Olive Variety Classification and Prediction From 3D Morphology of Fruit and Stone: A Study Case on Five South Italy Autochthone Cultivars

Accurate olive cultivar identification is critical for ensuring quality control and traceability in the olive oil industry. TheInternational Olive Council (IOC) and the International Union for the Protection of New Varieties of Plants (UPOV) have es-tablished standardized protocols for varietal characterization. Over the past two decades, two-dimensional image analysis tech-niques have been increasingly employed for olive variety identification, utilizing various morphological parameters and machinelearning approaches. This study investigates olive varietal classification through three-dimensional morphological analysis offruits and stones using X-ray microtomography. The research evaluates the discriminative power of different trait combinationsusing both Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) algorithms to contribute to an optimizedprotocol for cultivar identification. Five autochthonous olive cultivars from the Campania region (Southern Italy) were analyzed.A preliminary comparison of classification performance between continuous and discrete morphological olive data revealedsuperior effectiveness of the continuous ones. Integrating quantitative morphometric traits with selected visual discrete UPOVcharacteristics yielded optimal overall classification accuracy of 88.41% using LDA with 84.4% for Ravece, 81.5% for Ortice, 100%for Frantoio, 81.3% for Rotondella, and 90.9% for Minucciola olive varieties. The best variety prediction rates, based on an olivesample not used for training, were provided by SVM, obtaining 70.0% for Ravece, 87.5% for Ortice, 54.5% for Frantoio, 60.0% forRotondella, and 66.7% for Minucciola. Quantification of varietal overlap through Bhattacharyya coefficients identified Orticeand Ravece as the most phenotypically similar varieties, while Rotondella and Minucciola exhibited the most distinctive fruitmorphology. Notably, all varieties showed at least one misclassification with the Frantoio variety. Morphological analysis demon-strated that endocarp surface traits provided the most discriminative power, and internal cavity characteristics also contributedsignificantly to varietal differentiation. These findings suggest two key implications: potential updates of UPOV guidelines fordistinctness evaluation protocols and promising applications in authenticity verification for high-quality olive products.