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Proceedings Paper

Geographical classification of apple based on hyperspectral imaging
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Paper Abstract

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.

Paper Details

Date Published: 29 May 2013
PDF: 8 pages
Proc. SPIE 8721, Sensing for Agriculture and Food Quality and Safety V, 87210J (29 May 2013); doi: 10.1117/12.2015559
Show Author Affiliations
Zhiming Guo, Beijing Academy of Agriculture and Forestry Sciences (China)
China Agricultural Univ. (China)
Wenqian Huang, Beijing Academy of Agriculture and Forestry Sciences (China)
Liping Chen, Beijing Academy of Agriculture and Forestry Sciences (China)
National Engineering Research Ctr. of Intelligent Equipment for Agriculture (China)
Chunjiang Zhao, Beijing Academy of Agriculture and Forestry Sciences (China)
National Engineering Research Ctr. of Intelligent Equipment for Agriculture (China)
Yankun Peng, China Agricultural Univ. (China)


Published in SPIE Proceedings Vol. 8721:
Sensing for Agriculture and Food Quality and Safety V
Moon S. Kim; Shu-I Tu; Kuanglin Chao, Editor(s)

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