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Advances in site-specific weed management in agriculture: A review

dc.contributor.authorGerhards, Roland
dc.contributor.authorAndújar Sanchez, Dionisio
dc.contributor.authorHamouz, Pavel
dc.contributor.authorPeteinatos, Gerassimos G.
dc.contributor.authorChristensen, Svend
dc.contributor.authorFernandez‐Quintanilla, Cesar
dc.date.accessioned2024-10-23T12:25:43Z
dc.date.available2024-10-23T12:25:43Z
dc.date.issued2022de
dc.description.abstractThe developments of information and automation technologies have opened a new era for weed management to fit physical and chemical control treatments to the spatial and temporal heterogeneity of weed distributions in agricultural fields. This review describes the technologies of site‐specific weed management (SSWM) systems, evaluates their ecological and economic benefits and gives a perspective for the implementation in practical farming. Sensor technologies including 3D cameras, multispectral imaging and Artificial Intelligence (AI) for weed classification and computer‐based decision algorithms are described in combination with precise spraying and hoeing operations. Those treatments are targeted for patches of weeds or individual weed plants. Cameras can also guide inter‐row hoes precisely in the centre between two crop rows at much higher driving speed. Camera‐guided hoeing increased selectivity and weed control efficacy compared with manual steered hoeing. Robots combine those guiding systems with in‐row hoeing or spot spraying systems that can selectively control individual weeds within crop rows. Results with patch spraying show at least 50% saving of herbicides in various crops without causing additional costs for weed control in the following years. A challenge with these technologies is the interoperability of sensing and controllers. Most of the current SSWM technologies use their own IT protocols that do not allow connecting different sensors and implements. Plug & play standards for linking detection, decision making and weeding would improve the adoption of new SSWM technologies and reduce operational costs. An important impact of SSWM is the potential contribution to the EU‐Green Deal targets to reduce pesticide use and increase biodiversity. However, further on‐farm research is needed for integrating those technologies into agricultural practice.en
dc.identifier.swb179584664X
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/16774
dc.identifier.urihttps://doi.org/10.1111/wre.12526
dc.language.isoengde
dc.rights.licensecc_by-nc-ndde
dc.source1365-3180de
dc.sourceWeed research; Vol. 62, No. 2 (2022), 123-133de
dc.subjectArtificial Intelligence
dc.subjectPatch spraying
dc.subjectPrecision farming
dc.subjectRobotic weeding
dc.subjectSensor technologies
dc.subjectWeed mapping
dc.subject.ddc630
dc.titleAdvances in site-specific weed management in agriculture: A reviewen
dc.type.diniArticle
dcterms.bibliographicCitationWeed research, 62 (2022), 2, 123-133. https://doi.org/10.1111/wre.12526. ISSN: 1365-3180
dcterms.bibliographicCitation.issn1365-3180
dcterms.bibliographicCitation.issue2
dcterms.bibliographicCitation.journaltitleWeed research
dcterms.bibliographicCitation.volume62
local.export.bibtex@article{Gerhards2022, url = {https://hohpublica.uni-hohenheim.de/handle/123456789/16774}, doi = {10.1111/wre.12526}, author = {Gerhards, Roland and Andújar Sanchez, Dionisio and Hamouz, Pavel et al.}, title = {Advances in site‐specific weed management in agriculture—A review}, journal = {1365-3180}, year = {2022}, volume = {62}, number = {2}, }
local.export.bibtexAuthorGerhards, Roland and Andújar Sanchez, Dionisio and Hamouz, Pavel et al.
local.export.bibtexKeyGerhards2022
local.export.bibtexType@article

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