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ARS Home » Research » Publications at this Location » Publication #222157
Title: CITRUS CANKER DETECTION USING HYPERSPECTRAL REFLECTANCE IMAGING AND PCA-BASED IMAGE CLASSIFICATION METHOD

Author
item QIN, JIANWEI - UNIV OF FLORIDA
item BURKE, THOMAS - UNIV OF FLORIDA
item Kim, Moon
item Chao, Kuanglin - Kevin Chao
item RITENOUR, MARK - UNIV OF FLORIDA

Submitted to: Sensing and Instrumentation for Food Quality and Safety
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/7/2008
Publication Date: 5/1/2008
Citation: Qin, J., Burke, T., Kim, M.S., Chao, K., Ritenour, M.A. 2008. Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sensing and Instrumentation for Food Quality and Safety. 2(3):168-177.

Interpretive Summary: Scientists at the Food Safety Laboratory, ARS have developed various nondestructive sensing devices to obtain the quality and safety attributes from agricultural products. In this investigation, we evaluated the potential of using hyperspectral imaging techniques for detecting canker lesions on citrus fruit. 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. A recently developed, portable hyperspectral imaging system was used to acquire reflectance images from citrus samples in the wavelength range between 400 nm and 900 nm with 99 spectral bands. 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 discriminate cankerous samples from normal and other diseased samples. The overall accuracy for canker detection was 92.7%. The hyperspectral imaging technique for canker disease detection presented in this paper is useful to food scientists, engineers, regulatory government agencies, and food processing industries.

Technical 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 lighting unit, 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 with 99 spectral bands. 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.