A machine learning approach for classifying date fruit varieties at the Rutab stage - Alfarhood, Meshal; Alsahw, Nawaf; Almajed, Mohammed; Alzahrani, Meshaal; Alawfi, Ahmad; Alanazi, Meshal; Alalwan, Abdalrahman (Frontiers in Plant Science, 2025)
IntroductionDates have long been a vital part of the cultural and nutritional heritage of arid regions, particularly in the Middle East. Among their ripening stages, the Rutab stage—an intermediate phase between the Khalal (immature) and Tamar (fully ripe) stages—holds unique significance in terms of taste, texture, and market value. However, the classification of Rutab varieties remains underrepresented in the literature.MethodsTo address this gap, we present a pipeline that leverages machine learning to classify Rutab dates from images. A custom dataset comprising 1,659 images across eight popular Rutab types was collected, and several deep learning models were evaluated.Results and discussionAmong the tested models, YOLOv12 achieved the highest recall of 93%. The proposed system is deployed within a mobile application, aiming to promote cultural preservation and increase global awareness of the diversity found within date varieties.