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An OCRNet-Based Method for Assessing Apple Watercore in Cold and Cool Regions of Yunnan Province

Author

Listed:
  • Yaxing Ma

    (College of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Yushuo Tan

    (Modern Postal College, Shijiazhuang Posts and Telecommunications Technical College, Shijiazhuang 050021, China)

  • Wenbin Zhang

    (College of Mechanical and Electrical Engineering, Kunming University, Kunming 650214, China)

  • Zhipeng Yin

    (College of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Chunlin Zhao

    (College of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Panpan Guo

    (School of Rail Transportation, Soochow University, Suzhou 215131, China)

  • Haijian Wu

    (College of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Ding Hu

    (College of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China)

Abstract

The content of the watercore in apples plays a decisive role in their taste and selling price, but there is a lack of methods to accurately assess it. Therefore, this paper proposes an OCRNet-based method for apple watercore content evaluation. A total of 720 watercores of apples from Mengzi, Lijiang, and Zhaotong City in Yunnan Province were used as experimental samples. An appropriate watercore extraction model was selected based on different evaluation indicators. The watercore feature images extracted using the optimal model were stacked, and the watercore content of apples in different regions was evaluated by calculating the fitted area of the stacked watercore region. The results show that the OCRNet model is optimal in all evaluation metrics when facing different datasets. The error of OCRNet is also minimized when extracting overexposed as well as underexposed images with 0.15% and 0.38%, respectively, and it can be used to extract the characteristics of the apple watercore. The evaluation result of the watercore content of apples in different regions is that Lijiang apples have the highest watercore content, followed by Mengzi apples, and Zhaotong apples have the least watercore content.

Suggested Citation

  • Yaxing Ma & Yushuo Tan & Wenbin Zhang & Zhipeng Yin & Chunlin Zhao & Panpan Guo & Haijian Wu & Ding Hu, 2025. "An OCRNet-Based Method for Assessing Apple Watercore in Cold and Cool Regions of Yunnan Province," Agriculture, MDPI, vol. 15(10), pages 1-19, May.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:10:p:1040-:d:1653439
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