Deep Learning for Tomato Leaf Disease Classification: Comparative Benchmarking of CNN and Vision Transformer Architectures

Résumé fourni par la source

Tomato production is highly vulnerable to foliar diseases that can reduce yield, increase management costs, and complicate timely intervention. Automated image-based diagnosis has therefore become an important research direction for precision agriculture. In this study, we present a comparative evaluation of four representative deep learning backbones for tomato leaf disease classification on the Plant Village dataset: EfficientNetV2-S, ConvNeXt-Base, DeiT3-Base, and Swin-Base. The dataset comprised 18,160 images from ten classes, including nine disease categories and one healthy class, and was divided into training, validation, and test sets using a 70:15:15 split. All models were trained under a standardized transfer learning pipeline with identical preprocessing, augmentation, and optimization settings to enable a fair comparison across architectures. Performance was assessed using accuracy, precision, recall, F1-score, parameter count, and GFLOPs. All evaluated models achieved very high classification performance, with test accuracies of at least 0.9985. Among them, Swin-Base yielded the best overall predictive performance, reaching an accuracy of 0.9989 and an F1-score of 0.9987. In contrast, EfficientNetV2-S provided the most favorable efficiency profile, achieving 0.9985 accuracy with only 20.19 million parameters and 5.4193 GFLOPs. These findings indicate that both convolutional and transformer-based models can deliver highly reliable tomato leaf disease classification under controlled benchmark conditions, while the final model choice should be guided by the application scenario. Swin-Base is preferable when maximum predictive performance is prioritized, whereas EfficientNetV2-S offers a more practical option for computationally constrained deployments.

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