Identification of olive cultivars using image processing techniques


Abstract: As a country in an olive production zone, olive (Olea europaea L.) production is of great economic importance for Turkey. Turkey has 88 domestic and 28 foreign cultivars of olive, all of which are located in the Kemalpaşa Production and Research Garden (Olive Gene Bank) at the Olive Research Station in İzmir (38°25'34.7628''N, 27°25'22.9872''E). The aim of this study was the identification of olive cultivars using image processing techniques. For this aim, images of olives, taken at 2896 x 1944 pixel and 300 dpi resolution, were captured using a DSLR camera, and evaluations of pixels were used for considering the pixel distribution and dimension measurements. For this aim, MATLAB v2012 and Image j software were used. In the light of the obtained data, analysis of variance and Duncan?s test were used to characterize the olive cultivars. As a result, all observed olive cultivars were identified at P < 0.05 significance level. Sarı ulak, Gemlik, Edincik su, Memecik, Eşek zeytini, Ayvalık, Kilis yağlık, Uslu, Çilli, and Domat olive cultivars were identified utilizing the validation process employing image processing and analysis techniques. Only the Erkence cultivar was not identified. Moreover, different classification techniques were applied to the olive stone color value data by the help of SPSS v22 and Clementine v12, which is a data mining software package from SPSS. In addition to the first results the Erkence cultivar was identified 69% with artificial neural networks.

Keywords: Artificial vision, image analysis, olives, olive stone, varietal identification

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