Fuzzy Aggregation-Based Deep Learning Ensemble for Enhanced Tomato Leaf Disease Classification

Accurate and timely diagnosis of plant diseases plays a crucial role in ensuring global food security, safeguarding human health, and supporting the economic well-being of farmers. Currently, there is a growing interest in developing reliable deep learning systems for identifying various plant species and their associated diseases. In this respect, combining multiple CNN is an effective approach for improving disease classification since each individual model has unique design principles that enable them to extract different levels and types of features from input images. The present work introduces a novel fuzzy aggregation-based CNN ensemble for tomato leaf disease classification. Initially, five CNN architectures, namely, VGG16, ResNet152V2, MobileNetV2, InceptionV3, and DenseNet201, are designed for classifying tomato leaf diseases. Based on their diverse statistical characteristics, selected models are then integrated into a CNN ensemble. To achieve this step, we introduce a new fuzzy Max-Sum integral, built upon a fuzzy convex combination operator. Performance assessment is conducted on two datasets including 10 and 6 tomato disease classes. Further experiments are conducted by merging these datasets to include a broader range of disease categories. The results demonstrate that the proposed CNN ensemble outperforms both individual models and the CNN ensemble based on the classical Sugeno fuzzy integral.