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。

WeedScout：基于专用硬件的实时自主黑草分类与制图

WeedScout: Real-Time Autonomous blackgrass Classification and Mapping  using dedicated hardware

As the burden of herbicide resistance grows and the environmental
repercussions of excessive herbicide use become clear, new ways of managing
weed populations are needed. This is particularly true for cereal crops, like
wheat and barley, that are staple food crops and occupy a globally significant
portion of agricultural land. Even small improvements in weed management
practices across these major food crops worldwide would yield considerable
benefits for both the environment and global food security. Blackgrass is a
major grass weed which causes particular problems in cereal crops in north-west
Europe, a major cereal production area, because it has high levels of of
herbicide resistance and is well adapted to agronomic practice in this region.
With the use of machine vision and multispectral imaging, we investigate the
effectiveness of state-of-the-art methods to identify blackgrass in wheat and
barley crops. As part of this work, we provide a large dataset with which we
evaluate several key aspects of blackgrass weed recognition. Firstly, we
determine the performance of different CNN and transformer-based architectures
on images from unseen fields. Secondly, we demonstrate the role that different
spectral bands have on the performance of weed classification. Lastly, we
evaluate the role of dataset size in classification performance for each of the
models trialled. We find that even with a fairly modest quantity of training
data an accuracy of almost 90% can be achieved on images from unseen fields.

利用机器视觉和多光谱成像技术，研究了在小麦和大麦作物中识别黑草的先进方法的有效性，并提供了大量数据集来评估黑草杂草识别的几个关键方面，包括不同卷积神经网络和 Transformer 架构的性能，不同光谱波段的作用以及数据集规模对分类性能的影响。研究结果表明，即使使用相对较少的训练数据，也可以在未知领域的图像上达到近 90% 的准确率。