Weeds are a significant threat to agricultural productivity and the environment. The increasing demand for sustainable weed control practices has driven innovative developments in alternative weed control technologies aimed at reducing the reliance on herbicides. The barrier to adoption of these technologies for selective in-crop use is availability of suitably effective weed recognition. With the great success of deep learning in various vision tasks, many promising image-based weed detection algorithms have been developed. This paper reviews recent developments of deep learning techniques in the field of image-based weed detection. The review begins with an introduction to the fundamentals of deep learning related to weed detection. Next, recent advancements in deep weed detection are reviewed with the discussion of the research materials including public weed datasets. Finally, the challenges of developing practically deployable weed detection methods are summarized, together with the discussions of the opportunities for future research. We hope that this review will provide a timely survey of the field and attract more researchers to address this inter-disciplinary research problem.

杂草对农业生产力和环境构成重大威胁。对可持续杂草控制实践的需求不断增长，推动了旨在减少对除草剂依赖的替代杂草控制技术的创新发展。采用这些技术在作物上选择性使用的障碍是能否有效识别杂草。随着深度学习在各种视觉任务中的巨大成功，许多有前途的基于图像的杂草检测算法已经被开发出来。本文回顾了深度学习技术在基于图像的杂草检测领域的最新进展。本综述首先介绍了与杂草检测相关的深度学习的基础知识。接下来，通过对包括公共杂草数据集在内的研究材料的讨论，回顾了深层杂草检测的最新进展。最后，总结了开发实用的杂草检测方法所面临的挑战，并讨论了未来研究的机会。我们希望这篇综述能够对该领域提供及时的调查，并吸引更多的研究人员来解决这个跨学科的研究问题。