FOOD SCIENCE ›› 2025, Vol. 46 ›› Issue (4): 268-277.doi: 10.7506/spkx1002-6630-20240729-279

• Safety Detection • Previous Articles    

Grading Method Based on Lightweight YOLOv8-FasterBlock Model for Green Arabica Coffee Bean Produced in Yunnan

YANG Hongxin, CHEN Yue, PEI Guoquan, QIAN Xueying, LI Peiyao, ZHU Caiying, XIA Qian, LIU Zigao, WU Wendou   

  1. (1. College of Food Science and Technology, Yunnan Agricultural University, Kunming 650201, China; 2. Yunnan Agricultural Mechanization Cadre School, Kunming 650011, China; 3. College of Big Data, Yunnan Agricultural University, Kunming 650201, China; 4. Yunnan Traceability Technology Co. Ltd., Kunming 650000, China)
  • Published:2025-02-07

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Abstract: To establish a grading method for green Arabica coffee bean based on the lightweight YOLOv8-FasterBlock model, a total of 500 g of first-grade, second-grade, third-grade and defective green Arabica coffee bean from Yunnan were collected and mixed for acquirement of RGB images as the dataset for coffee bean grading. The YOLOv8n model was improved by replacing the BottleneckBlock in the C2f module with the FasterBlock module in FasterNet, resulting in a new lightweight YOLOv8-FasterBlock model. The improved model took 2.4 ms, on average, to discriminate different grades of coffee beans with accuracy, recall, and average precision of 98.4%, 94.3%, and 97.4%, respectively. Comparison, ablation, lightweighting, and adherent bean tests proved the superiority and structural validity of the YOLOv8-FasterBlock model. The YOLOv8-FasterBlock model improved the feature extraction capacity and inference speed for green Arabica coffee bean while having reduced complexity, enabling rapid grading of coffee bean. The results of the study provide a reference for the deployment of vision module in green Arabica coffee bean grading equipment, and also provide theoretical support for the grading of other agricultural products.

Key words: Arabica coffee; green coffee beans; YOLOv8-FasterBlock model; target detection; grading

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