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Proceedings Paper

Detecting citrus canker by hyperspectral reflectance imaging and PCA-based image classification method
Author(s): Jianwei Qin; Thomas F. Burks; Moon S. Kim; Kuanglin Chao; Mark A. Ritenour
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Paper Abstract

Citrus canker is one of the most devastating diseases that threaten citrus crops. Technologies that can efficiently identify citrus canker would assure fruit quality and safety and enhance the competitiveness and profitability of the citrus industry. This research was aimed to investigate the potential of using hyperspectral imaging technique for detecting canker lesions on citrus fruit. A portable hyperspectral imaging system consisting of an automatic sample handling unit, a light source, and a hyperspectral imaging unit was developed for citrus canker detection. The imaging system was used to acquire reflectance images from citrus samples in the wavelength range between 400 nm and 900 nm. Ruby Red grapefruits with normal and various diseased skin conditions including canker, copper burn, greasy spot, wind scar, cake melanose, and specular melanose were tested. Hyperspectral reflectance images were analyzed using principal component analysis (PCA) to compress the 3-D hyperspectral image data and extract useful image features that could be used to discriminate cankerous samples from normal and other diseased samples. Image processing and classification algorithms were developed based upon the transformed images of PCA. The overall accuracy for canker detection was 92.7%. This research demonstrated that hyperspectral imaging technique could be used for discriminating citrus canker from other confounding diseases.

Paper Details

Date Published: 15 April 2008
PDF: 11 pages
Proc. SPIE 6983, Defense and Security 2008: Special Sessions on Food Safety, Visual Analytics, Resource Restricted Embedded and Sensor Networks, and 3D Imaging and Display, 698305 (15 April 2008); doi: 10.1117/12.786866
Show Author Affiliations
Jianwei Qin, Univ. of Florida (United States)
Thomas F. Burks, Univ. of Florida (United States)
Moon S. Kim, USDA Agricultural Research Service (United States)
Kuanglin Chao, USDA Agricultural Research Service (United States)
Mark A. Ritenour, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 6983:
Defense and Security 2008: Special Sessions on Food Safety, Visual Analytics, Resource Restricted Embedded and Sensor Networks, and 3D Imaging and Display
Moon S. Kim; Kaunglin Chao; William J. Tolone; William Ribarsky; Sergey I. Balandin; Bahram Javidi; Shu-I Tu, Editor(s)

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