Journal
INTERNATIONAL JOURNAL OF FOOD PROPERTIES
Volume 21, Issue 1, Pages 1598-1607Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/10942912.2018.1503299
Keywords
Apple bruises; hyperspectral image; adaBoost; Correlation based feature selection (CFS); Independent component analysis (ICA)
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Hyperspectral imaging technique (400-1000nm) was used for rapid and nondestructive recognition of bruises of apples. A total of 324 hyperspectral images were collected from 108 Fuji apples and the average spectral reflectance was extracted from the region of interest (ROI) of each image. The classification results of AdaBoost for the data pretreated by various existing methods were compared. Then, the correlation-based feature selection (CFS) algorithm was used to obtain characteristic wavelengths for reducing data redundancy. After pretreating with multiplicative scatter correction (MSC) and CFS, the average accuracy of the selected wavelengths was 97.63%. Then, an image processing algorithm based on the characteristic wavelengths selected before was proposed for the visual discrimination of bruises. This algorithm performed independent component analysis (ICA) transformation of the selected wavelengths, and chose the third component image of the ICA transform, then used adaptive threshold segmentation to obtain the bruise region of apples. The results showed that hyperspectral imaging technology could discriminate apple bruise, and this study can help to develop an online apple bruises detection system.
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