  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.
