Data/hora: 05/06/2024 03:39:05

Provedor de dados: | 109
País: | Colombia
Título: | Intelligent classification models for food products basis on morphological, colour and texture features
Autores: | Ganganagowder,Narendra Veernagouda; Kamath,Priya.
Data: | 2017-12-01
Ano: | 2017
Palavras-chave: | Algorithm; Digital images; Food classifiers; Prediction accuracy; Training/test.
Resumo: | Abstract 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.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-28122017000400486
Editor: | Universidad Nacional de Colombia
Relação: | 10.15446/acag.v66n4.60049
Formato: | text/html
Fonte: | Acta Agronómica  v.66 n.4 2017
Fonte: | info:eu-repo/semantics/openAccess