Deep learning based instance segmentation of mandarin fruit slices for precision assessment and morphological quantification

Accurate instance segmentation of mandarin fruit slices is essential for quantifying segment morphology and central core structure, which are key traits in cultivar evaluation, fruit quality assessment, and postharvest application. Manual measurement of these anatomical features, however, is time-consuming and prone to inconsistency. In this study, we developed an automated segmentation framework based on YOLOv8 to detect and semantic segment the fruit segments and the central core in high-resolution mandarin transversely cut images. The model was trained on a curated datasetderived from 58 original high-resolution cross-sectional images (5100 × 7019 pixels), which were systematically partitioned into 280 cropped sub-images (1712 × 1778 pixels), each containing a single complete citrus slice, and demonstrated excellent performance. YOLOv8 achieved near-perfect detection metrics, with bounding box metrics precision ~ 0.997, recall ~ 1.00, mAP50 ~ 0.995, and mAP50-95 ~ 0.922. Semantic segmentation accuracy was similarly strong, with precision = 0.997, Recall ~ 1.00, mAP50 ~ 0.995, and mAP50-95 = 0.965. Training and validation losses converged steadily, indicating stable learning without overfitting. The nonsignificant differences between YOLOv8-predicted measurements and ground truth data, together with low mean absolute error (MAE) values, demonstrate that the model not only performs well in semantic segmentation metrics but also maintains high accuracy in quantitative measurements, which is critical for cultivar discrimination and genetic studies. Our work provides a robust, high-precision, and reproducible framework for mandarin fruit slice phenotyping, offering significant potential for applications in agricultural research, breeding programs, and automated fruit quality evaluation.