FAnet: A lightweight end-to-end framework for Fructus Aurantii identification and quality grading

The quality grade of Fructus Aurantii(FA) is closely related to its clinical efficacy and medication safety; therefore, a standardized grading method is needed. 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. The results indicate that FAnet can support automated FA segmentation and preliminary grading under controlled imaging conditions. Broader validation across larger and more diverse sample populations is still required before deployment-oriented application.