Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction
Abstract The assessment of apple quality is pivotal in agricultural production management, and apple ripeness is a key determinant of apple quality. This paper proposes an approach for assessing apple ripeness from both structured and unstructured observation data, i.e., text and images. For structu...
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| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-91939-4 |
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| author | Yuchi Li Zhigao Wang Aiwei Yang Xiaoqi Yu |
| author_facet | Yuchi Li Zhigao Wang Aiwei Yang Xiaoqi Yu |
| author_sort | Yuchi Li |
| collection | DOAJ |
| description | Abstract The assessment of apple quality is pivotal in agricultural production management, and apple ripeness is a key determinant of apple quality. This paper proposes an approach for assessing apple ripeness from both structured and unstructured observation data, i.e., text and images. For structured text data, support vector regression (SVR) models optimized using the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) were utilized to predict apple ripeness, with the WOA-optimized SVR demonstrating exceptional generalization capabilities. For unstructured image data, an Enhanced-YOLOv8+, a modified YOLOv8 architecture integrating Detect Efficient Head (DEH) and Efficient Channel Attention (ECA) mechanism, was employed for precise apple localization and ripeness identification. The synergistic application of these methods resulted in a significant improvement in prediction accuracy. These approaches provide a robust framework for apple quality assessment and deepen the understanding of the relationship between apple maturity and observed indicators, facilitating more informed decision-making in postharvest management. |
| format | Article |
| id | doaj-art-b1b9ccf120a148cd85b6a64e91f4d23a |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b1b9ccf120a148cd85b6a64e91f4d23a2025-08-20T02:16:40ZengNature PortfolioScientific Reports2045-23222025-03-0115112010.1038/s41598-025-91939-4Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness predictionYuchi Li0Zhigao Wang1Aiwei Yang2Xiaoqi Yu3School of Labor Economics, China University of Labor RelationsSchool of Computing, China University of Labor RelationsSchool of Computing, China University of Labor RelationsSchool of Labor Economics, China University of Labor RelationsAbstract The assessment of apple quality is pivotal in agricultural production management, and apple ripeness is a key determinant of apple quality. This paper proposes an approach for assessing apple ripeness from both structured and unstructured observation data, i.e., text and images. For structured text data, support vector regression (SVR) models optimized using the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) were utilized to predict apple ripeness, with the WOA-optimized SVR demonstrating exceptional generalization capabilities. For unstructured image data, an Enhanced-YOLOv8+, a modified YOLOv8 architecture integrating Detect Efficient Head (DEH) and Efficient Channel Attention (ECA) mechanism, was employed for precise apple localization and ripeness identification. The synergistic application of these methods resulted in a significant improvement in prediction accuracy. These approaches provide a robust framework for apple quality assessment and deepen the understanding of the relationship between apple maturity and observed indicators, facilitating more informed decision-making in postharvest management.https://doi.org/10.1038/s41598-025-91939-4Apple ripenessSVR optimizationEvolutionary algorithmsEnhanced-YOLOv8+Postharvest management |
| spellingShingle | Yuchi Li Zhigao Wang Aiwei Yang Xiaoqi Yu Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction Scientific Reports Apple ripeness SVR optimization Evolutionary algorithms Enhanced-YOLOv8+ Postharvest management |
| title | Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction |
| title_full | Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction |
| title_fullStr | Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction |
| title_full_unstemmed | Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction |
| title_short | Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction |
| title_sort | integrating evolutionary algorithms and enhanced yolov8 for comprehensive apple ripeness prediction |
| topic | Apple ripeness SVR optimization Evolutionary algorithms Enhanced-YOLOv8+ Postharvest management |
| url | https://doi.org/10.1038/s41598-025-91939-4 |
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