Abstract
Computer vision methods have so far been applied in almost every area of our lives. They are used in medical sciences, natural sciences, engineering, etc. Computer vision methods have already been used in studies on the search for links between the quality of raw food technology and their external characteristics (e.g. color, size, texture). Such work is also conducted for cereals. For the analysis results to meet the expectations of users of the system, it should include not only the attributes describing the controlled products, materials or raw materials, but should also indicate the type of material or species/variety of raw material. However existing solutions are very often implemented as closed source software (black box) therefore the user has no possibility to customize them (for example the enterprise cannot integrate these solutions into its management information system). The high cost of automated visual inspection systems are also a major problem for enterprises. The aim of this paper is to develop a method of estimating the size and shape of a rice grains using visual quality analysis, implemented in the multi-agent system named Rice Identification Collaborative Environment. Using this method will allow statistical analysis of the characteristics of the sample, and will be one of the factors leading to the identification of species/varieties of cereals and determining the percentage of the grains that do not meet quality standards. The method will be implemented as an open source software in Java. Consequently it can be easily integrated into enterprise’s management information system. Because it will be available for free, the cost of automated visual inspection systems will be reduced significantly. This paper is organized as follows: the first part shortly presents the state-of-the-art in the considered field; next, a developed method for size and shape estimation implemented in the Rice Identification Collaborative Environment is characterized; the results of a research experiment for verification of the developed method are presented in the last part of paper.
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Abdullah, M.Z., Guan, L.C., Lim, K.C., Karim, A.A.: The applications of computer vision system and tomographic radar imaging for assessing physical properties of food. J. Food Eng. 61, 125–135 (2004)
Baptista, P., Cunha, T.R., Gama, C., Bernardes, C.: A new and practical method to obtain grain size measurements in sandy shores based on digital image acquisition and processing. Sed. Geol. 282, 294–306 (2012)
Buscombe, D., Masselink, G.: Grain-size information from the statistical properties of digital images of sediment. Sedimentology 56, 421–438 (2009)
Cannata, S., Engle, T.E., Moeller, S.J., Zerby, H.N., Radunz, A.E., Green, M.D., Bass, P.D., Belk, K.E.: Effect of visual marbling on sensory properties and quality traits of pork loin. Meat Sci. 85, 428–434 (2010)
Fornal, J., Jeliński, T., Sadowska, J., Quattrucci, E.: Comparison of endosperm microstructure of wheat and durum wheat using digital image analysis. Int. Agrophys. 13(2), 215–220 (1999)
Fortin, A., Robertson, W.M., Tong, A.K.W.: The eating quality of Canadian pork and its relationship with intramuscular fat. Meat Sci. 69, 297–305 (2005)
Fornal, Ł., Kozirok, W., Chorazewicz, R.: New possibilities to characterizing wheat grain endosperm. Pol. J. Food Nutr. Sci. 13(1), 170–183 (2003)
Franklin, S., Patterson F.G.: The LIDA architecture: adding new modes of learning to an intelligent, autonomous, software agent. In: Proceedings of the International Conference on Integrated Design and Process Technology. Society for Design and Process Science, San Diego (2006)
Graham, D.J., Reid, I., Rice, S.P.: Automated sizing of coarse-grained sediments: image-processing procedures. Math. Geol. 37, 1–28 (2005)
Hernes, M., Sobieska-Karpińska, J.: Application of the consensus method in a multiagent financial decision support system. Inf. Syst. e-Bus. Manag., 1–19 (2015). doi:10.1007/s10257-015-0280-9. Springer, Heidelberg
Hernes, M., Maleszka, M., Nguyen, N.T., Bytniewski, A.: The automatic summarization of text documents in the Cognitive Integrated Management Information System. In: Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Łódź (2015)
Iqbal, A., Valous, N.A., Mendoza, F., Sun, D.-W., Allen, P.: Classification of pre-sliced pork and Turkey ham qualities based on image colour and textural features and their relationships with consumer responses. Meat Sci. 84, 455–465 (2010)
Majumdar, S., Jayas, D.S.: Classification of cereal grains using machine vision: VI. Combined morphology, color, and texture models. Am. Soc. Agric. Eng. 43(6), 1689–1694 (2000)
Rubin, D.M., Chezar, H., Harney, J.N., Topping, D.J., Melis, T.S., Sherwood, C.R.: Underwater microscope for measuring spatial and temporal changes in bed-sediment grain size. Sed. Geol. 202, 402–408 (2007)
Sànchez, A.J., Albarracin, W., Grau, R., Ricolfe, C., Barat, J.M.: Control of ham salting by using image segmentation. Food Control 19, 135–142 (2008)
Visen, N.S., Paliwal, J., Jayas, D.S., White, N.D.G.: Specialist neural networksfor cereal grain classification. Biosyst. Eng. 82(2), 151–159 (2001)
Warrick, J.A., Rubin, D.M., Ruggiero, P., Harney, J.N., Draut, A.E., Buscombe, D.: Cobble cam: grain-size measurements of sand to boulder from digital photographs and autocorrelation analyses. Earth Surf. Proc. Land. 34, 1811–1821 (2009)
Hernes, M., Nguyen, N.T.: Deriving consensus for hierarchical incomplete ordered partitions and coverings. J. Univ. Comput. Sci. 13(2), 317–328 (2007)
Nguyen, N.T.: Consensus systems for conflict solving in distributed systems. Inf. Sci. 147(1–4), 91–122 (2002)
Pham, V.H., Lee, B.R.: An image segmentation approach for fruit defect detection using k-Means clustering and graph-based algorithm. Vietnam J. Comput. Sci. 2(1), 25–33 (2015)
Hoang, V.D., Jo, K.H.: Path planning for autonomous vehicle based on heuristic searching using online images. Vietnam J. Comput. Sci. 2(2), 109–120 (2015)
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Hernes, M., Maleszka, M., Nguyen, N.T., Bytniewski, A. (2016). A Method for Size and Shape Estimation in Visual Inspection for Grain Quality Control in the Rice Identification Collaborative Environment Multi-agent System. In: Nguyen, N.T., Kowalczyk, R. (eds) Transactions on Computational Collective Intelligence XXII. Lecture Notes in Computer Science(), vol 9655. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49619-0_11
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DOI: https://doi.org/10.1007/978-3-662-49619-0_11
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