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Agriculture
Volume 15
Issue 10
10.3390/agriculture15101040
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Yaxing Ma
1,
Yushuo Tan
2,*,
Wenbin Zhang
3,*,
Zhipeng Yin
1,
Chunlin Zhao
1,
Panpan Guo
4,
Haijian Wu
1 and
Ding Hu
1
1
College of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
2
Modern Postal College, Shijiazhuang Posts and Telecommunications Technical College, Shijiazhuang 050021, China
3
College of Mechanical and Electrical Engineering, Kunming University, Kunming 650214, China
4
School of Rail Transportation, Soochow University, Suzhou 215131, China
*
Authors to whom correspondence should be addressed.
Agriculture 2025, 15(10), 1040; https://doi.org/10.3390/agriculture15101040 (registering DOI)
Submission received: 29 March 2025 / Revised: 19 April 2025 / Accepted: 9 May 2025 / Published: 11 May 2025
(This article belongs to the Section Digital Agriculture)
Versions Notes

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.
Keywords: apple watercore; feature extraction; OCRNet; evaluation of watercore content; neural network apple watercore; feature extraction; OCRNet; evaluation of watercore content; neural network

Share and Cite

MDPI and ACS Style

Ma, Y.; Tan, Y.; Zhang, W.; Yin, Z.; Zhao, C.; Guo, P.; Wu, H.; Hu, D. An OCRNet-Based Method for Assessing Apple Watercore in Cold and Cool Regions of Yunnan Province. Agriculture 2025, 15, 1040. https://doi.org/10.3390/agriculture15101040

AMA Style

Ma Y, Tan Y, Zhang W, Yin Z, Zhao C, Guo P, Wu H, Hu D. An OCRNet-Based Method for Assessing Apple Watercore in Cold and Cool Regions of Yunnan Province. Agriculture. 2025; 15(10):1040. https://doi.org/10.3390/agriculture15101040

Chicago/Turabian Style

Ma, Yaxing, Yushuo Tan, Wenbin Zhang, Zhipeng Yin, Chunlin Zhao, Panpan Guo, Haijian Wu, and Ding Hu. 2025. "An OCRNet-Based Method for Assessing Apple Watercore in Cold and Cool Regions of Yunnan Province" Agriculture 15, no. 10: 1040. https://doi.org/10.3390/agriculture15101040

APA Style

Ma, Y., Tan, Y., Zhang, W., Yin, Z., Zhao, C., Guo, P., Wu, H., & Hu, D. (2025). An OCRNet-Based Method for Assessing Apple Watercore in Cold and Cool Regions of Yunnan Province. Agriculture, 15(10), 1040. https://doi.org/10.3390/agriculture15101040

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