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Detection of bruising in pear with varying bruising degrees and formation times by using SIRI technique combining with texture feature-based LS-SVM and ResNet-18-based CNN model

文献类型： 外文期刊

作者： Li, Jiangbo 1 ; Zhang, Junyi 1 ; Mei, Mengwen 1 ; Diao, Zhihua 2 ; Li, Xuetong 1 ; Shi, Ruiyao 1 ; Cai, Zhonglei 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

2.Zhengzhou Univ Light Ind, Coll Elect & Informat Engn, Zhengzhou 450002, Peoples R China

关键词： Pears; Bruising detection; Convolutional neural network; Machine learning; Enhanced imaging

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 223 卷

页码：

收录情况： SCI

摘要： Bruising negatively affects the appearance and sensory quality of fruit, weakening their commercial value. Optical non-destructive testing of bruised fruit is an important research topic. However, the degree and formation time of bruising have a significant impact on detection accuracy, which poses a challenge for accurately detecting bruised fruit. This study attempted to use structured-illumination reflectance imaging (SIRI) to detect bruised pears with different degrees and formation times. Structured-illumination images of pears with three bruising levels (S1, S2 and S3) and five bruising formation times (0, 6, 24, 48 and 72 hours) were collected. Two machine learning models including texture feature-based least squares support vector machine (LS-SVM) and ResNet-18-based convolutional neural network (CNN) were established using different input images (DC, AC, RT, DC-AC and DC-AC-RT). Study indicated that AC and RT images have depth resolution capability, achieving good results for detecting pear bruises within 24 hours combining with two types of models, showing that the powerful detection capability of SIRI technology for early bruising. Compared to LS-SVM model, the ResNet-18-based CNN model obtained better detection performance, and its detection accuracy was not significantly affected by the bruising degree and formation time. For all samples, ResNet-18-based CNN model, combined with AC and RT images, achieved an overall detection accuracy of 92.5 % and 94.3 %, respectively. This study provided a valuable solution for accurate identification of bruised pears with varying bruising degrees and times.

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