Abstract
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.
Abstract (translated)
随着除草剂抗性的增加以及过度使用除草剂对环境影响的明确，需要开发新的方法来管理杂草。这对主要粮食作物（如小麦和大麦）来说尤为重要，这些作物占据了全球农业土地面积的很大比例。即使在这些主要粮食作物上的杂草管理实践的微小改进，也能为环境和全球粮食安全带来显著好处。黑草是一种主要的大麦草，在西北欧的小麦和大麦作物中引起了 particular 问题，因为它的除草剂抗性水平很高，并且对这一地区的农业实践特别适应。利用机器视觉和多光谱成像技术，我们研究了最先进的方法识别黑草在小麦和黑麦作物中的有效性。 作为这项工作的一部分，我们提供了大量数据集，用于评估几种黑草杂草识别的关键方面。首先，我们确定不同卷积神经网络和转换器架构在未见过的场地上图像的性能。其次，我们证明了不同光谱带在杂草分类中的作用。最后，我们评估了每个试点模型对分类性能的影响。我们发现，即使只有很小的训练数据，来自未见过的场地的图像上的准确性也可以达到近 90%。
URL
https://arxiv.org/abs/2405.02218
