New Directions in Weed Management and Research Using 3D Imaging
Recent innovations in 3D imaging technology have created unprecedented potential for better understanding weed responses to management tactics. Although traditional 2D imaging methods for mapping weed populations can be limited in the field by factors such as shadows and tissue overlap, 3D imaging m...
Gespeichert in:
Veröffentlicht in: | Weed science 2022-11, Vol.70 (6), p.641-647 |
---|---|
Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 647 |
---|---|
container_issue | 6 |
container_start_page | 641 |
container_title | Weed science |
container_volume | 70 |
creator | Dobbs, April M. Ginn, Daniel Skovsen, Søren Kelstrup Bagavathiannan, Muthukumar V. Mirsky, Steven B. Reberg-Horton, Chris S. Leon, Ramon G. |
description | Recent innovations in 3D imaging technology have created unprecedented potential for better understanding weed responses to management tactics. Although traditional 2D imaging methods for mapping weed populations can be limited in the field by factors such as shadows and tissue overlap, 3D imaging mitigates these challenges by using depth data to create accurate plant models. Three-dimensional imaging can be used to generate spatiotemporal maps of weed populations in the field and target weeds for site-specific weed management, including automated precision weed control. This technology will also help growers monitor cover crop performance for weed suppression and detect late-season weed escapes for timely control, thereby reducing seedbank persistence and slowing the evolution of herbicide resistance. In addition to its many applications in weed management, 3D imaging offers weed researchers new tools for understanding spatial and temporal heterogeneity in weed responses to integrated weed management tactics, including weed–crop competition and weed community dynamics. This technology will provide simple and low-cost tools for growers and researchers alike to better understand weed responses in diverse agronomic contexts, which will aid in reducing herbicide use, mitigating herbicide-resistance evolution, and improving environmental health. |
doi_str_mv | 10.1017/wsc.2022.56 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2754803871</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><cupid>10_1017_wsc_2022_56</cupid><sourcerecordid>2754803871</sourcerecordid><originalsourceid>FETCH-LOGICAL-b370t-a334b1d722de6e49dae6b4a8476466599bae2e4861f17412b1d1b96bf95d3f803</originalsourceid><addsrcrecordid>eNp90E9LwzAYBvAgCs7pyS8Q8CTSmqRJ2pxENv8MpoI4PIakfVs7bDqTjuG3N2MDL-Ip7-HH8755EDqnJKWE5tebUKaMMJYKeYBGVAiSsFyoQzQihGcJzbk4RichLAmhklE1QjfPsMHT1kM5tL0LuHX4HaDCT8aZBjpwAzauwq8QwPjyAy9C6xqcTfGsM00cT9FRbT4DnO3fMVrc371NHpP5y8NscjtPbJaTITFZxi2tcsYqkMBVZUBabgqeSy6lUMoaYMALSet4JGXRUqukrZWosrog2Rhd7HJXvv9aQxj0sl97F1fq-EMeRZHTqK52qvR9CB5qvfJtZ_y3pkRvG9KxIb1tSAsZdbLXprO-rRr4Df3bX-68bfvewb_ZP-37c1c</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2754803871</pqid></control><display><type>article</type><title>New Directions in Weed Management and Research Using 3D Imaging</title><source>Cambridge University Press Journals Complete</source><creator>Dobbs, April M. ; Ginn, Daniel ; Skovsen, Søren Kelstrup ; Bagavathiannan, Muthukumar V. ; Mirsky, Steven B. ; Reberg-Horton, Chris S. ; Leon, Ramon G.</creator><creatorcontrib>Dobbs, April M. ; Ginn, Daniel ; Skovsen, Søren Kelstrup ; Bagavathiannan, Muthukumar V. ; Mirsky, Steven B. ; Reberg-Horton, Chris S. ; Leon, Ramon G.</creatorcontrib><description>Recent innovations in 3D imaging technology have created unprecedented potential for better understanding weed responses to management tactics. Although traditional 2D imaging methods for mapping weed populations can be limited in the field by factors such as shadows and tissue overlap, 3D imaging mitigates these challenges by using depth data to create accurate plant models. Three-dimensional imaging can be used to generate spatiotemporal maps of weed populations in the field and target weeds for site-specific weed management, including automated precision weed control. This technology will also help growers monitor cover crop performance for weed suppression and detect late-season weed escapes for timely control, thereby reducing seedbank persistence and slowing the evolution of herbicide resistance. In addition to its many applications in weed management, 3D imaging offers weed researchers new tools for understanding spatial and temporal heterogeneity in weed responses to integrated weed management tactics, including weed–crop competition and weed community dynamics. This technology will provide simple and low-cost tools for growers and researchers alike to better understand weed responses in diverse agronomic contexts, which will aid in reducing herbicide use, mitigating herbicide-resistance evolution, and improving environmental health.</description><identifier>ISSN: 0043-1745</identifier><identifier>EISSN: 1550-2759</identifier><identifier>DOI: 10.1017/wsc.2022.56</identifier><language>eng</language><publisher>New York, USA: The Weed Science Society of America</publisher><subject>Accuracy ; Agricultural practices ; Automation ; Biomass ; Cover crops ; Disease management ; Environmental health ; Evolution ; Herbicide resistance ; Herbicides ; Heterogeneity ; Imaging ; Integrated weed management ; Photogrammetry ; point cloud ; Populations ; Production increases ; Real time ; Remote sensing ; REVIEW ; Seed banks ; Seeds ; stereo vision ; structure-from-motion ; Tactics ; Three dimensional imaging ; Three dimensional models ; Unmanned aerial vehicles ; Weed control ; Weeds</subject><ispartof>Weed science, 2022-11, Vol.70 (6), p.641-647</ispartof><rights>The Author(s), 2022. Published by Cambridge University Press on behalf of the Weed Science Society of America. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.</rights><rights>The Author(s), 2022. Published by Cambridge University Press on behalf of the Weed Science Society of America</rights><rights>The Author(s), 2022. Published by Cambridge University Press on behalf of the Weed Science Society of America. This work is licensed under the Creative Commons Attribution License This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b370t-a334b1d722de6e49dae6b4a8476466599bae2e4861f17412b1d1b96bf95d3f803</citedby><cites>FETCH-LOGICAL-b370t-a334b1d722de6e49dae6b4a8476466599bae2e4861f17412b1d1b96bf95d3f803</cites><orcidid>0000-0002-8549-7145 ; 0000-0001-6222-3005 ; 0000-0001-5002-106X ; 0000-0002-3207-0420 ; 0000-0002-1924-3331 ; 0000-0002-1107-7148</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.cambridge.org/core/product/identifier/S004317452200056X/type/journal_article$$EHTML$$P50$$Gcambridge$$Hfree_for_read</linktohtml><link.rule.ids>164,314,780,784,27924,27925,55628</link.rule.ids></links><search><creatorcontrib>Dobbs, April M.</creatorcontrib><creatorcontrib>Ginn, Daniel</creatorcontrib><creatorcontrib>Skovsen, Søren Kelstrup</creatorcontrib><creatorcontrib>Bagavathiannan, Muthukumar V.</creatorcontrib><creatorcontrib>Mirsky, Steven B.</creatorcontrib><creatorcontrib>Reberg-Horton, Chris S.</creatorcontrib><creatorcontrib>Leon, Ramon G.</creatorcontrib><title>New Directions in Weed Management and Research Using 3D Imaging</title><title>Weed science</title><addtitle>Weed Sci</addtitle><description>Recent innovations in 3D imaging technology have created unprecedented potential for better understanding weed responses to management tactics. Although traditional 2D imaging methods for mapping weed populations can be limited in the field by factors such as shadows and tissue overlap, 3D imaging mitigates these challenges by using depth data to create accurate plant models. Three-dimensional imaging can be used to generate spatiotemporal maps of weed populations in the field and target weeds for site-specific weed management, including automated precision weed control. This technology will also help growers monitor cover crop performance for weed suppression and detect late-season weed escapes for timely control, thereby reducing seedbank persistence and slowing the evolution of herbicide resistance. In addition to its many applications in weed management, 3D imaging offers weed researchers new tools for understanding spatial and temporal heterogeneity in weed responses to integrated weed management tactics, including weed–crop competition and weed community dynamics. This technology will provide simple and low-cost tools for growers and researchers alike to better understand weed responses in diverse agronomic contexts, which will aid in reducing herbicide use, mitigating herbicide-resistance evolution, and improving environmental health.</description><subject>Accuracy</subject><subject>Agricultural practices</subject><subject>Automation</subject><subject>Biomass</subject><subject>Cover crops</subject><subject>Disease management</subject><subject>Environmental health</subject><subject>Evolution</subject><subject>Herbicide resistance</subject><subject>Herbicides</subject><subject>Heterogeneity</subject><subject>Imaging</subject><subject>Integrated weed management</subject><subject>Photogrammetry</subject><subject>point cloud</subject><subject>Populations</subject><subject>Production increases</subject><subject>Real time</subject><subject>Remote sensing</subject><subject>REVIEW</subject><subject>Seed banks</subject><subject>Seeds</subject><subject>stereo vision</subject><subject>structure-from-motion</subject><subject>Tactics</subject><subject>Three dimensional imaging</subject><subject>Three dimensional models</subject><subject>Unmanned aerial vehicles</subject><subject>Weed control</subject><subject>Weeds</subject><issn>0043-1745</issn><issn>1550-2759</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>IKXGN</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp90E9LwzAYBvAgCs7pyS8Q8CTSmqRJ2pxENv8MpoI4PIakfVs7bDqTjuG3N2MDL-Ip7-HH8755EDqnJKWE5tebUKaMMJYKeYBGVAiSsFyoQzQihGcJzbk4RichLAmhklE1QjfPsMHT1kM5tL0LuHX4HaDCT8aZBjpwAzauwq8QwPjyAy9C6xqcTfGsM00cT9FRbT4DnO3fMVrc371NHpP5y8NscjtPbJaTITFZxi2tcsYqkMBVZUBabgqeSy6lUMoaYMALSet4JGXRUqukrZWosrog2Rhd7HJXvv9aQxj0sl97F1fq-EMeRZHTqK52qvR9CB5qvfJtZ_y3pkRvG9KxIb1tSAsZdbLXprO-rRr4Df3bX-68bfvewb_ZP-37c1c</recordid><startdate>20221101</startdate><enddate>20221101</enddate><creator>Dobbs, April M.</creator><creator>Ginn, Daniel</creator><creator>Skovsen, Søren Kelstrup</creator><creator>Bagavathiannan, Muthukumar V.</creator><creator>Mirsky, Steven B.</creator><creator>Reberg-Horton, Chris S.</creator><creator>Leon, Ramon G.</creator><general>The Weed Science Society of America</general><general>Cambridge University Press</general><scope>IKXGN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7SS</scope><scope>7T7</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7XB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M0K</scope><scope>M2O</scope><scope>M7N</scope><scope>M7P</scope><scope>MBDVC</scope><scope>P64</scope><scope>PADUT</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>RC3</scope><orcidid>https://orcid.org/0000-0002-8549-7145</orcidid><orcidid>https://orcid.org/0000-0001-6222-3005</orcidid><orcidid>https://orcid.org/0000-0001-5002-106X</orcidid><orcidid>https://orcid.org/0000-0002-3207-0420</orcidid><orcidid>https://orcid.org/0000-0002-1924-3331</orcidid><orcidid>https://orcid.org/0000-0002-1107-7148</orcidid></search><sort><creationdate>20221101</creationdate><title>New Directions in Weed Management and Research Using 3D Imaging</title><author>Dobbs, April M. ; Ginn, Daniel ; Skovsen, Søren Kelstrup ; Bagavathiannan, Muthukumar V. ; Mirsky, Steven B. ; Reberg-Horton, Chris S. ; Leon, Ramon G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b370t-a334b1d722de6e49dae6b4a8476466599bae2e4861f17412b1d1b96bf95d3f803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Agricultural practices</topic><topic>Automation</topic><topic>Biomass</topic><topic>Cover crops</topic><topic>Disease management</topic><topic>Environmental health</topic><topic>Evolution</topic><topic>Herbicide resistance</topic><topic>Herbicides</topic><topic>Heterogeneity</topic><topic>Imaging</topic><topic>Integrated weed management</topic><topic>Photogrammetry</topic><topic>point cloud</topic><topic>Populations</topic><topic>Production increases</topic><topic>Real time</topic><topic>Remote sensing</topic><topic>REVIEW</topic><topic>Seed banks</topic><topic>Seeds</topic><topic>stereo vision</topic><topic>structure-from-motion</topic><topic>Tactics</topic><topic>Three dimensional imaging</topic><topic>Three dimensional models</topic><topic>Unmanned aerial vehicles</topic><topic>Weed control</topic><topic>Weeds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dobbs, April M.</creatorcontrib><creatorcontrib>Ginn, Daniel</creatorcontrib><creatorcontrib>Skovsen, Søren Kelstrup</creatorcontrib><creatorcontrib>Bagavathiannan, Muthukumar V.</creatorcontrib><creatorcontrib>Mirsky, Steven B.</creatorcontrib><creatorcontrib>Reberg-Horton, Chris S.</creatorcontrib><creatorcontrib>Leon, Ramon G.</creatorcontrib><collection>Cambridge University Press Wholly Gold Open Access Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Research Library</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Research Library China</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><jtitle>Weed science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dobbs, April M.</au><au>Ginn, Daniel</au><au>Skovsen, Søren Kelstrup</au><au>Bagavathiannan, Muthukumar V.</au><au>Mirsky, Steven B.</au><au>Reberg-Horton, Chris S.</au><au>Leon, Ramon G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>New Directions in Weed Management and Research Using 3D Imaging</atitle><jtitle>Weed science</jtitle><addtitle>Weed Sci</addtitle><date>2022-11-01</date><risdate>2022</risdate><volume>70</volume><issue>6</issue><spage>641</spage><epage>647</epage><pages>641-647</pages><issn>0043-1745</issn><eissn>1550-2759</eissn><abstract>Recent innovations in 3D imaging technology have created unprecedented potential for better understanding weed responses to management tactics. Although traditional 2D imaging methods for mapping weed populations can be limited in the field by factors such as shadows and tissue overlap, 3D imaging mitigates these challenges by using depth data to create accurate plant models. Three-dimensional imaging can be used to generate spatiotemporal maps of weed populations in the field and target weeds for site-specific weed management, including automated precision weed control. This technology will also help growers monitor cover crop performance for weed suppression and detect late-season weed escapes for timely control, thereby reducing seedbank persistence and slowing the evolution of herbicide resistance. In addition to its many applications in weed management, 3D imaging offers weed researchers new tools for understanding spatial and temporal heterogeneity in weed responses to integrated weed management tactics, including weed–crop competition and weed community dynamics. This technology will provide simple and low-cost tools for growers and researchers alike to better understand weed responses in diverse agronomic contexts, which will aid in reducing herbicide use, mitigating herbicide-resistance evolution, and improving environmental health.</abstract><cop>New York, USA</cop><pub>The Weed Science Society of America</pub><doi>10.1017/wsc.2022.56</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0002-8549-7145</orcidid><orcidid>https://orcid.org/0000-0001-6222-3005</orcidid><orcidid>https://orcid.org/0000-0001-5002-106X</orcidid><orcidid>https://orcid.org/0000-0002-3207-0420</orcidid><orcidid>https://orcid.org/0000-0002-1924-3331</orcidid><orcidid>https://orcid.org/0000-0002-1107-7148</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0043-1745 |
ispartof | Weed science, 2022-11, Vol.70 (6), p.641-647 |
issn | 0043-1745 1550-2759 |
language | eng |
recordid | cdi_proquest_journals_2754803871 |
source | Cambridge University Press Journals Complete |
subjects | Accuracy Agricultural practices Automation Biomass Cover crops Disease management Environmental health Evolution Herbicide resistance Herbicides Heterogeneity Imaging Integrated weed management Photogrammetry point cloud Populations Production increases Real time Remote sensing REVIEW Seed banks Seeds stereo vision structure-from-motion Tactics Three dimensional imaging Three dimensional models Unmanned aerial vehicles Weed control Weeds |
title | New Directions in Weed Management and Research Using 3D Imaging |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T06%3A41%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=New%20Directions%20in%20Weed%20Management%20and%20Research%20Using%203D%20Imaging&rft.jtitle=Weed%20science&rft.au=Dobbs,%20April%20M.&rft.date=2022-11-01&rft.volume=70&rft.issue=6&rft.spage=641&rft.epage=647&rft.pages=641-647&rft.issn=0043-1745&rft.eissn=1550-2759&rft_id=info:doi/10.1017/wsc.2022.56&rft_dat=%3Cproquest_cross%3E2754803871%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2754803871&rft_id=info:pmid/&rft_cupid=10_1017_wsc_2022_56&rfr_iscdi=true |