Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features

Biosystems Engineering

Volumn 103, Issue 2, 2009, Pages 137-145

  Blasco, J ^a   Aleixos, N ^b   Gomez Sanchis J ^c   Molto E ^a  

^a INSTITUTO VALENCIANO DE INVESTIGACIONES AGRARIAS IVIA   (Spain)

^b UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

^c IDAL   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER VISION; INFRARED DEVICES;

AUTOMATIC INSPECTION; COMPUTER VISION SYSTEM; DETECTING DEFECTS; MORPHOLOGICAL FEATURES; MULTI-SPECTRAL DATA; NEAR INFRA-RED REFLECTANCES; SPECTRAL INFORMATION; SUPERVISED METHODS;

CITRUS FRUITS;

CITRUS;

EID: 67349265572     PISSN: 15375110     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystemseng.2009.03.009     Document Type: Article

References (28)

1. Alchanatis V., Peleg K., and Ziv M. Classification of tissue culture segments by colour machine vision. Journal of Agricultural Engineering Research 55 (1999) 299-311
2. Aleixos N., Blasco J., Navarrón F., and Moltó E. Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Computers and Electronics in Agriculture 33 2 (2002) 121-137
3. Al-Mallahi A., Kataoka T., and Okamoto H. Discrimination between potato tubers and clods by detecting the significant wavebands. Biosystems Engineering 100 3 (2008) 329-337
4. Ariana D., Guyer D.E., and Shrestha S. Integrating multispectral reflectance and fluorescence imaging for defect detection on apples. Computers and Electronics in Agriculture 50 (2006) 148-161
5. Batchelor B.G., and Whelan P.F. Real-time colour recognition in symbolic programming for machine vision systems. Machine Vision and Applications 8 6 (1995) 385-398
6. Bennedsen B.S., Peterson D.L., and Tabb A. Identifying defects in images of rotating apples. Computers and Electronics in Agriculture 48 2 (2005) 92-102
7. Bennedsen B.S., and Peterson D.L. Performance of a system for apple surface defect identification in near-infrared images. Biosystems Engineering 90 4 (2005) 419-431
8. Blasco J., Aleixos N., Roger J.M., Rabatel G., and Moltó E. Robotic weed control using machine vision. Biosystems Engineering 83 2 (2002) 149-157
9. Blasco J., Aleixos N., and Moltó E. Machine vision system for automatic quality grading of fruit. Biosystems Engineering 85 4 (2003) 415-423
10. Blasco J., Aleixos N., Gómez J., and Moltó E. Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering 83 3 (2007) 384-393
11. Blasco J., Aleixos N., and Moltó E. Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm. Journal of Food Engineering 81 3 (2007) 535-543
12. Congalton R.G., and Green K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practice. Mapping Sciences Series (1999), CRC Press Inc, New York (USA)
13. Duda R.O., and Hart P.E. Pattern Classification and Scene Analysis (1973), John Willey & Sons, New York (USA)
14. Fleiss J.L. Measuring nominal scale agreement among many raters. Psychological Bulletin 76 5 (1971) 378-382
15. Gómez-Sanchis J., Gómez-Chova L., Aleixos N., Camps-Valls G., Montesinos-Herrero C., Moltó E., and Blasco J. Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. Journal of Food Engineering 89 1 (2008) 80-86
16. Gómez-Sanchis J., Moltó E., Camps-Valls G., Gómez-Chova L., Aleixos N., and Blasco J. Automatic correction of the effects of the light source on spherical objects. An application to the analysis of hyperspectral images of citrus fruits. Journal of Food Engineering 85 2 (2008) 191-200
17. Gonzalez R.C., and Woods R.E. Digital Image Processing. second ed. (2002), Prentice Hall, UpperSaddle River, NJ (USA)
18. Kleynen O., Leemans V., and Destain M.F. Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering 69 (2005) 41-49
19. Leemans V., and Destain M.F. A real-time grading method of apples based on features extracted from defects. Journal of Food Engineering 61 (2004) 83-89
20. Li Q., Wang M., and Gu W. Computer vision based system for apple surface defect detection. Computers and Electronics in Agriculture 36 (2002) 215-223
21. Mehl P.M., Chao K., Kim M., and Chen Y.R. Detection of defects on selected apple cultivars using hyperspectral and multispectral image analysis. Applied Engineering in Agriculture 18 2 (2000) 219-226
22. Ponsa D., Benavente R., Lumbreras F., Martínez J., and Roca X. Quality control of safety belts by machine vision inspection for real-time production. Optical Engineering 42 4 (2003) 1114-1120
23. Sim J., and Wright C.C. The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Physical Therapy 85 3 (2005) 206-282
24. Tao Y., Morrow C.T., Heinemann P.H., and Sommer H.J. Fourier-based separation technique for shape grading of potatoes using machine vision. Transactions of the ASAE 38 3 (1995) 949-957
25. Tenenbaum R.A. Fundamentals of Applied Dynamics (2004), Springer, New York, (USA)
26. Unay D., and Gosselin B. Automatic defect segmentation of 'Jonagold' apples on multi-spectral images: a comparative study. Postharvest Biology and Technology 42 (2006) 271-279
27. Wen Z., and Tao Y. Building rule-based machine vision system for defect inspection on apple sorting and packing lines. Expert Systems with Applications 16 (1999) 307-313
28. Zheng C., Sun D.W., and Zheng L. Recent developments and applications of image features for food quality evaluation and inspection e a review. Trends in Food Science & Technology 17 (2006) 642-655