Data-Partitioning Sensitivity and Within-Session Stage Transfer in Vision-Based Garlic Soil Residue Grading on a Soil-Removal Test Bench

Continuously acquired garlic images can contain temporally related observations that affect soil residue grading estimates. This study assessed data-partitioning sensitivity and within-session acquisition-stage transfer using a two-stage You Only Look Once version 26 nano (YOLO26n) detector and EfficientNet-B0 classifier. The dataset comprised 754 red–green–blue (RGB) scene images and 10,721 bulb instances collected during one controlled session in which the conveyor operated at 0.5 m/s, while active vibratory soil removal was disabled (0 Hz vibration frequency and 0° screen inclination); inter-annotator agreement was high (quadratic-weighted Cohen’s κ = 0.8750; 360 instances). For seed 42, macro-averaged F1-score (Macro-F1) was 97.49% with random image splitting, 87.43% with 2 s temporal exclusion, and 76.32% with complete stage isolation. Three additional temporal-exclusion runs yielded 85.92 ± 1.38% Macro-F1. On a fixed S07 test set (26 scenes, 405 bulbs), replacing temporally neighboring training images with more distant same-stage images, while preserving training-scene and class-specific instance counts, changed Macro-F1 from 99.76% to 74.12%. In a three-seed, four-fold leave-one-stage-out (LOSO) evaluation, stage-based group distributionally robust optimization (Stage-GroupDRO) achieved 75.57% Stage-macro Macro-F1 versus 74.11% for class-weighted empirical risk minimization (Weighted-ERM), but the paired 95% confidence interval included zero. The full pipeline achieved 72.82% pooled end-to-end Macro-F1 at 19.32 frames per second (FPS) during offline evaluation. These results demonstrate substantial sensitivity to data partitioning within one acquisition session; external generalization, quantitative soil-removal efficiency, and closed-loop control remain untested.