Computer vision-based method for classification of wheat grains using artificial neural network

Sabanci K., Kayabasi A., TOKTAŞ A.

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, cilt.97, sa.8, ss.2588-2593, 2017 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 97 Sayı: 8
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1002/jsfa.8080
  • Dergi Adı: JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2588-2593
  • Anahtar Kelimeler: classification, wheat grains, image processing, artificial neural network (ANN), multilayer perceptron, MACHINE VISION, SYSTEM, COFFEE
  • Ankara Üniversitesi Adresli: Hayır

Özet

BACKGROUND: A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying wheat grains into bread or durum is presented. The images of 100 bread and 100 durum wheat grains are taken via a high-resolution camera and subjected to pre-processing. The main visual features of four dimensions, three colors and five textures are acquired using image-processing techniques (IPTs). A total of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model. The data sets of visual features are considered as input parameters of the ANN model. The ANN with four different input data subsets is modelled to classify the wheat grains into bread or durum. The ANN model is trained with 180 grains and its accuracy tested with 20 grains froma total of 200 wheat grains.