Advancing automatic recognition of Moroccan Phoenix dactylifera L. varieties through transfer learning techniques

Date fruit holds immense significance not only commercially and in terms of health benefits but also in Moroccan cultural heritage. However, as knowledge about Moroccan date cultivars diminishes, leveraging modern technology becomes essential for both preservation and accessibility of this invaluable cultural heritage. In this study, a comprehensive approach utilising computer vision for automatic recognition of popular Moroccan date fruit cultivars is presented. Through various methodologies, including manual feature extraction and transfer learning. The dataset comprises nine cultivars, with emphasis on capturing real-world variability through image acquisition under unstable conditions. The findings indicate exceptional performance, achieving close to 99% accuracy using transfer learning.