Computer Vision Applied to Flower, Fruit and Vegetable Processing
This paper presents the theoretical background and
the real implementation of an automated computer system to
introduce machine vision in flower, fruit and vegetable processing
for recollection, cutting, packaging, classification, or fumigation
tasks. The considerations and implementation issues presented in this
work can be applied to a wide range of varieties of flowers, fruits and
vegetables, although some of them are especially relevant due to the
great amount of units that are manipulated and processed each year
over the world. The computer vision algorithms developed in this
work are shown in detail, and can be easily extended to other
applications. A special attention is given to the electromagnetic
compatibility in order to avoid noisy images. Furthermore, real
experimentation has been carried out in order to validate the
developed application. In particular, the tests show that the method
has good robustness and high success percentage in the object
characterization.
[1] FruitLogistica informs, 2003-2008. Agrooglyad: Vegetables and Fruits.
http://www.lol.org.ua/eng/
[2] P. C. Condlife, M. R. Davey, B. J. Power, H. Koehorst-Van Putten, and
P. B. Visser, "An optimised protocol for rose transformation applicable
to different cultivars," in XXI International Eucarpia Symposium on
Classical versus Molecular Breeding of Ornamentals, M├╝nchen, 2003.
[3] J. Blasco, N. Aleixos, J. Gomez, and E. Molto, "Citrus sorting by
identification of the most common defects using multispectral computer
vision," Journal of Food Engineering, vol. 83, no. 3, pp. 384-393, 2007.
[4] J. Blasco, S. Cubero, J. Gomez-Sanchis, P. Mira, and E. Molto,
"Development of a machine for the automatic sorting of pomegranate
(Punica granatum) arils based on computer vision," Journal of Food
Engineering, vol. 90, no. 1, pp. 27-34, 2009.
[5] K. S. Fu, R. C. Gonzalez, and C. S. G. Lee, Robotics Control, Sensing,
Vision and Intelligence. New York: McGraw-Hill, 1987.
[6] W. Niblack,. An Introduction to Digital Image Processing. New Jersey:
Prentice Hall, 1986.
[7] A. G. Manh, G. Rabatel, L. Assemat, and M. J. Aladon, "Automation
and emerging technologies: weed leaf image segmentation by
deformable templates," Journal of Agricultural Engineering Research,
vol. 80, no. 2, pp. 139-146, 2001.
[8] D. Bulanon, T. Kataoka, H. Okamoto, and S. Hata, "A Real-time Image
processing algorithm for apple fruit detection," Journal of Hokkaido
Branch of the Japanese Society of Agricultural Machinery, vol. 45,
pp. 71-75, 2005.
[9] J. Blasco, N. Aleixos, J. M. Roger, G. Rabatel, and E. Molto, "Robotic
weed control using machine vision," Biosystems Engineering, vol. 83,
no. 2, pp. 149-157, 2002.
[10] A. N. Hejase, A. T. Adams, R. F. Harrington, T. K. Sarkar, "Shielding
effectiveness of `pigtail' connections," IEEE Transactions on
Electromagnetic Compatibility, vol. 31, no. 1, 63-68, 1989.
[11] E. R. Dougherty, An Introduction to Morphological Image Processing.
Washington: SPIE Optical Engineering Press, 1992.
[12] H. C. Raymond, H. Chung-Wa, and N. Mila, "Salt-and-pepper noise
removal by median-type noise detectors and detail-preserving
regularization," IEEE Transactions on Image Processing, vol. 14, no. 10,
pp. 1479-1485, 2005.
[13] K. M. Schmitt, R. C. D. Young, J. R. Riddington, D. M. Budgett, C. R.
Chatwin, "Image processing applied to brick quality control," The
International Journal of Advanced Manufacturing Technology, vol. 16,
no. 6, pp. 434-440, 2000.
[14] Y. Abdel-Aziz and H. Karara, "Direct linear trasnformation from
comparator coordinates into object space coordinates in close-range
photogrammetry," in Symposium on Close-Range Photogrammetry,
Illinois, 1971, pp. 1-18.
[15] J. Serra, Image Analysis Using Mathematical Morphology. New York:
Academic Press Inc., 1982.
[16] R. M. Haralick, S. R. Sternberg, and X. Zhuang, "Image analysis using
mathematical morphology," IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 9, no. 4, pp. 532-550, 1987.
[17] S. Nagabhushana, Computer Vision and Image Processing. New Age
International. 2006.
[18] T. Kanungo and R. M. Haralick, "Character recognition using
mathematical morphology" in Proceedings of the 5th USPS Advanced
Technology Conference, Washington, 1990, pp. 237-251.
[19] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image
Processing Using MATLAB. New Jersey: Prentice Hall, 2004.
[1] FruitLogistica informs, 2003-2008. Agrooglyad: Vegetables and Fruits.
http://www.lol.org.ua/eng/
[2] P. C. Condlife, M. R. Davey, B. J. Power, H. Koehorst-Van Putten, and
P. B. Visser, "An optimised protocol for rose transformation applicable
to different cultivars," in XXI International Eucarpia Symposium on
Classical versus Molecular Breeding of Ornamentals, M├╝nchen, 2003.
[3] J. Blasco, N. Aleixos, J. Gomez, and E. Molto, "Citrus sorting by
identification of the most common defects using multispectral computer
vision," Journal of Food Engineering, vol. 83, no. 3, pp. 384-393, 2007.
[4] J. Blasco, S. Cubero, J. Gomez-Sanchis, P. Mira, and E. Molto,
"Development of a machine for the automatic sorting of pomegranate
(Punica granatum) arils based on computer vision," Journal of Food
Engineering, vol. 90, no. 1, pp. 27-34, 2009.
[5] K. S. Fu, R. C. Gonzalez, and C. S. G. Lee, Robotics Control, Sensing,
Vision and Intelligence. New York: McGraw-Hill, 1987.
[6] W. Niblack,. An Introduction to Digital Image Processing. New Jersey:
Prentice Hall, 1986.
[7] A. G. Manh, G. Rabatel, L. Assemat, and M. J. Aladon, "Automation
and emerging technologies: weed leaf image segmentation by
deformable templates," Journal of Agricultural Engineering Research,
vol. 80, no. 2, pp. 139-146, 2001.
[8] D. Bulanon, T. Kataoka, H. Okamoto, and S. Hata, "A Real-time Image
processing algorithm for apple fruit detection," Journal of Hokkaido
Branch of the Japanese Society of Agricultural Machinery, vol. 45,
pp. 71-75, 2005.
[9] J. Blasco, N. Aleixos, J. M. Roger, G. Rabatel, and E. Molto, "Robotic
weed control using machine vision," Biosystems Engineering, vol. 83,
no. 2, pp. 149-157, 2002.
[10] A. N. Hejase, A. T. Adams, R. F. Harrington, T. K. Sarkar, "Shielding
effectiveness of `pigtail' connections," IEEE Transactions on
Electromagnetic Compatibility, vol. 31, no. 1, 63-68, 1989.
[11] E. R. Dougherty, An Introduction to Morphological Image Processing.
Washington: SPIE Optical Engineering Press, 1992.
[12] H. C. Raymond, H. Chung-Wa, and N. Mila, "Salt-and-pepper noise
removal by median-type noise detectors and detail-preserving
regularization," IEEE Transactions on Image Processing, vol. 14, no. 10,
pp. 1479-1485, 2005.
[13] K. M. Schmitt, R. C. D. Young, J. R. Riddington, D. M. Budgett, C. R.
Chatwin, "Image processing applied to brick quality control," The
International Journal of Advanced Manufacturing Technology, vol. 16,
no. 6, pp. 434-440, 2000.
[14] Y. Abdel-Aziz and H. Karara, "Direct linear trasnformation from
comparator coordinates into object space coordinates in close-range
photogrammetry," in Symposium on Close-Range Photogrammetry,
Illinois, 1971, pp. 1-18.
[15] J. Serra, Image Analysis Using Mathematical Morphology. New York:
Academic Press Inc., 1982.
[16] R. M. Haralick, S. R. Sternberg, and X. Zhuang, "Image analysis using
mathematical morphology," IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 9, no. 4, pp. 532-550, 1987.
[17] S. Nagabhushana, Computer Vision and Image Processing. New Age
International. 2006.
[18] T. Kanungo and R. M. Haralick, "Character recognition using
mathematical morphology" in Proceedings of the 5th USPS Advanced
Technology Conference, Washington, 1990, pp. 237-251.
[19] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image
Processing Using MATLAB. New Jersey: Prentice Hall, 2004.
@article{"International Journal of Information, Control and Computer Sciences:59510", author = "Luis Gracia and Carlos Perez-Vidal and Carlos Gracia", title = "Computer Vision Applied to Flower, Fruit and Vegetable Processing", abstract = "This paper presents the theoretical background and
the real implementation of an automated computer system to
introduce machine vision in flower, fruit and vegetable processing
for recollection, cutting, packaging, classification, or fumigation
tasks. The considerations and implementation issues presented in this
work can be applied to a wide range of varieties of flowers, fruits and
vegetables, although some of them are especially relevant due to the
great amount of units that are manipulated and processed each year
over the world. The computer vision algorithms developed in this
work are shown in detail, and can be easily extended to other
applications. A special attention is given to the electromagnetic
compatibility in order to avoid noisy images. Furthermore, real
experimentation has been carried out in order to validate the
developed application. In particular, the tests show that the method
has good robustness and high success percentage in the object
characterization.", keywords = "Image processing, Vision system, Automation", volume = "5", number = "6", pages = "639-7", }