An Intelligent Group Learning Framework for Detecting Common Tomato Diseases Using Simple and Weighted Majority Voting with Deep Learning Models

Plant diseases pose significant economic challenges and may lead to ecological consequences. Although plant pathologists have a significant ability to diagnose plant diseases, rapid, accurate, and early diagnosis of plant diseases by intelligent systems could improve disease control and management. This study evaluates six efficient classification models (classifiers) based on deep learning to detect common tomato diseases by analyzing symptomatic patterns on leaves. Additionally, group learning techniques, including simple and weighted majority voting methods, were employed to enhance classification performance further. Six tomato leaf diseases, including Pseudomonas syringae pv. syringae bacterial spot, Phytophthora infestance late blight, Cladosporium fulvum leaf mold, Septoria lycopersici Septoria leaf spot, Corynespora cassiicola target spot, and Alternaria solani early blight, as well as healthy leaves, resulting in a total of seven classes, were utilized for the classification. Deep learning models, such as convolutional neural networks (CNNs), GoogleNet, ResNet-50, AlexNet, Inception v3, and MobileNet, were utilized, achieving classification accuracies of 65.8%, 84.9%, 93.4%, 89.4%, 93.4%, and 96%, respectively. Furthermore, applying the group learning approaches significantly improved the results, with simple majority voting achieving a classification accuracy of 99.5% and weighted majority voting achieving 100%. These findings highlight the effectiveness of the proposed deep ensemble learning models in accurately identifying and classifying tomato diseases, featuring their potential for practical applications in tomato disease diagnosis and management.