Physiological and image-based characterization of Botrytis cinerea infection progression in postharvest 'Haegeum'...

Gray mold caused by Botrytis cinerea severely limits the postharvest storage and marketability of kiwifruit, creating a need for accurate and non-destructive infection assessment. In this study, a symptom based, ROI-aware, and interpretable deep-learning framework was developed to classify infection levels in golden-fleshed 'Haegeum' kiwifruit. Infection severity was categorized into four levels based on external visual symptoms: Level 1: no lesions; Level 2: slight discoloration; Level 3: localized mycelial growth; Level 4: extensive mycelial growth and tissue breakdown. The associated physiological responses were subsequently characterized for each level. Disease progression induced fruit softening with firmness decreasing from 4.82 at Level 1 to 2.79 N at Level 4, while respiration rates increased from 0.05 to 0.24 µmol kg^-1 s^-1. Ethylene production began at Level 3 and sharply increased at Level 4, reaching 0.015 µmol kg^-1 s^-1. To quantify lesion severity, a region-of-interest-aware pipeline using RGB blue and LAB L* and a* channels was developed, enabling lesion-focused visualization of infection. Compared with the baseline model, the proposed approach improved classification performance, achieving accuracies of 90.3% with EfficientNet-B0, ResNet-50, and ShuffleNet-V2, and 87.6% with ResNet-18. Gradient-weighted class activation mapping further indicated that the ROI-aware pipeline reduced background-related activation and improved the spatial confinement of model attention within fruit regions. These findings demonstrate that symptom-based RGB classification, supported by post hoc physiological characterization and explainable deep-learning analysis, provides a biologically interpretable framework for monitoring visible fungal infection progression in postharvest kiwifruit.