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 × 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.

Eco‐Friendly Alternative to Brining for Sustainable Storage of Table‐Olives Based on the Analyses Done with the Extracted Oil

“…Turkey has 88 domestic and 28 foreign cultivars of olive Beyaz and Öztürk. [ 22 ] Kaya et al. [ 23 ] after making the assessment of the genetic relationship of the Turkish olives ( Olea europaea subsp europaea ) based on the cpDNA trnL‐F regions of their genes, reported that the Turkish olive cultivars demonstrated a low level of genetic variation in terms of cpDNA trnL‐F regions and concluded that this may indicate the genetic stability of plastid genomes.…”

Authentication and Chemometric Discrimination of Six Greek PDO Table Olive Varieties through Morphological Characteristics of Their Stones

“…As there is plethora of olive varieties with a diversity of morphological and physiological characteristics, the existence of many different qualities is expected [19]. In order to avoid olive adulterations, several discriminant protocols for varietal identification, based on stone, fruit, and leaf data have been used [20].…”

“…Several advanced analytical techniques have been used for the study of authentication of table olives, such as high-performance liquid chromatography (HPLC) [23,28], ultra-highperformance liquid chromatography-quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) [25], gas chromatography-mass spectrometry (GC-MS) [26] and nuclear magnetic resonance spectroscopy (NMR) [21]. Chemometrics is an important science which has been extensively used in food science and authenticity studies to facilitate interpretation of huge load of data, and it provides an easy way to visualize the samples [19,20,[29][30][31][32][33][34][35][36].…”

“…Applying a 5% bleach solution for one hour, there were a complete absence of biological material and no damage appears in the endocarp texture [33]. Bleach solution was also used by Beyaz et al for the cleaning process of olive stones [20]. Specifically, the olive stones were kept in plastic containers, containing 10% bleach solution, for 15 h and stored at −4 • C to prevent them from cracking because of physiological activity.…”

Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach

“…In recent years, there are also some product-based classification studies conducted by taking apple, banana, pear, pineapple, grape, melon, watermelon, citrus and avocado fruit into consideration (Zhang and Wu, 2012;Zhang et al, 2014). Variety based studies have been conducted to classify varieties of wheat (Dubey et al, 2006;Marini et al, 2008;Arefi et al, 2011;Pazoki and Pazoki, 2011;Zapotoczny, 2011;Khoshroo et al, 2014;Taner et al, 2015), rice (Liu et al, 2005;Guzman and Perelta, 2008;Silva and Sonnadara, 2013;Pazoki et al, 2014), barley (Zapotoczny, 2012), corn (Chen et al, 2010), bean (Nasirahmadi and Behroozi-Khazaei, 2013), and olive (Beyaz and Öztürk, 2016;Beyaz et al, 2017). Taner et al (2018) conducted both product and variety-based classification and in their study, they conducted with bread wheat, durum wheat, barley, oat and triticale products.…”