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Developing universal classification models for the detection of early decayed citrus by structured-illumination reflectance imaging coupling with deep learning methods

文献类型： 外文期刊

作者： Cai, Zhonglei 1 ; Sun, Chanjun 2 ; Zhang, Hailiang 4 ; Zhang, Yizhi 4 ; Li, Jiangbo 1 ;

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

2.Jiangsu Univ, Dept Stomatol, Affiliated Hosp, Zhenjiang 212001, Jiangsu, Peoples R China

3.ShiHeZi Univ, Coll Mech & Elect Engn, Shihezi, Peoples R China

4.East China Jiaotong Univ, Coll Elect & Automat Engn, Nanchang 330013, Peoples R China

5.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

关键词： Citrus; Early detection; Image processing; Universal classification model; Deep learning

期刊名称：POSTHARVEST BIOLOGY AND TECHNOLOGY （ 影响因子：7.0； 五年影响因子：6.9 ）

ISSN： 0925-5214

年卷期： 2024 年 210 卷

页码：

收录情况： SCI

摘要： Early detection of decay caused by fungal infection in citrus fruit is a major challenge for the citrus industry, as the decayed area is almost invisible on the surface of fruit. This study constructed a new detection system for structural illumination imaging combined with light-emitting diode (LED) lamp and a monochrome camera. The direct component (DC) and alternating component (AC) images were recovered by demodulating three phaseshifting pattern images under the spatial frequency of 0.25 cycles mm(--1). Compared with the DC image, the decayed area can be clearly displayed in the AC image and ratio image (i.e. AC/DC). For independent models, the classification accuracy of the decayed oranges and sugar mandarins reached 92.5% and 95.0% by combining RT images with convolutional neural network (CNN) method, respectively. However, it is time-consuming and labor-intensive to construct different models to predict the corresponding citrus variety. Thus, this study also explored the feasibility of establishing the universal classification model suitable for various citrus fruit. The classification performance of partial least square discriminant analysis and CNN models was evaluated and compared. Among all universal models, the CNN model exhibited superior performance with classification accuracies of 95.0% for independent test set including two varieties of citrus fruit (orange and sugar mandarin). For four types of citrus (orange, sugar mandarin, dekopon and Nanfeng sweet mandarin), the overall classification accuracy of the universal model was 90.6%. This study demonstrated that different varieties of early decayed citrus can be effectively identified by constructing a universal CNN model combined with structured-illumination reflectance imaging technology.

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