Browsing by Author "Mwaibale, Upendo"

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    Enhancing detection of common bean diseases using Fast Gradient Sign Method–trained Vision Transformers
    (Frontiers in Artificial Intelligence, 2025-08-06) Mwaibale, Upendo; Mduma, Neema; Laizer, Hudson; Mgawe, Bonny
    Common bean production in Tanzania is threatened by diseases such as bean rust and bean anthracnose, with early detection critical for effective management. This study presents a Vision Transformer (ViT)-based deep learning model enhanced with adversarial training to improve disease detection robustness under real- world farm conditions. A dataset of 100,000 annotated images augmented with geometric, color, and FGSM-based perturbations, simulating field variability. FGSM was selected for its computational efficiency in low-resource settings. The model, fine-tuned using transfer learning and validated through cross-validation, achieved an accuracy of 99.4%. Results highlight the effectiveness of integrating adversarial robustness to enhance model reliability for mobile-based plant disease detection in resource-constrained environments.