<p>Ablation experiment result.</p>

However, FA samples show considerable morphological variation, and conventional grading depends largely on manual visual assessment, which is subjective and inefficient. This study proposes FAnet, a lightweight instance segmentation framework based on YOLOv8n-seg, for estimating peel thickness and outer diameter during FA quality grading.

FAnet integrates three task-adapted components: C2Star, which combines StarNet and C2f to improve multi-scale feature extraction; the Simple Parameter-Free Attention Module (SimAM), which enhances spatial-channel feature weighting without introducing additional trainable parameters; and a multi-scale dynamic fitting module (MSDFM), which maps segmentation-derived areas to physical dimensions for grading. Using a self-built dataset containing 1,093 original images and 5,035 augmented training images, FAnet achieved 96.12% precision, 98.77% recall, 98.60% mAP@50, and 77.08% mAP@95, with 2.97 M parameters and 7.9 GFLOPs under the present experimental setting.