Proceedings of International Conference on Applied Innovation in IT
2026/03/31, Volume 14, Issue 1, pp.49-58
Hybrid CNN - ViT Framework for Guava Leaves and Fruits Disease Detection in Precision Agriculture
Nebras Jalel Ibrahim, Farah Hatem Khorsheed, Zainab Hassan Mohammed, Alaulddin Mueen Latfa, Walaa Badr Khudhair , Zahraa Fadhil Hassan and Hussein Alkattan Abstract: The detection of guava plant diseases is essential for ensuring fruit quality and reducing agricultural losses. In this study, we developed a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) to classify guava leaf diseases from image data. The model was trained and validated on an augmented dataset comprising five categories: Disease Free, Phytophthora, Red Rust, Scab, and Styler and Root. Experimental results demonstrate that the proposed hybrid model achieved an overall accuracy of 96%, with a macro-averaged F1-score of 0.97. Class-level performance showed near-perfect detection for Disease Free and Red Rust, while slightly lower but still robust results were obtained for Styler and Root (F1-score = 0.93). The confusion matrix indicates strong generalization across all classes with minimal misclassification. Despite the strong classification metrics, highlighting the need for further optimization in probability calibration. Overall, the results confirm that combining CNN feature extraction with ViT’s sequence modeling provides an effective strategy for guava disease recognition, offering a reliable decision-support tool for precision agriculture.
Keywords: Guava Disease Detection, Convolutional Neural Networks (CNN), Vision Transformers (Vit), Hybrid Deep Learning, Image Classification, Precision Agriculture, Plant Disease Detection.
DOI: Under indexing
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