Efficient detection of tomato leaf diseases using optimized Compact Convolutional Transformers (CCT) Model

Tomato crops are highly susceptible to various leaf diseases, posing a significant threat to agricultural yield and economic viability. Traditional disease detection methods, reliant on expert visual inspection, are time-intensive, inconsistent, and impractical on a large scale. This study addresses these limitations by developing an optimized Compact Convolutional Transformer (CCT) model tailored to efficiently and accurately classify tomato leaf diseases using image data. Leveraging a dataset of over 30,000 images spanning multiple disease classes and augmented through advanced techniques, we trained and tested the CCT model alongside popular transfer learning architectures, including VGG16, ResNet50, and Vision Transformers (ViTs). Our methodology involved extensive hyperparameter tuning and comparative analysis to maximize model accuracy and robustness. Results demonstrate that the optimized CCT model outperforms competing architectures, achieving an impressive accuracy of 98.87%, significantly higher than baseline models. The analysis further includes learning curves, confusion matrices, and ROC-AUC evaluations, which validate the model's reliability and ability to generalize across diverse image conditions. This work underscores the potential of hybrid transformer models in agriculture, offering a scalable, high-performance solution for the real-time detection of tomato leaf disease. The scalability of our solution makes it adaptable to various agricultural settings, ensuring its forward-thinking nature.