Multispectral fine-grained classification of blackgrass in wheat and barley crops
Madeleine Darbyshire, Shaun Coutts, Eleanor Hammond, and 6 more authors
Computers and Electronics in Agriculture, 2025
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 herbicide resistance. Moreover, detecting blackgrass in grass crops is challenging due to its visual similarity to these crops. Despite this, a systematic review of the literature on weed recognition in wheat and barley, included in this study, highlights that blackgrass – and grass weeds more broadly – have received less research attention compared to certain broadleaf weeds. 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 present the Eastern England Blackgrass Dataset, 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. All models tested achieved an accuracy greater than 80%. Our best model achieved 89.6% and that only half the training data was required to achieve this performance. Our dataset is available at: https://lcas.lincoln.ac.uk/wp/research/data-sets-software/eastern-england-blackgrass-dataset/.
Spatial Receptor Allocation for a Multiple Access Hub in NanonetworksIEEE Transactions on Molecular, Biological, and Multi-Scale Communications, 2019
