Grasslands are the mainstay of terrestrial ecosystems and crucial ecological barriers, serving as the foundation for the development of grassland husbandry. However, the frequent occurrence of poisonous plants in grasslands weakens the stability of grassland ecosystems and constrains the growth of grassland livestock husbandry. To achieve early detection of the grassland weed Phlomoides umbrosa (Turcz.) Kamelin & Makhm, this study improves the YOLO-v8 model and proposes a BSS-YOLOv8 network model using UAV images. Using UAV, we can obtain early-stage image data of P. umbrosa and build a seedling dataset. To address challenges such as the complex grassland background and the dwarf seedlings of P. umbrosa, this study incorporated the BoTNet module into the backbone network of the YOLO-v8 model. Enhancing the integrity of feature extraction by linking global and local features through its multi-head self-attention mechanism (MHSA). Additionally, a detection layer was added in the model’s neck structure with an output feature map scale of 160 × 160 to further integrate P. umbrosa feature details from the shallow neural network, thereby strengthening the recognition of small target P. umbrosa. The use of GSConv, as a replacement for some standard convolutions, not only reduced model computational complexity but also further improved its detection performance. Ablation test results reveal that the BSS-YOLOv8 network model achieved a precision of 91.1%, a recall rate of 86.7%, an mAP50 of 92.6%, an F1-Score of 88.85%, and an mAP50:95 of 61.3% on the P. umbrosa seedling dataset. Compared with the baseline network, it demonstrated respective improvements of 2.5%, 3.8%, 3.4%, 3.19%, and 4.4%. When compared to other object detection models (YOLO-v5, Faster R-CNN, etc.), the BSS-YOLOv8 model similarly achieved the best detection performance. The BSS-YOLOv8 proposed in this study enables rapid identification of P. umbrosa seedlings in grassland backgrounds, holding significant importance for early detection and control of weeds in grasslands.

The frequent occurrence of poisonous grassland weed disasters has weakened the stability of the grassland ecosystem in the Ili region of Xinjiang, and its spread has seriously affected the development of grassland animal husbandry. However, there is no relevant literature to report the species and distribution of poisonous grassland weeds in the Ili region. In this study, investigation was carried out using methods such as personal inspection and sampling methods, and importance-value and diversity analyses were conducted. The results showed that there were 126 species of poisonous weeds in the Ili region, belonging to 91 genera and 28 families. The poisonous weeds of Compositae and Leguminosae accounted for 33.3%. The Shannon–Wiener diversity index of the poisonous weed population was 3.99, the richness of the poisonous weed population was high, and the distribution range was uniform. The Simpson diversity index of poisonous weeds population was 0.97, and the highest importance value of poisonous weeds was Urtica cannabina L., with an importance value of 11.50. The evenness index of the poisonous weed population was 0.83; the spatial distribution pattern of the poisonous weed population tended to aggregate distribution. This study lays a foundation for the control of poisonous weeds and the sustainable development of grasslands in the Ili region of Xinjiang.