COMPUTER VISION FOR SPLENDID SQUID SIZE AND SPECIES CLASSIFICATION
Thai seafood industry relies heavily on labor intensive work, especially in a classification process. Classifying size and species of splendid squids can be exhausted and prone to errors. This study investigated approaches to automate size sorting of splendid squid and differentiating its species such as L.duvauceli and L.chinensis. Parameters extracted from squids’ images for example, area, width, length and constructive geometries were tested for their significance. As functions of these parameters, classifiers for grading size were developed based on regression analysis and neural network models; a discrimination analysis was employed in species sorting. Neural network at the accuracy of 92.67% was found to marginally outperform the regression model (88% accuracy) in size prediction; however, non-linear regression was recommended in practice due to its simplicity to apply. For species differentiation, the discrimination equation was as accurate as the crisp divider (width over length ratio) at approximately 90%. These computerized approaches in size and species classification were proven to be superior to manual practice; they can overcome limitations of work performance due to individual capability and ergonomic factors.
T. Boonwanich, S. Thossapornpitakkul and U. Chotitummo, “Reproductive biology of squid Loligo duvauceli and L. chinensis in the Southern Gulf of Thailand,” Technical Report, Southern Marine Fisheries Development Center, Marine Fisheries Division, Department of Fisheries, Thailand, 1998.
National Bureau of Agricultural Commodity and Food Standards. (2011). The National Bureau of Agricultural Commodity and Food Standards (ACFS) [Online]. Available: http://www.acfs.go.th/eng/
N. Sukramongkol, K. Tsuchiya and S. Segawa, “Age and maturation of Loligo Duvauceli and L. Chinensis from Andaman Sea of Thailand”, Reviews in Fish Biology and Fisheries, vol. 17, no. 2-3, pp. 237–246, 2007.
Y. Fasser and D. Bretter, Process Improvement in the Electronics Industries. New York: John Wiley & Sons Inc., 1992.
T. Brosnanand and D.-W. Sun, “Inspection and grading of agricultural and food products by computer vision systems—a review”, Computers and Electronics in Agriculture, vol. 36, no. 2-3, pp. 193–213, 2002.
P.M. Bato, M. Nagata, M. Mitarai, Q. Cao and T. Kitahara, “Study on sorting system for strawberry using machine vision (part 2): development of sorting system with direction and judgment functions for strawberry (Akihime variety)”, Journal of Japan Society of Agricultural Machinery, vol. 62, no. 2, pp. 101–110, 2000.
B. Jarimopas and N. Jaisin, “An experimental machine vision system for sorting sweet tamarind”, Journal of Food Engineering, vol. 89, no. 3, pp. 291–297, 2008.
H.-H. Chen and C.-H. Ting, “The development of a machine vision system for shiitake grading”, Journal of Food Quality, vol. 27, no. 5, pp. 352–365, 2004.
N.H. Cho, D. Chang, S.-H. Lee, H.-J. Kim and Y.-H. Lee, “Development of automatic sorting system for green pepper using machine vision”, in the 2007 ASABE Annual International Meeting Sponsored by ASABE, Minneapolis Convention Center, Minneapolis, Minnesota, 2007, pp. 1-11.
M. Kashiha, C. Bahr, S. Ott, C. P.H. Moon, T.A. Niewold, F. O. Odgerg and D. Berckmans, “Automatic weight estimation of individual pigs using image analysis”, Computers and Electronics in Agriculture, vol. 107, pp. 38-44, 2014.
N. Thammachot, S. Chaiprapat and K. Waiyakan, “Development of an image processing system in splendid squid grading”, in the 9th International Conference on Computing and InformationTechnology (IC2IT2013), Bangkok, Thailand, 2013, pp. 175–183.
A.O. Yousef, “Computer vision based date fruit grading system: Design and implementation,” Journal of King Saud University - Computer and Information Sciences, vol. 23, no. 1, pp. 29–36, 2011.
M. Nagata and J.G. Tallada, “Quality Evaluation of Strawberries”, in Computer Vision Technology for Food Quality Evaluation, San Diego: Academic Press, 2008, pp. 265-2008.
S. Riyadi, A.A. Rahni, M.M. Mustafa and A. Hussain, “Shape characteristics analysis for papaya size classification”, in 5th Student Conference on Research and Development, Selangor, Malaysia, 2007, pp. 1–5.
K. Kılıç, I.H. Boyacı, H. Köksel and I. Küsmenoğlu, “A classification system for beans using computer vision system and artificial neural networks”, Journal of Food Engineering, vol. 78, no. 3, pp. 897–904, 2007.
X. Chen, Y. Xun, W. Li and J. Zhang, “Combining discriminant analysis and neural networks for corn variety identification”, Computer and Computing Technologies in Agriculture, vol. 71, pp. S48–S53, 2010.
S. Sansomboonsuk and N. Afzulpurkar, “Machine vision for rice quality evaluation”, in Technology and Innovation for Sustainable Development Conference, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand, 2008, pp. 343-346.
X. Liming and Z. Yanchao, “Automated strawberry grading system based on image processing”, Computers and Electronics in Agriculture, vol. 71, pp. S32–S39, 2010.
D.S. Jayas, J. Paliwal and N.S. Visen, “Review paper (AE—Automation and Emerging Technologies): multi-layer neural networks for image analysis of agricultural products”, Journal of Agricultural Engineering Research, vol. 77, no. 2, pp. 119–128, 2000.
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