Grain classifier with computer vision using adaptive neuro-fuzzy inference system

Sabanci K., Toktaş A., Kayabasi A.

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, cilt.97, sa.12, ss.3994-4000, 2017 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 97 Sayı: 12
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1002/jsfa.8264
  • Dergi Adı: JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.3994-4000
  • Anahtar Kelimeler: wheat grains, classification, image processing, feature selection, adaptive neuro-fuzzy inference system (ANFIS), MACHINE VISION, VARIETIES, WHEAT, FEATURES, COFFEE
  • Ankara Üniversitesi Adresli: Evet

Özet

BACKGROUNDA computer vision-based classifier using an adaptive neuro-fuzzy inference system (ANFIS) is designed for classifying wheat grains into bread or durum. To train and test the classifier, images of 200 wheat grains (100 for bread and 100 for durum) are taken by a high-resolution camera. Visual feature data of the grains related to dimension (#4), color (#3) and texture (#5) as inputs of the classifier are mainly acquired for each grain using image processing techniques (IPTs). In addition to these main data, nine features are reproduced from the main features to ensure a varied population. Thus four sub-sets including categorized features of reproduced data are constituted to examine their effects on the classification. In order to simplify the classifier, the most effective visual features on the results are investigated.