Performance Analysis of YOLOv11: Nano, Small, and Medium Models for Herbal Leaf Classification

Indonesian people, especially the younger generation, often overlook the great potential of herbal leaves that are easily found around their homes. These leaves not only offer health benefits but also hold significant economic value. This research developed a system to classify 10 types of herbal leaves (<i>Annona muricata</i>, <i>Anredera cordifolia</i>, <i>Piper betle</i>, <i>Ocimum basilicum</i>, <i>Peperomia pellucida</i>, <i>Psidium guajava</i>, <i>Isotoma longiflora</i>, <i>Coleus scutellarioides</i>, <i>Ageratum conyzoides</i>, and <i>Syzygium polyanthum</i>) using artificial intelligence (AI). The study employed the Convolutional Neural Network (CNN) method and the You Only Look Once (YOLO) v11 algorithm, focusing on evaluating the performance of YOLOv11 in three variants, Nano, Small, and Medium. The results showed that the YOLOv11 Medium variant achieved the best performance, with the highest mAP50-95 value of 0.743 and mAP50 of 0.974 at the last epoch. The YOLOv11 Small variant outperformed Nano in precision (0.947 vs. 0.933) and mAP50 (0.973 vs. 0.972), while YOLOv11 Nano had slightly higher recall (0.921 vs. 0.906). Confusion Matrix results for YOLOv11 Medium showed precision (P) = 0.932, recall (R) = 0.928, mAP50 = 0.974, and mAP50-95 = 0.743. Based on these metrics, YOLOv11 Medium stood out as the best-performing variant, followed by Small and Nano. This research highlights the potential of AI technology to enhance the utilization of herbal leaves, which can provide broader health benefits and support the local economy.