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Two-wavelength image detection of early decayed oranges by coupling spectral classification with image processing

文献类型： 外文期刊

作者： Li, Jiangbo 1 ; Luo, Wei 3 ; Han, Lvhua 1 ; Cai, ZhongLei 2 ; Guo, Zhiming 1 ;

作者机构： 1.Jiangsu Univ, Sch Food & Biol Engn, Key Lab Modern Agr Equipment & Technol, Minist Educ, Zhenjiang 212000, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

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

4.Jiangsu Univ, Jiangsu Educ Dept, Int Joint Res Lab Intelligent Agr & Agri Prod Proc, Zhenjiang 212013, Peoples R China

关键词： Identification of decayed orange; Hyperspectral imaging; Wavelength image selection; Classification model construction; Spectrum and image fusion

期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS （ 影响因子：4.52； 五年影响因子：4.942 ）

ISSN： 0889-1575

年卷期： 2022 年 111 卷

页码：

收录情况： SCI

摘要： Citrus decay is one of the most serious postharvest diseases, which can cause great economic losses and food safety problem. Early decay has no obvious features, which makes rapid detection and grading of decayed citrus fruit a major challenge for citrus industry. This study utilized the unique image-spectrum fusion property of hyperspectral imaging, and proposed a strategy to quickly identify the citrus with early decay using only two wavelength images. The typical average spectra of sound and decayed tissues were extracted. The linear partial least squares-discriminant analysis (PLS-DA) and nonlinear back-propagation artificial neural network (BP-ANN) models were constructed for classifying two types of tissues. MC-UVE-SPA algorithm by combining Monte Carlo cross-validation (MC-UVE) with successive projections algorithm (SPA) was used to extract 16 variables characterizing two types of tissues. The wavelength images corresponding to the extracted variables were performed principal component analysis (PCA) to find the optimal PC image. Only two wavelength images at 568.8 nm and 771.2 nm were selected by analyzing the weighting coefficients of the third principal component (PC3) image. An improved watershed segmentation method was proposed to segment decay region in oranges based on PC2 image of the selected two wavelength images. Classification performance of the proposed algorithm was evaluated by all samples. The results showed that the overall classification accuracy of 96.6% was achieved, with 100% and 91.3% for the decayed and sound oranges respectively. The proposed detection strategy only involves two wavelength images, which will contribute to the establishment of a fast and low-cost multispectral imaging system for detection of citrus with early decay.

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