<![CDATA[Strawberries hold significant economic value in Indonesia due to their high demand and nutritional benefits. Traditional harvesting methods, which rely on manual visual inspection, are often inefficient and prone to errors. Real-time multi-object detection presents a promising solution to enhance automation in harvesting, ripeness classification, and post-harvest processing. This study assesses the performance of four YOLOv11 variants—YOLOv11N, YOLOv11S, YOLOv11M, and YOLOv11L—in detecting strawberries across five quality and ripeness categories: Unripe, Half Ripe Grade B, Half Ripe Grade A, Fully Ripe Grade B, and Fully Ripe Grade A. A dataset originally consisting of 3,055 high-resolution strawberry images was expanded through data augmentation to 7,940 images. These were subsequently split into training (7,330 images), validation (305 images), and testing (305 images) sets. All models were trained under identical conditions utilizing the AdamW optimizer, cosine annealing learning rate scheduling, a batch size of 16, and an input resolution of 640×640 pixels. Performance was evaluated based on Precision, Recall, F1-Score, mAP@0.5, mAP@0.95, and inference time. The results indicate that YOLOv11N achieved the best overall performance, with a Precision of 0.869, Recall of 0.878, F1-Score of 0.87, mAP@0.95 of 0.830, and the fastest inference time of 3.6 ms, rendering it suitable for real-time deployment. YOLOv11M provided a balanced trade-off between accuracy and speed, while YOLOv11S offered competitive accuracy with lower inference latency. YOLOv11L demonstrated strong detection capabilities but with the slowest inference time. These findings affirm the efficacy of YOLOv11-based models in facilitating scalable and intelligent systems for precision agriculture. Keywords—strawberry, computer vision, deep learning, object detection, classification, YOLO.]]>
