A Multi-Stage Pipeline for Date Fruit Processing: Integrating YOLOv11 Detection, Classification, and Automated Counting

In this study, an automated multimodal system for detecting, classifying, and dating fruit was developed using a two-stage YOLOv11 pipeline. In the first stage, the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them. These bounding boxes are subsequently passed to a YOLOv11 classification model, which analyzes cropped images and assigns class labels. An additional counting module automatically tallies the detected fruits, offering a near-instantaneous estimation of quantity. The experimental results suggest high precision and recall for detection, high classification accuracy (across 15 classes), and near-perfect counting in real time. This paper presents a multi-stage pipeline for date fruit detection, classification, and automated counting, employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput. The results demonstrated that the detection precision exceeded 90%, the classification accuracy approached 92%, and the counting module correlated closely with the manual tallies. These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes. Future studies will include dataset expansion, user-centric interfaces, and integration with harvesting robotics.