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Title: A computer vision approach based on endocarp features for the identification of olive cultivars
Authors: Satorres Martínez, Silvia
Martínez Gila, Diego Manuel
Abdullah, Beyaz
Gómez Ortega, Juan
Gámez García, Javier
Abstract: 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.
Keywords: Endocarp features
Computer vision
Olive varietal identification
Wilk’s Lambda
Partial least squares discriminant analysis
Issue Date: 20-Sep-2018
metadata.dc.description.sponsorship: DPI2016-78290-R
Publisher: ELSEVIER
Citation: Satorres Martínez, S., Martínez Gila, D., Beyaz, A., Gómez Ortega, J., & Gámez García, J. (2018). A computer vision approach based on endocarp features for the identification of olive cultivars. Computers and Electronics in Agriculture, 154, 341–346.
Appears in Collections:DIEA-Artículos

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