Blackgrass (Alopecurus myosuroides) is a competitive weed that has
wide-ranging impacts on food security by reducing crop yields and increasing
cultivation costs. In addition to the financial burden on agriculture, the
application of herbicides as a preventive to blackgrass can negatively affect
access to clean water and sanitation. The WeedScout project introduces a
Real-Rime Autonomous Black-Grass Classification and Mapping (RT-ABGCM), a
cutting-edge solution tailored for real-time detection of blackgrass, for
precision weed management practices. Leveraging Artificial Intelligence (AI)
algorithms, the system processes live image feeds, infers blackgrass density,
and covers two stages of maturation. The research investigates the deployment
of You Only Look Once (YOLO) models, specifically the streamlined YOLOv8 and
YOLO-NAS, accelerated at the edge with the NVIDIA Jetson Nano (NJN). By
optimising inference speed and model performance, the project advances the
integration of AI into agricultural practices, offering potential solutions to
challenges such as herbicide resistance and environmental impact. Additionally,
two datasets and model weights are made available to the research community,
facilitating further advancements in weed detection and precision farming
technologies.

该研究介绍了一个实时黑草分类与制图系统（RT-ABGCM），使用人工智能算法处理图像数据，推断黑草密度并覆盖两个成熟阶段，以实现精准的杂草管理，并探索了加速边缘端的 YOLOv8 和 YOLO-NAS 模型在农业实践中的应用，提供 AI 在农业实践中的潜在解决方案。此外，研究将两个数据集和模型权重提供给研究社区，促进杂草检测和精准农业技术的进一步发展。