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.

翻译：随着除草剂抗性负担的加剧以及过度使用除草剂对环境的负面影响日益凸显，亟需开发新的杂草种群管理方法。这尤其适用于小麦和大麦等谷类作物——作为全球重要的主粮作物，它们占据了全球农业用地的显著比例。即便在这些主要粮食作物的杂草管理实践中取得微小改进，也将为环境和全球粮食安全带来巨大效益。黑麦草是一种主要禾本科杂草，在西北欧这一重要谷物产区对谷类作物造成严重问题，原因在于其具有高度除草剂抗性，且高度适应该地区的农艺实践。本研究利用机器视觉和多光谱成像技术，探究最先进方法在小麦和大麦作物中识别黑麦草的有效性。作为研究的一部分，我们提供了一个大型数据集，基于该数据集评估黑麦草识别的若干关键方面。首先，我们确定了不同CNN和基于Transformer的架构在未见田块图像上的识别性能。其次，我们证明了不同光谱波段对杂草分类性能的影响。最后，我们评估了数据集规模对每种测试模型分类性能的作用。研究发现，即使使用相当少量的训练数据，也能在未见田块图像上实现近90%的准确率。