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This review provides a comprehensive synthesis of recent progress in deep learning-based weed detection, with a focus on three major model families: object detection, image segmentation, and image classification. For each category, representative architectures, key algorithmic features, and typical agricultural application scenarios are summarized and compared. The strengths and limitations of these approaches\u2014particularly in terms of spatial localization, pixel-level delineation, computational efficiency, and model generalization\u2014are critically analyzed. In addition, major challenges such as dataset scarcity, annotation cost, variability in weed morphology, and real-time deployment constraints are discussed, along with emerging solutions including crop-based indirect detection, semi-supervised learning, and model\u2013actuator integration. This review highlights future opportunities toward scalable, data-efficient, and precision-integrated weed management, offering guidance for the development of next-generation intelligent weeding systems.<\/jats:p>","DOI":"10.3389\/fpls.2025.1746406","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T06:41:04Z","timestamp":1767940864000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep learning\u2013based approaches for weed detection in crops"],"prefix":"10.3389","volume":"16","author":[{"given":"Hua","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"11833","DOI":"10.1007\/s00521-023-08324-3","article-title":"Development of A compressed fcn architecture for semantic segmentation using particle swarm optimization","volume":"35","author":"Agarwal","year":"2023","journal-title":"Neural Computing And Appl."},{"key":"B2","doi-asserted-by":"publisher","first-page":"106081","DOI":"10.1016\/j.compag.2021.106081","article-title":"Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems","volume":"184","author":"Ahmad","year":"2021","journal-title":"Comput. 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