Geographical classification of apple based on hyperspectral imaging

文献类型： 外文期刊

第一作者： Guo, Zhiming

作者： Guo, Zhiming;Huang, Wenqian;Chen, Liping;Zhao, Chunjiang

作者机构：

关键词： hyperspectral imaging;geographical origin;apple;feature extraction;support vector machine

期刊名称：SENSING FOR AGRICULTURE AND FOOD QUALITY AND SAFETY V

ISSN： 0277-786X

年卷期： 2013 年 8721 卷

页码：

收录情况： SCI

摘要： Attribute of apple according to geographical origin is often recognized and appreciated by the consumers. It is usually an important factor to determine the price of a commercial product. Hyperspectral imaging technology and supervised pattern recognition was attempted to discriminate apple according to geographical origins in this work. Hyperspectral images of 207 Fuji apple samples were collected by hyperspectral camera (400-1000nm). Principal component analysis (PCA) was performed on hyperspectral imaging data to determine main efficient wavelength images, and then characteristic variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) from dominant waveband image. All characteristic variables were obtained by fusing the data of images in efficient spectra. Support vector machine (SVM) was used to construct the classification model, and showed excellent performance in classification results. The total classification rate had the high classify accuracy of 92.75% in the training set and 89.86% in the prediction sets, respectively. The overall results demonstrated that the hyperspectral imaging technique coupled with SVM classifier can be efficiently utilized to discriminate Fuji apple according to geographical origins.

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