Comparative performance of YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN models for detection of multiple weed species

• Developed annotated image database for cocklebur, dandelion, common waterhemp, common lambsquarters and Palmer amaranth to fine-tune YOLO and Faster R-CNN models • YOLOv11 was the fastest model with inference time of 13.5 ms, followed by YOLOv8 and YOLOv10 at 23 ms and 19.3 ms, respectively • YOLOv9 achieved highest detection accuracy with a mAP@0.5 of 0.935 • YOLO algorithms outperformed Faster R-CNN in speed and accuracy • YOLO models showed strong generalization from limited data and successfully distinguished between visually similar weed species, such as Palmer amaranth and common waterhemp. • Despite having fewer annotated images, dandelion and cocklebur had higher mAP values than Palmer amaranth and common lambsquarters, underscoring the importance of physiological characteristics over the number of images. Weeds pose a serious production challenge in various agronomic crops by reducing their grain yields. Increasing cases of herbicide-resistant (HR) weed populations further exacerbate the problem. Future weed control tactics require the integration of non-chemical and reduced chemical-based strategies that can target site- and specie-specific weed management (SSSWM). Advanced machine learning technology has the potential to localize and detect weed seedlings to implement SSSWM. However, due to large biological variability among various weed species and environmental conditions where they grow, accurate and precise weed detection remains challenging. The main objectives of this research were to (1) develop an annotated image database of cocklebur ( Xanthium strumarium L.), dandelion ( Taraxacum officinale ), common waterhemp ( Amaranthus tuberculatus ), Palmer amaranth ( Amaranthus palmeri ) and common lambsquarters ( Chenopodium album L.), and (2) investigate the comparative performance (speed and accuracy) of YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN algorithms in detecting those weed species. A weed dataset with bounding box annotations for each weed species was created, consisting of images collected under variable field conditions which were preprocessed and augmented to create a dataset of 2348 color images. The YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN were trained using the annotated weed image database to detect each weed species. Results indicated that YOLOv11 was the fastest model with inference time of 13.5 milliseconds (ms) followed by YOLOv8 and YOLOv10 with inference time 23 and 19.3 milliseconds (ms), respectively. The YOLOv9 had the highest accuracy in detecting different weed species with an overall mean average precision (mAP@0.5) of 0.935. In contrast, the Detectron2 with Fast R-CNN configuration provided mAP@0.5 of 0.821 with an inference time of 63.8 ms. These results suggest that the YOLO series algorithms have the potential for real-time deployment for weed species detection more accurately and faster than Faster R-CNN in agricultural fields.