Image-Based Assessment of External Quality Traits in Chinese Bayberry (Myrica rubra) Using a Deep Learning Detection and Multi-Trait Scoring Framework

Chinese bayberry (Myrica rubra) is an extensively popular fruit with unique flavors and high nutritional values. A precise and efficient method for assessing external quality traits of Chinese bayberry is critical for machine automatic sorting to ensure the quality and increase economic value. Here, we developed an automatic evaluation flow based on deep learning detection and a multi-trait scoring framework for postharvest quality grading of Chinese bayberry. Firstly, the YOLOv11 framework was applied to detect the Chinese bayberry fruits, and the network consists of a CSPDarknet-based backbone with C3k2 modules, an SPPF block, a bidirectional PANet neck, and three decoupled detection heads. The experimental results showed a precision of 0.9991 and a recall of 1.0000 for the waxberry class (mAP@0.5 = 0.9950, mAP@0.5:0.95 = 0.8632). Furthermore, the quality scoring model was built on a modified AlexNet backbone that provided more stable training and better accuracy compared to the other seven CNN models. The R2, MAE, and RMSE showed the close alignment between predicted and reference values, revealing reliable estimations of our six fruit-quality traits, including radius, color depth, color class, color uniformity, surface injury area ratio, and plumpness. This self-developed flow provides a comprehensive and effective assessment method to improve fruit quality and economic value, which further facilitates automated agricultural production.