From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods

文献类型： 外文期刊

第一作者： Lu, Junhua

作者： Lu, Junhua;Liao, Hongsen;Chen, Jiawei;Yang, Yuxin;Zhang, Mei;Hu, Yongsong;Ma, Wei;Tian, Zhiwei

作者机构：

关键词： machine vision; near infrared technology; fruits and vegetables; grading

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 10 期

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收录情况： SCI

摘要： Machine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. The characteristics of fruits and vegetables can easily be automatically distinguished by these two technologies, such as appearance, shape, color and texture. Nondestructive testing is reasonably used for image processing and pattern recognition, and can meet the identification and grading of single features and fusion features in production. Through the summary and analysis of the fruits and vegetable grading technology in the past five years, the results show that the accuracy of machine vision for fruits and vegetable size grading is 70-99.8%, the accuracy of external defect grading is 88-95%, and the accuracy of NIR and hyperspectral internal detection grading is 80.56-100%. Comprehensive research on multi-feature fusion technology in the future can provide comprehensive guidance for the construction of automatic integrated grading of fruits and vegetables, which is the main research direction of fruits and vegetable grading in the future.

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