Construction of a stable YOLOv8 classification model for apple bruising detection based on physicochemical property analysis and structured-illumination reflectance imaging

文献类型： 外文期刊

第一作者： Zhang, Junyi

作者： Zhang, Junyi;Chen, Liping;Cai, Zhonglei;Shi, Ruiyao;Cai, Letian;Li, Jiangbo;Zhang, Junyi;Luo, Liwei;Yang, Xuhai;Li, Jiangbo

作者机构：

关键词： Apple; Bruising detection; Physicochemical property analysis; Structured-illumination reflectance imaging; Deep learning model

期刊名称：POSTHARVEST BIOLOGY AND TECHNOLOGY （ 影响因子：6.8； 五年影响因子：7.5 ）

ISSN： 0925-5214

年卷期： 2025 年 219 卷

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收录情况： SCI

摘要： Effective and accurate detection of bruises at all stages has always been a challenge in non-destructive grading of apples. In this study, the visible structured-illumination reflectance imaging (SIRI) combing with deep learning method was proposed to identify bruised 'Fuji' apples at four different time stages (0, 6, 12 and 24 h). The macroscopic/microscopic structures and physicochemical properties of bruised tissue were measured and analyzed to determine the relationship between bruising time and these properties, as well as how they affect the accuracy of bruising detection. Results indicated that classification accuracy increased with the decrease of water and total phenolic content of the bruised tissue, as well as with the increase of color browning and bruised area. The YOLOv8 model achieved the highest detection accuracy (99.5 %) and stability. This research enhances understanding of apple bruise optics and aids in developing advanced nondestructive testing techniques.

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