[{
  "type": "article-journal",
  "title": "CLASSIFICATION OF RICE GRAIN VARIETIES USING TWO ARTIFICIAL NEURAL NETWORKS (MLP AND NEURO-FUZZY)",
  "author": [
    {
      "family": "1",
      "given": ""
    },
    {
      "family": "Farokhi",
      "given": ""
    },
    {
      "family": "Pazoki",
      "given": ""
    }
  ],
  "issued": {
    "date-parts": [[2014]]
  },
  "container-title": "Journal of Animal and Plant Sciences",
  "ISSN": "1018-7081",
  "volume": "24",
  "issue": "1",
  "page": "336-343",
  "DOI": "NA",
  "abstract": "<p>Artificial neural networks (ANNs) have many applications in various scientific areas such as identification, prediction and image processing. This research was done at the Islamic Azad University, Shahr-e-Rey Branch, during 2011 for classification of 5 main rice grain varieties grown in different environments in Iran. Classification was made in terms of 24 color features, 11 morphological features and 4 shape factors that were extracted from color images of each grain of rice. The rice grains were then classified according to variety by multi layer perceptron (MLP) and neuro-fuzzy neural networks. The topological structure of the MLP model contained 39 neurons in the input layer, 5 neurons (Khazar, Gharib, Ghasrdashti, Gerdeh and Mohammadi) in the output layer and two hidden layers; neuro-fuzzy classifier applied the same structure in input and output layers with 60 rules. Average accuracy amounts for classification of rice grain varieties computed 99.46% and 99.73% by MLP and neuro-fuzzy classifiers alternatively. The accuracy of MLP and neuro-fuzzy networks changed after feature selections were 98.40% and 99.73 % alternatively.</p>",
  "publisher": "Pakistan Agricultural Scientists Forum",
  "URL": "https://thejaps.org.pk/AbstractView.aspx?mid=2014-JAPS-47"
}]