Back to Search
Start Over
Intelligent classification models for food products basis on morphological, colour and texture features
- Source :
- Acta Agronómica, Vol 66, Iss 4, Pp 486-494 (2017)
- Publication Year :
- 2017
- Publisher :
- Universidad Nacional de Colombia, 2017.
-
Abstract
- The aim of this paper 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.
Details
- Language :
- English, Spanish; Castilian, Portuguese
- ISSN :
- 01202812 and 23230118
- Volume :
- 66
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- Acta Agronómica
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.46ce27c3a8f24461907004a588d34484
- Document Type :
- article
- Full Text :
- https://doi.org/10.15446/acag.v66n4.60049