Palm Fruit Ripeness and Quality Detection System Using YOLOv11

The ripeness level of palm fruit is a crucial factor that determines the quality and efficiency of palm oil production. Manual ripeness assessment is often subjective, inconsistent, and time-consuming, creating the need for an automated solution. Therefore, an automated approach using computer vision is needed to ensure efficiency and consistency. To address this need, this study implements the YOLOv11 deep learning model to classify palm fruit into four categories (unripe, underripe, ripe, and overripe). The dataset, obtained from Roboflow, consists of 800 annotated images evenly distributed across the four classes. Data preparation included resizing images to 640×640 pixels and applying augmentation techniques to improve model generalization. The model was trained for 100 epochs on google colab with GPU L4 acceleration. Evaluation results demonstrate high performance with mAP@0.5 of 97.4% and mAP@0.5:0.95 of 94.1%, alongside precision of 94.7% and recall of 90.6%. The best performance was achieved on the unripe and underripe classes, while the ripe category showed relatively lower accuracy due to visual similarities with adjacent classes. These findings confirm that YOLOv11 is an effective and efficient approach for automatic palm fruit ripeness detection, offering potential benefits for harvesting optimization and supporting smart farming practices.