Computer Vision Applied to Flower, Fruit and Vegetable Processing

Authors: Luis Gracia, Carlos Perez-Vidal, Carlos Gracia

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

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1075268

References:

[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.