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Traditional morphological analysis is a widely employed tool for the identification and discrimination of olive germplasm by using morphological markers which are monitored by subjective manual measurements that are labor intensive and time-consuming. Alternatively, an automated methodology can quantify the geometrical features of fruits, leaves and endocarps with high accuracy and efficiency in order to define their morphological characteristics. In this study, 24 characteristics for fruits, 16 for leaves and 25 for endocarps were determined and used in an automated way with basic classifiers combined with a meta-classsifier approach. This resulted to the discrimination of 14 olive cultivars utilizing data obtained from two consecutive olive growing periods. The cultivar classification algorithms were based on machine learning techniques. The 95% accuracy rate of the meta-classifier approach indicated that was an efficient tool to discriminate olive cultivars. The contribution of each morphological feature to cultivar discrimination was quantified, and the significance of each one was automatically detected in a quantitative way. The higher the contribution of each feature, the higher the significance for cultivar discrimination. The identification of most cultivars was guided by the features of both endocarps and fruits, while those of leaves were only efficient to identify the Kalamon cultivar. The combined use of morphological features of three olive organs might have an additive effect leading to higher capacity for discrimination of cultivars. The proposed methodology might be considered a phenomics tool for olive cultivar identification and discrimination in a wide range of applications including breeding.

The genetic relationships between Greek wild olive tree populations and cultivars were investigated. A total of 219 wild genotypes and 67 cultivar genotypes were analyzed by employing 10 SSR markers. Data evidenced that the wild populations exhibited high levels of genetic diversity and exclusively host 40% of the total number of alleles detected. Inbreeding was observed within populations, probably as a consequence of their fragmented spatial distribution. The genetic differentiation between cultivars and wild individuals, as well as within wild populations, was low. Nevertheless, three gene pools of wild trees were detected, corresponding to the geographical areas of Northeastern Greece, Peloponnese-Crete and Epirus. Most cultivars clustered in a separate group, while the rest of them formed a heterogenous group with membership coefficients akin to the three wild olive clusters. Regarding the history of olive cultivation in Greece, bidirectional gene flow was detected between populations of Peloponnese-Crete and the gene pool that composes some of Greece’s most important cultivars, such as "Koroneiki” and “Mastoidis”, which is inferred as an indication of a minor domestication event in the area. A strategy for the protection of Greek-oriented olive genetic resources is proposed, along with suggestions for the utilization of the genetically diverse wild resources with regard to the introgression of traits of agronomical interest to cultivars.

Olive (Olea europeae L.) salinity stress induces responses at morphological, physiological and molecular levels, affecting plant productivity. Four olive cultivars with differential tolerance to salt were grown under saline conditions in long barrels for regular root growth to mimic field conditions. Arvanitolia and Lefkolia were previously reported as tolerant to salinity, and Koroneiki and Gaidourelia were characterized as sensitive, exhibiting a decrease in leaf length and leaf area index after 90 days of salinity. Prolyl 4-hydroxylases (P4Hs) hydroxylate cell wall glycoproteins such as arabinogalactan proteins (AGPs). The expression patterns of P4Hs and AGPs under saline conditions showed cultivar-dependent differences in leaves and roots. In the tolerant cultivars, no changes in OeP4H and OeAGP mRNAs were observed, while in the sensitive cultivars, the majority of OeP4Hs and OeAGPs were upregulated in leaves. Immunodetection showed that the AGP signal intensity and the cortical cell size, shape and intercellular spaces under saline conditions were similar to the control in Arvanitolia, while in Koroneiki, a weak AGP signal was associated with irregular cells and intercellular spaces, leading to aerenchyma formation after 45 days of NaCl treatment. Moreover, the acceleration of endodermal development and the formation of exodermal and cortical cells with thickened cell walls were observed, and an overall decrease in the abundance of cell wall homogalacturonans was detected in salt-treated roots. In conclusion, Arvanitolia and Lefkolia exhibited the highest adaptive capacity to salinity, indicating that their use as rootstocks might provide increased tolerance to irrigation with saline water.

Abstract Background The morphological analysis of olive leaves, fruits and endocarps may represent an efficient tool for the characterization and discrimination of cultivars and the establishment of relationships among them. In recent years, much attention has been focused on the application of molecular markers, due to their high diagnostic efficiency and independence from environmental and phenological variables. Results In this study, we present a semi-automatic methodology of detecting various morphological parameters. With the aid of computing and image analysis tools, we created semi-automatic algorithms applying intuitive mathematical descriptors that quantify many fruit, leaf and endocarp morphological features. In particular, we examined quantitative and qualitative characters such as size, shape, symmetry, contour roughness and presence of additional structures such as nipple, petiole, endocarp surface roughness, etc.. Conclusion We illustrate the performance and the applicability of our approach on Greek olive cultivars; on sets of images from fruits, leaves and endocarps. In addition, the proposed methodology was also applied for the description of other crop species morphologies such as tomato, grapevine and pear. This allows us to describe crop morphologies efficiently and robustly in a semi-automated way.