Deep Learning-Based Leaf Image Analysis for Tomato Plant Disease Detection and Classification

Abstract

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Tomato plant disease detection and classification, utilizing leaf images through deep learning, intersects the fields of plant pathology and agriculture. Deep learning has demonstrated significant potential in accurately identifying and classifying various plant diseases from leaf images. In this study, we introduce a hybrid system that combines a potent machine learning algorithm, Exponential Discriminant Analysis (EDA), with a transfer learning process leveraging recent and advanced deep networks, including ResNet50, Darknet53, DenseNet201, and EfficientNetB0. This system was evaluated using two challenging datasets: Taiwan and PlantVillage tomato leaf datasets. The experimental results underscore the high competitiveness of the proposed method, achieving mean accuracies of 98.29% and $\mathbf{9 8. 0 9 \%}$ on these datasets, respectively.