Multispectral detection of citrus canker using hyperspectral band selection

Transactions of the ASABE

Volumn 54, Issue 6, 2011, Pages 2331-2341

  Qin, J ^a   Burks, T F ^a   Zhao, X ^a   Niphadkar, N ^a   Ritenour, M A ^b  

^a University of Florida   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

Author keywords

Band selection; Citrus canker; Disease detection; Image classification; Multispectral imaging

Indexed keywords

BAND SELECTION; CITRUS CANKER; CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; CORRELATION ANALYSIS; DETECTION CAPABILITY; DISEASE DETECTION; HYPERSPECTRAL; HYPERSPECTRAL IMAGE DATAS; HYPERSPECTRAL REFLECTANCE; INSECT DAMAGE; INTERNATIONAL MARKETS; MULTI-SPECTRAL; MULTI-SPECTRAL IMAGING SYSTEMS; MULTISPECTRAL DETECTION; MULTISPECTRAL IMAGE PROCESSING; MULTISPECTRAL IMAGING; PIXEL CLASSIFICATION; RATIO IMAGES; REGIONS OF INTEREST; SPECTRAL REGION;

CITRUS FRUITS; DIGITAL IMAGE STORAGE; IMAGE CLASSIFICATION; IMAGE PROCESSING; INTERNATIONAL TRADE; PIXELS;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; BACTERIAL DISEASE; CORRELATION; CROP DAMAGE; DETECTION METHOD; FRUIT; FUNGAL DISEASE; IMAGE CLASSIFICATION; IMAGING METHOD; MULTISPECTRAL IMAGE; PIXEL; REAL TIME; REMOTE SENSING; SPECTRAL REFLECTANCE; WAVELENGTH;

CITRUS; CITRUS X PARADISI; HEXAPODA;

EID: 84862941508     PISSN: 21510032     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (23)

1. Bajwa, S. G., P. Bajcsy, P. Groves, and L. F. Tian. 2004. Hyperspectral image data mining for band selection in agricultural applications. Trans. ASAE 47(3) : 895-907. (Pubitemid 39104278)
2. Blasco, J., N. Aleixos, J. Gómez-Sanchis, and E. Moltó. 2007. Citrus sorting by identification of the most common defects using multispectral computer vision. J. Food Eng. 83(3) : 384-393. (Pubitemid 47043198)
3. Chao, K., Y. R. Chen, W. R. Hruschka, and F. B. Gwozdz. 2002. On-line inspection of poultry carcasses by a dual-camera system. J. Food Eng. 51(3) : 185-192. (Pubitemid 33110481)
4. Cheng, X., Y. Tao, Y. R. Chen, and Y. Luo. 2003. NIR/MIR dual-sensor machine vision system for on-line apple stem-end/calyx recognition. Trans. ASAE 46(2) : 551-558. (Pubitemid 36832339)
5. Gómez-Sanchis, J., L. Gómez-Chova, N. Aleixos, G. Camps-Valls, C. Montesinos-Herrero, E. Moltó E, and J. Blasco. 2008. Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. J. Food Eng. 89(1) : 80-86.
6. Kim, M. S., A. M. Lefcourt, K. Chao, Y. R. Chen, I. Kim, and D. E. Chan. 2002. Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part I. Application of visible and near-infrared reflectance imaging. Trans. ASAE 45(6) : 2027-2037. (Pubitemid 37387786)
7. Kim, M. S., A. M. Lefcourt, Y. R. Chen, and Y. Tao. 2005. Automated detection of fecal contamination of apples based on multispectral fluorescence image fusion. J. Food Eng. 71(1) : 85-91. (Pubitemid 40710149)
8. Kleynen, R., V. Leemans, and M. F. Destain. 2005. Development of a multi-spectral vision system for the detection of defects on apples. J. Food Eng. 69(1) : 41-49. (Pubitemid 40200687)
9. Lee, K., S. Kang, S. R. Delwiche, M. S. Kim, and S. Noh. 2008. Correlation analysis of hyperspectral imagery for multispectral wavelength selection for detection of defects on apples. Sens. Instrum. Food Qual. Saf. 2(2) : 90-96.
10. Lu, R. 2004. Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biol. Tech. 31(2) : 147-157. (Pubitemid 38192856)
11. Miller, W. M., and G. P. Drouillard. 2001. Multiple feature analysis for machine vision grading of Florida citrus. Applied Eng. in Agric. 17(5) : 627-633. (Pubitemid 34307769)
12. Nakariyakul, S., and D. P. Casasent. 2008. Hyperspectral waveband selection for contaminant detection on poultry carcasses. Opt. Eng. 47(8) : 087202.
13. Niphadkar, N., T. F. Burks, J. Qin, and M. A. Ritenour. 2009. Estimation of detection limit for size of citrus canker lesions based on hyperspectral reflectance imaging. ASABE Paper No. 096254. St. Joseph, Mich.: ASABE.
14. Park, B., K. C. Lawrence, W. R. Windham, and R. J. Buhr. 2002. Hyperspectral imaging for detecting fecal and ingesta contaminants on poultry carcasses. Trans. ASAE 45(6) : 2017-2026. (Pubitemid 37387785)
15. Pydipati, R., T. F. Burks, and W. S. Lee. 2006. Identification of citrus disease using color texture features and discriminant analysis. Comput. Electron. Agric. 52 (1-2) : 49-59.
16. Qin, J., T. F. Burks, M. S. Kim, K. Chao, and M. A. Ritenour. 2008. Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sens. Instrum. Food Qual. Saf. 2(3) : 168-177.
17. Qin, J., T. F. Burks, M. A. Ritenour, and W. G. Bonn. 2009. Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. J. Food Eng. 93(2) : 183-191.
18. Schubert, T. S., S. A. Rizvi, X. A. Sun, T. R. Gottwald, J. H. Graham, and W. N. Dixon. 2001. Meeting the challenge of eradicating citrus canker in Florida - Again. Plant Dis. 85(4) : 340-356. (Pubitemid 32247779)
19. Thenkabail, P. S., R. B. Smith, and E. De Pauw. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ. 71(2) : 158-182. (Pubitemid 30092038)
20. Timmer, L. W., S. M. Garnsey, and J. H. Graham. 2000. Compendium of Citrus Diseases. 2nd ed. St. Paul, Minn. : American Phytopathological Society.
21. USDA-APHIS. 2009. Citrus canker: Movement of fruit from quarantined areas. Federal Register 74(203) : 54431-54445.
22. Xing, J. D. Guyer, D. Ariana, and R. Lu. 2008. Determining optimal wavebands using genetic algorithm for detection of internal insect infestation in tart cherry. Sens. Instrum. Food Qual. Saf. 2(3) : 161-167.
23. Zhao, X., T. F. Burks, J. Qin, and M. A. Ritenour. 2010. Effect of fruit harvest time on citrus canker detection using hyperspectral reflectance imaging. Sens. Instrum. Food Qual. Saf. 4(3) : 126-135.