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| Main Author: | |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Universidad Nacional de Colombia
2017
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| Online Access: | https://www.redalyc.org/articulo.oa?id=169952658006 https://www.redalyc.org/journal/1699/169952658006/ https://www.redalyc.org/journal/1699/169952658006/html/ https://www.redalyc.org/journal/1699/169952658006/169952658006.epub https://www.redalyc.org/journal/1699/169952658006/movil |
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Table of Contents:
- Intelligent classification models for food products basis on morphological, colour and texture features Narendra Veernagouda Ganganagowder Priya Kamath Agrociencias test training Algorithm digital images food classifiers The aim of this research is to build a supervised intelligent classification model of food products such as Biscuits, Cereals, Vegetables, Edible nuts and etc., using digital images. The Correlation-based Feature Selection (CFS) algorithm and 2nd derivative pre-treatments of the Morphological, Colour and Texture features are used to train the models for classification and detection. The best prediction accuracy is obtained for the Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Simple Logistic (SLOG) and Sequential Minimal Optimization (SMO) classifiers (more than 80% of the success rate for the training/test set and 80% for the validation set). The percentage of correctly classified instances is very high in these models and ranged from 80% to 96% for the training/test set and up to 95% for the validation set. 2017 artículo científico 0120-2812 https://www.redalyc.org/articulo.oa?id=169952658006 https://www.redalyc.org/journal/1699/169952658006/ https://www.redalyc.org/journal/1699/169952658006/html/ https://www.redalyc.org/journal/1699/169952658006/169952658006.epub https://www.redalyc.org/journal/1699/169952658006/movil en http://www.redalyc.org/revista.oa?id=1699 Acta Agronómica application/pdf Universidad Nacional de Colombia Acta Agronómica (Colombia) Num.4 Vol.66