Identification of bean varieties according to color features using artificial neural network
Abstract
A machine vision and a multilayer perceptron artificial neural network (MLP-ANN) were applied to identify bean varieties, based on color features. Ten varieties of beans, which were grown in Iran (Khomein1, KS21108, Khomein2, Sarab1, Khomein3, KS21409, Akhtar2, Sarab2, KS21205, and G11870) were collected. Six color features of the bean and six color features of the spots were extracted and used as input for MLP-ANN classifier. In this study, 1000 data sets were used, 70% for training, 15% for validating and 15% for testing. The results showed that the applied machine vision and neural network were able to classify bean varieties with 100% sensibility and specificity, except with Sarab1 with sensibilities of 100%, 73.3%, 60% for the training, validation and testing processes, respectively and KS21108 with specificities of 100%, 79% and 71%, respectively for the aforementioned processes. Considering total sensibilities of 100%, 97.33%, 96% and also specificities of 100%, 97.9% and 97.1% for training, validation and testing of beans, respectively, the ANN could be used as a effective tool for classification of bean varieties.Downloads
References
Abdullah MZ, Guan LC, Mohd Azemi BMN, 2001. Stepwise discriminant analysis for colour grading of oil palm using machine vision system. Food Bioprod Process 79: 223-231. http://dx.doi.org/10.1205/096030801753252298
Al Ohali Y, 2011. Computer vision based date fruit grading system: Design and implementation. KSU_CIS 23: 29-36.
Anami BS, Pujari JD, Yakkundimath R, 2011. Identification and classification of normal and affected agriculture/horticulture produce based on combined color and texture feature extraction. IJCAES I (III): 356-360.
Arribas JI, Sanchez-Ferrero GV, Ruiz-Ruiz G, Gomez-Gil J, 2011. Leaf classification in sunflower crops by computer vision and neural networks. Comput Electron Agr 78: 9–18. http://dx.doi.org/10.1016/j.compag.2011.05.007
Chen X, Xun Y, Li W, Zhang J, 2010.Combining discriminant analysis and neural networks for corn variety identification. Comput Electron Agr 71: S48-S53. http://dx.doi.org/10.1016/j.compag.2009.09.003
Du CJ, Sun DW, 2006. Learning techniques used in computer vision for food quality evaluation: a review. J Food Eng 72: 39-55. http://dx.doi.org/10.1016/j.jfoodeng.2004.11.017
Gonzalez R, Woods R, 2007. Digital image processing. 3rd edition. Prentice-Hall.
Granitto PM, Navone HD, Verdes PF, Ceccatto HA, 2002. Weed seeds identification by machine vision. Comput Electron Agr 33: 91-103. http://dx.doi.org/10.1016/S0168-1699(02)00004-2
Guevara-Hernandez F, Gomez-Gil J, 2011. Machine vision system for classification of wheat and barley grain kernels. Span J Agric Res 9: 672-680.
Huang KY, 2007. Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Comput Electron Agr 57: 3-11. http://dx.doi.org/10.1016/j.compag.2007.01.015
Kılıç K, Boyacı IH, Köksel H, Küsmenoğlu I, 2007. A classification system for beans using computer vision system and artificial neural networks. J Food Eng 78: 897-904. http://dx.doi.org/10.1016/j.jfoodeng.2005.11.030
Laurent B, Ousman B, Dzudie1 T, Carl MF Mb, Emmanuel T, 2010. Digital camera images processing of hard-to-cook beans. J Eng Technol Res 2: 177-188.
Leemans V, Destain MF, 2004. A real-time grading method of apple based on features extracted from defects. J Food Eng 61: 83-89. http://dx.doi.org/10.1016/S0260-8774(03)00189-4
Leemans V, Magein H, Destain MF, 1998. Defects segmentation on 'Golden Delicious' apples by using colour machine vision. Comput Electron Agr 20: 117-130. http://dx.doi.org/10.1016/S0168-1699(98)00012-X
Peres AM, Baptista P, Malheiro R, Dias LG, Bento A, Pereira JA, 2011. Chemometric classification of several olive cultivars from Trás-os-Montes region (northeast of Portugal) using artificial neural networks. Chemom Intell Lab Syst 105: 65-73. http://dx.doi.org/10.1016/j.chemolab.2010.11.001
Pydipati R, Burks TF, Lee WS, 2006. Identification of citrus disease using color texture features and discriminant analysis. Comput Electron Agr 52: 49–59. http://dx.doi.org/10.1016/j.compag.2006.01.004
Ruiz-Ruiza G, Gómez-Gilb J, Navas-Gracia LM, 2009. Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA). Comput Electron Agr 68: 88-96. http://dx.doi.org/10.1016/j.compag.2009.04.009
Sahin F,1997. A radial basis function approach to a color image classification problem in a real time industrial application. Master's thesis, Virginia Polytechnic Institute, Blacksburg.
Sarkar N, Wolfe RR, 1985. Feature extraction techniques for sorting tomatoes by computer vision. TASAE 28: 970-979.
Shahin MA, Symons SJ, 2001. A machine vision system for grading lentils. Can Biosyst Eng 43: 7-14.
Thorp KR, Dierig DA, 2011. Color image segmentation approach to monitor flowering in lesquerella. Ind Crops Prod 34: 1150-1159. http://dx.doi.org/10.1016/j.indcrop.2011.04.002
Venora G, Grillo O, Shahin MA, Symons SJ, 2007. Identification of Sicilian landraces and Canadian cultivars of lentil using an image analysis system. Food Res Int 40: 161-166. http://dx.doi.org/10.1016/j.foodres.2006.09.001
Venora G, Grillo O, Ravalli C, Cremonini R, 2009. Identification of Italian landraces of bean (Phaseolus vulgaris L.) using an image analysis system. Sci Hortic 121: 410-418. http://dx.doi.org/10.1016/j.scienta.2009.03.014
© CSIC. Manuscripts published in both the printed and online versions of this Journal are the property of Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.
All contents of this electronic edition, except where otherwise noted, are distributed under a “Creative Commons Attribution 4.0 International” (CC BY 4.0) License. You may read here the basic information and the legal text of the license. The indication of the CC BY 4.0 License must be expressly stated in this way when necessary.
Self-archiving in repositories, personal webpages or similar, of any version other than the published by the Editor, is not allowed.
