The olive sector is crucial in the province of Jaén (Spain), as it is the main source of income. Within this sector, the identification of olive varieties is an essential task that is hampered by the morphological variability of the leaves and by the fact that there are numerous varieties that directly affect the quality of the oil. This study uses deep learning to classify olive tree varieties, notably the Picual variety, using RGB images. A balanced dataset of leaf images was created through acquisition and segmentation. Six pre-trained Convolutional Neural Networks (VGG16, ResNet50, InceptionV3, Xception, MobileNetV2 and DenseNet121) were evaluated and adjusted using transfer learning. Among them, DenseNet121 achieved 83.2% accuracy, a loss of 0.549, and 41/50 correct predictions on unseen leaves, leveraging its dense connectivity. Preprocessing included data augmentation and hyperparameter tuning to ensure robustness. This approach improves precision agriculture by automating variety identification. Future work will include expanding the dataset and integrating hyperspectral data. This research supports the efficient management of olive crops and offers a scalable solution for producers in major producing regions.