A computer vision approach based on endocarp features for the identification of olive cultivars

Satorres Martinez S., Martinez Gila D., BEYAZ A., Gomez Ortega J., Gamez Garcia J.

COMPUTERS AND ELECTRONICS IN AGRICULTURE, cilt.154, ss.341-346, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 154
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.compag.2018.09.017
  • Dergi Adı: COMPUTERS AND ELECTRONICS IN AGRICULTURE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.341-346
  • Anahtar Kelimeler: Endocarp features, Computer vision, Olive varietal identification, Wilk's Lambda, Partial least squares discriminant analysis, SYSTEM
  • Ankara Üniversitesi Adresli: Evet

Özet

The identification of olive cultivars is of utmost importance for a multitude of factors affecting both, the olive oil elaboration process and fair trade exchanges. The accurate varietal identification is a time consuming task that requires trained specialists or expensive and specific equipment. When applying the traditional method, a specialist assesses morphological features using the olive endocarp. A proposal to automate this identification method is presented in this paper. Endocarp images, acquired under three different perspectives, are processed to extract the same information that the specialist utilizes. Then, the partial least squares discriminant analysis classifier, with or without feature selection, has been tested on a set of 250 samples from 5 different varieties. Results show that the proposal is an alternative identification method which could also be used in the traditional one in order to assist the specialist in the determination of the variety.