Weeds are a significant threat to the agricultural productivity and the environment. The increasing demand for sustainable agriculture has driven innovations in accurate weed control technologies aimed at reducing the reliance on herbicides. 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 progresses on 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.

翻译：杂草对农业生产力和环境构成重大威胁。可持续农业需求的日益增长推动了精准除草技术的创新，旨在减少对除草剂的依赖。随着深度学习在各类视觉任务中取得巨大成功，许多基于图像的杂草检测算法已被开发出来。本文综述了深度学习技术在基于图像的杂草检测领域的最新进展。首先介绍了与杂草检测相关的深度学习基础知识。接着，结合研究材料（包括公开杂草数据集）的讨论，回顾了深度杂草检测的最新成果。最后，总结了开发可实际部署的杂草检测方法所面临的挑战，并讨论了未来研究的机遇。希望本综述能为该领域提供及时的总结，并吸引更多研究者关注这一跨学科研究问题。