Digital microscopic imaging for citrus peel disease classification using color texture features

Applied Engineering in Agriculture

Volumn 25, Issue 5, 2009, Pages 769-776

  Zhao, Xuhui ^a   Burks, Thomas F ^a   Qin, Jianwei ^a   Ritenour, Mark A ^b  

^a J Crayton Pruitt Family Department of Biomedical Engineering   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

Author keywords

Citrus; Color co occurrence method; Discriminate classification; Disease control; Microscope; Texture feature

Indexed keywords

A-STABILITY; CITRUS; CITRUS PEEL; CLASSIFICATION ACCURACY; CLASSIFICATION MODELS; COLOR CO-OCCURRENCE METHOD; COLOR TEXTURE FEATURES; DATA SETS; DIGITAL MICROSCOPES; DISCRIMINATE CLASSIFICATION; DISEASE CLASSIFICATION; ECONOMIC LOSS; FEATURE MODELS; FLORIDA; FRUIT QUALITY; INSECT DAMAGE; MACHINE VISION; MICROSCOPIC IMAGE; MICROSCOPIC IMAGING; REGION-OF-INTEREST IMAGES; RGB IMAGES; STANDARD DEVIATION; STEPWISE DISCRIMINANT ANALYSIS; TEXTURE FEATURE; TEXTURE FEATURES; TIMELY IDENTIFICATION; TRAINING AND TESTING;

AGRICULTURAL MACHINERY; COLOR; COMPUTER VISION; DIGITAL DEVICES; DISCRIMINANT ANALYSIS; DISEASE CONTROL; FRUITS; MICROSCOPES; MICROSCOPIC EXAMINATION;

TEXTURES;

CITRUS; CITRUS X PARADISI; HEXAPODA;

EID: 70350751322     PISSN: 08838542     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (20)

1. Aleixos, N., J. Blasco, F. Navarron, and E. Molto. 2002. Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Comput. Electron. Agric. 33(2): 121-137.
2. Blasco, J., N. Aleixos, J. Gomez, and E. Molto. 2007. Citrus sorting by identification of the most common defects using multispectral computer vision. J. Food Eng. 83(3): 384-393.
3. Burks, T. F., S. A. Shearer, and F. A. Payne. 2000. Classification of weed species using color texture features and discriminant analysis. Trans. ASABE 43(2): 441-448.
4. Castleman, K. R. 1998. Concepts in imaging and microscopy: Color image processing for microscopy. Biol. Bull. 194(2): 100-107.
5. Chandraratne, M. R., D. Kulasiri, and S. Samarasinghe. 2007. Classification of lamb carcass using machine vision: Comparison of statistical and neural network analyses. J. Food Eng. 82(1): 26-34.
6. Edwards, J. G., and C. H. Sweet. 1986. Citrus blight assessment using a microcomputer: quantifying damage using an apple computer to solve reflectance spectra of entire trees. Fla. Sci. 49(1): 48-53.
7. Everitt, B. S., and G. Dunn. 2001. Applied Multivariate Data Analysis, 2nd ed. London, U.K.: Arnold.
8. FDACS. 2008. Florida agriculture statistical highlights. DACS-P-00054. Tallahassee, Fla.: Florida Department of Agriculture and Consumer Services.
9. Gaffney, J. J. 1973. Reflectance properties of citrus fruits. Trans. ASAE 16(2): 310-314.
10. Haralick, R. M., K. Shanmugam, and I. Dinstein. 1973. Textural features for image classification. IEEE Syst. Man. Cyb. 3(6): 610-621.
11. Kavdir, I., and D. E. Guyer. 2004. Comparison of artificial neural networks and statistical classifiers in apple sorting using textural features. Biosyst. Eng. 89(3): 331-344.
12. Li, J., J. Tan, and P. Shatadal. 2001. Classification of tough and tender beef by image texture analysis. Meat Sci. 57(4): 341-346.
13. Miller, W. M., and G. P. Drouillard. 2001. Multiple feature analysis for machine vision grading of Florida citrus. Applied Eng. Agric. 17(5): 627-633.
14. Park, B., and Y. R. Chen. 2001. Co-occurrence matrix texture features of multi-spectral images on poultry carcasses. J. Agric. Eng. Res. 78(2): 127-139.
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. & Instrumen. Food Qual. 2(3): 168-177.
17. Rao, C. R. 1973. Linear Statistical Inference and its Application. New York: John Wiley and Sons.
18. SAS. 2004. SAS/STAT 9.1 User's Guide. Cary, N.C.: SAS Institute, Inc.
19. Shearer, S. A., and R. G. Holmes. 1990. Plant identification using color co-occurrence matrices. Trans. ASAE 33(6): 2037-2044.
20. Xin, J., H. W. Beck, L. A. Halsey, J. H. Fletcher, F. S. Zazueta, and T. Momol. 2001. Development of a distance diagnostic and identification system for plant, insect, and disease problems. Applied Eng. Agric. 17(4): 561-565.