mlsanityData to 14 Oct 2026

Deep Learning Techniques for In-Crop Weed Identification: A Review

arXiv 2103.14872 ↗First posted 27 Mar 2021cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 38 of its 95 references that are in the map. First posted on arXiv 27 Mar 2021.

Builds on

38 of 95 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Vision Foundation Models in Agriculture: Toward Domain-Specific Adaptation for Weed Herbicide Trials Assessmentcited by 2
  2. 2026On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shiftcited by 0