Abstract:
Sweet orange leaf diseases significantly threaten agriculture, necessitating accurate
and timely detection for sustainable farming. This study presents a hybrid deep
learning approach combining Vision Transformers (ViT) and Convolutional Neural
Networks (CNN) for classifying sweet orange leaf diseases. The methodology includes
data preprocessing, such as resizing, normalization, and augmentation, to enhance
dataset quality and prepare it for deep learning models. Three models—ViT,
ResNet50v2, and the hybrid ViT-CNN—were implemented and evaluated. The hybrid
ViT-CNN model achieved the highest test accuracy of 98%, surpassing the individual
performances of ViT (90%) and ResNet50v2 (97%), with consistent training and
validation accuracies of 97%. The hybrid model integrates the localized feature
extraction of CNNs with the global contextual capabilities of ViTs, enabling superior
disease classification. This research highlights the scalability and robustness of the
hybrid approach, addressing dataset scarcity and computational efficiency challenges.
Implemented on Google Colaboratory, the system is optimized for deployment in
resource-constrained environments, ensuring accessibility for small-scale farmers.
The findings contribute to precision agriculture by reducing crop losses, minimizing
pesticide use, and promoting sustainable practices. This study establishes a reliable
framework for agricultural disease detection, paving the way for advancements in AIdriven solutions for broader crop management applications.
