  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.
