Beyond Image-Level Random Splits: A Leakage-Aware Reliability Assessment for Lightweight Jackfruit Leaf Health-State Recognition

This study presents a leakage-aware reliability evaluation framework for jackfruit leaf health-state recognition, addressing the risk of inflated performance estimates caused by image-level random splitting. It investigates how visually similar or related images distributed across training, validation, and test partitions can introduce hidden data leakage and compromise the reliability of deep learning evaluation.

To mitigate this issue, a conservative leakage-control visual grouping (LCVG) strategy is developed to enforce separation of visually related samples before model assessment. The proposed LCVG-safe evaluation protocol is systematically compared with conventional random splitting across multiple deep learning architectures, independent random seeds, and reconstructed dataset partitions to quantify the impact of leakage on reported performance.

Furthermore, this study provides a comprehensive reliability analysis through uncertainty estimation, calibration assessment, robustness evaluation under controlled corruptions, lightweight deployment analysis, and persistent error investigation. The findings highlight the importance of leakage-aware evaluation for developing trustworthy deep learning systems for practical agricultural applications.