Abstract
The development of an app capable of identifying olive varieties using photos of olive pits is the ultimate goal of ‘OliVaR,' a neural network trained with the largest photographic database of olive fruit endocarps, which has been generated by the partners of the GEN4OLIVE European project. The tool's development has been possible thanks to the cataloguing and documentation work of five germplasm banks in different countries and to advances in Artificial Intelligence systems. The University of Cordoba has played a fundamental role, as the institution having provided the most information, with data on 63 varieties from its Germplasm Bank. The initiative, which is part of the GEN4OLIVE European project to improve olive trees, coordinated by the Ucolivo group of the Maria de Maeztu Unit of Excellence - Department of Agronomy (DAUCO), involved the participation of olive germplasm banks from Morocco, Greece, Italy, and Turkey to gather more than 150,000 photos of 133 olive varieties from the Mediterranean basin. The Computer Science Department at Rome's Sapienza University was in charge of collecting the information and creating the algorithm for this tool, which proposes a new approach to identify varieties and automates the traditional morphological classification process.
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