Fungal Disease Detection on Vegetable Crops Using RGB Imaging

The fungal pathogen Botrytis cinerea (B. cinerea) attacks over 1400 plant species and results in estimated annual losses of $10–100 billion worldwide. In precision agriculture, deep learning (DL) provides reliable tools for rapid and objective plant disease detection. This study presents a unified two-stage DL solution for the automated detection of visible B. cinerea across three major vegetable crops—tomato, pepper, and cucumber—using standard RGB imagery. In the first stage, a YOLOv11-based instance segmentation model accurately localizes leaf regions, achieving a localization accuracy of 87.3% as measured by mAP50. In the second stage, an ensemble of 13 MobileViT variant models analyzes the segmented leaf regions and performs per-crop classification into healthy and infected leaves. The proposed system achieves an overall detection accuracy of 84.05%, with per-class detection of infected leaves at 88.61% for pepper, 82.68% for tomato, and 70.55% for cucumber, measured using the F1-score. These results demonstrate that the proposed approach can reliably detect B. cinerea symptoms across different crops using only RGB data, offering a practical path toward smartphone-based field deployment and integration into decision support systems for timely, symptom-based disease management.