28 January 2013 Identification and classification of similar looking food grains
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Proceedings Volume 8760, International Conference on Communication and Electronics System Design; 876008 (2013) https://doi.org/10.1117/12.2009957
Event: International Conference on Communication and Electronics System Design, 2013, Jaipur, India
Abstract
This paper describes the comparative study of Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers by taking a case study of identification and classification of four pairs of similar looking food grains namely, Finger Millet, Mustard, Soyabean, Pigeon Pea, Aniseed, Cumin-seeds, Split Greengram and Split Blackgram. Algorithms are developed to acquire and process color images of these grains samples. The developed algorithms are used to extract 18 colors-Hue Saturation Value (HSV), and 42 wavelet based texture features. Back Propagation Neural Network (BPNN)-based classifier is designed using three feature sets namely color – HSV, wavelet-texture and their combined model. SVM model for color- HSV model is designed for the same set of samples. The classification accuracies ranging from 93% to 96% for color-HSV, ranging from 78% to 94% for wavelet texture model and from 92% to 97% for combined model are obtained for ANN based models. The classification accuracy ranging from 80% to 90% is obtained for color-HSV based SVM model. Training time required for the SVM based model is substantially lesser than ANN for the same set of images.
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B. S. Anami, Sunanda D. Biradar, D. G. Savakar, P. V. Kulkarni, "Identification and classification of similar looking food grains", Proc. SPIE 8760, International Conference on Communication and Electronics System Design, 876008 (28 January 2013); doi: 10.1117/12.2009957; https://doi.org/10.1117/12.2009957
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