Detection of bruised potatoes using hyperspectral imaging technique based on discrete wavelet transform

Abstract In the view of difficulties in bruise detection of potatoes, a texture recognition technique based on hyperspectral imaging and discrete wavelet transform was presented and discussed in this paper. Firstly, characteristic bands of intact and damaged potato samples based on the principal component analysis (PCA) were obtained. Then, the PCA method was used again to generate the principal component images that make it possible to distinguish the bruised potatoes from the healthy ones. In order to enhance the texture characteristics of the damaged samples, histogram equalization, mean filter and gradient method were utilized to preprocess the reference image. The paper achieved the best image damage recognition effects with the method of histogram equalization. Next, the preprocessed images were discomposed by the paper to obtain sub-band images using a two-level discrete wavelet transform (DWT), generating gray level co-occurrence matrix (GLCM) from sub-band images and texture feature data. Finally, the optimal parameters of gray level co-occurrence matrix were found out with the method of grid search and AdaBoost-Fisher algorithm to reduce the dimension of texture data generated under each parameter. The following results were obtained experimentally: the highest damage identification rate was 99.82%, the data dimensions were reduced from 56 to 4, the picture gotten from WT was reduced from 7 to 4, and the optimal parameter combination was obtained when the prediction effect was best after dimensionality reduction. The results show that the hyperspectral image texture recognition technology based on DWT-GLCM can accurately identify slightly damaged potato samples, providing a reference for potato on-line non-destructive testing.

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