Bayes, JRip, and Decision Stump with BPPF and ACCF models
Keywords:
Real-time Machine Learning, Performance Metrics, Pattern Recognition, Classification, ACCF, BPPF, Feature Engineering, Decision Stump, JRip, Naïve BayesAbstract
Plant diseases constitute a critical challenge to global agricultural systems, leading to substantial economic losses and threatening food security. The early detection and accurate identification of plant diseases are essential components for achieving sustainable crop production. Solanum tuberosum (potato) is among the most widely cultivated and economically important crops worldwide, yet it remains highly susceptible to a variety of pathogenic infections, particularly foliar diseases. Early-stage identification and precise classification of leaf diseases are crucial for implementing timely management strategies and minimizing yield losses. Accurate disease classification not only aids in effective crop protection but also contributes to overall agricultural resilience. In this study, we propose a deep learning-based model specifically designed for the classification of potato leaf diseases, aiming to enhance early detection capabilities and support decision-making processes in precision agriculture. The research introduces a time-efficient disease classification framework that utilizes BPPF and ACCF feature extraction methods together with Naïve Bayes and Decision Stump and JRip classifiers running on WEKA 3.9.5 platform. The evaluation standard utilized the Kaggle dataset consisting of 1500 labeled images that covered 3 different classes of healthy and diseased leaves. A standardization process was applied to the input data through image resizing and normalization with additional augmentation techniques. The classifiers received training through extracted features from BPPF and ACCF while their performance evaluation used Accuracy, Precision, Recall and ROC-AUC, PRC-AUC along with Execution Time metrics. BPPF combined with Naïve Bayes classifier established 97.28% accuracy while ACCF and Naïve Bayes achieved 97.02% accuracy during 0.02 seconds of execution time. JRip-based models achieved high precision and recall numbers though their computational expenses remained high whereas Decision Stump models operated fast yet gave inferior classification accuracy results. Naïve Bayes classifiers operated with BPPF and ACCF descriptors reveal themselves as highly effective tools for real-time plant disease diagnosis in agricultural settings through their fast execution and interpretable method.
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