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.
Medentzidis et al. (Fri,) studied this question.