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

Yaxing Ma, Yushuo Tan (), Wenbin Zhang (), Zhipeng Yin, Chunlin Zhao, Panpan Guo, Haijian Wu and Ding Hu
Additional contact information
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

Agriculture, 2025, vol. 15, issue 10, 1-19

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 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/10/1040/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/10/1040/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:10:p:1040-:d:1653439

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().