A Special Collection: Drones to Improve Insect Pest Management
The Special Collection Drones to Improve Insect Pest Management presents research and development of unmanned (or uncrewed) aircraft system (UAS, or drone) technology to improve insect pest management. The articles bridge from more foundational studies (i.e., evaluating and refining abilities of dro...
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Veröffentlicht in: | Journal of economic entomology 2021-10, Vol.114 (5), p.1853-1856 |
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creator | Moses-Gonzales, Nathan Brewer, Michael J |
description | The Special Collection Drones to Improve Insect Pest Management presents research and development of unmanned (or uncrewed) aircraft system (UAS, or drone) technology to improve insect pest management. The articles bridge from more foundational studies (i.e., evaluating and refining abilities of drones to detect pest concerns or deliver pest management materials) to application-oriented case studies (i.e., evaluating opportunities and challenges of drone use in pest management systems). The collection is composed of a combination of articles presenting information first-time published, and a selection of articles previously published in Journal of Economic Entomology (JEE). Articles in the Collection, as well as selected citations of articles in other publications, reflect the increase in entomology research using drones that has been stimulated by advancement in drone structural and software engineering such as autonomous flight guidance; in- and post-flight data storage and processing; and companion advances in spatial data management and analyses including machine learning and data visualization. The Collection is also intended to stimulate discussion on the role of JEE as a publication venue for future articles on drones as well as other cybernectic-physical systems, big data analyses, and deep learning processes. While these technologies have their genesis in fields arguably afar from the discipline of entomology, we propose that interdisciplinary collaboration is the pathway for applications research and technology transfer leading to an acceleration of research and development of these technologies to improve pest management. |
doi_str_mv | 10.1093/jee/toab081 |
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The articles bridge from more foundational studies (i.e., evaluating and refining abilities of drones to detect pest concerns or deliver pest management materials) to application-oriented case studies (i.e., evaluating opportunities and challenges of drone use in pest management systems). The collection is composed of a combination of articles presenting information first-time published, and a selection of articles previously published in Journal of Economic Entomology (JEE). Articles in the Collection, as well as selected citations of articles in other publications, reflect the increase in entomology research using drones that has been stimulated by advancement in drone structural and software engineering such as autonomous flight guidance; in- and post-flight data storage and processing; and companion advances in spatial data management and analyses including machine learning and data visualization. The Collection is also intended to stimulate discussion on the role of JEE as a publication venue for future articles on drones as well as other cybernectic-physical systems, big data analyses, and deep learning processes. While these technologies have their genesis in fields arguably afar from the discipline of entomology, we propose that interdisciplinary collaboration is the pathway for applications research and technology transfer leading to an acceleration of research and development of these technologies to improve pest management.</description><identifier>ISSN: 0022-0493</identifier><identifier>EISSN: 1938-291X</identifier><identifier>DOI: 10.1093/jee/toab081</identifier><language>eng</language><publisher>US: Entomological Society of America</publisher><subject>actuation drones ; Biological control ; Case studies ; Data management ; Data storage ; Deep learning ; Drones ; Entomology ; Flight ; Geospatial data ; Imaging systems ; Industrial research ; Information storage and retrieval ; insect pest management ; Insect pests ; Insects ; Learning algorithms ; Machine learning ; Pest control ; Pests ; R&D ; Research & development ; Spatial analysis ; spectral imaging technology ; Technology transfer ; unmanned aerial system</subject><ispartof>Journal of economic entomology, 2021-10, Vol.114 (5), p.1853-1856</ispartof><rights>The Author(s) 2021. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. journals.permissions@oup.com</rights><rights>The Author(s) 2021. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. 2021</rights><rights>COPYRIGHT 2021 Oxford University Press</rights><rights>The Author(s) 2021. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b435t-146a9d2d6768e57e603a6267e6a5aac05a329c4d843f6f5f4c56ce96b4a7dff63</citedby><cites>FETCH-LOGICAL-b435t-146a9d2d6768e57e603a6267e6a5aac05a329c4d843f6f5f4c56ce96b4a7dff63</cites><orcidid>0000-0001-5556-8304 ; 0000-0002-4159-4138</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,1584,27924,27925</link.rule.ids></links><search><contributor>Rodriguez-Saona, Cesar</contributor><creatorcontrib>Moses-Gonzales, Nathan</creatorcontrib><creatorcontrib>Brewer, Michael J</creatorcontrib><title>A Special Collection: Drones to Improve Insect Pest Management</title><title>Journal of economic entomology</title><description>The Special Collection Drones to Improve Insect Pest Management presents research and development of unmanned (or uncrewed) aircraft system (UAS, or drone) technology to improve insect pest management. The articles bridge from more foundational studies (i.e., evaluating and refining abilities of drones to detect pest concerns or deliver pest management materials) to application-oriented case studies (i.e., evaluating opportunities and challenges of drone use in pest management systems). The collection is composed of a combination of articles presenting information first-time published, and a selection of articles previously published in Journal of Economic Entomology (JEE). Articles in the Collection, as well as selected citations of articles in other publications, reflect the increase in entomology research using drones that has been stimulated by advancement in drone structural and software engineering such as autonomous flight guidance; in- and post-flight data storage and processing; and companion advances in spatial data management and analyses including machine learning and data visualization. The Collection is also intended to stimulate discussion on the role of JEE as a publication venue for future articles on drones as well as other cybernectic-physical systems, big data analyses, and deep learning processes. While these technologies have their genesis in fields arguably afar from the discipline of entomology, we propose that interdisciplinary collaboration is the pathway for applications research and technology transfer leading to an acceleration of research and development of these technologies to improve pest management.</description><subject>actuation drones</subject><subject>Biological control</subject><subject>Case studies</subject><subject>Data management</subject><subject>Data storage</subject><subject>Deep learning</subject><subject>Drones</subject><subject>Entomology</subject><subject>Flight</subject><subject>Geospatial data</subject><subject>Imaging systems</subject><subject>Industrial research</subject><subject>Information storage and retrieval</subject><subject>insect pest management</subject><subject>Insect pests</subject><subject>Insects</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Pest control</subject><subject>Pests</subject><subject>R&D</subject><subject>Research & development</subject><subject>Spatial analysis</subject><subject>spectral imaging technology</subject><subject>Technology transfer</subject><subject>unmanned aerial system</subject><issn>0022-0493</issn><issn>1938-291X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><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>eNqFkE1Lw0AQhhdRsFZP_oGAIIqk3e9kPQilfhUqCip4C5vNbElJsjWbCP57t6QnD8oeZpl5ZuadF6FTgicEKzZdA0w7p3Ockj00IoqlMVXkYx-NMKY0xlyxQ3Tk_RpjIinBI3Qzi143YEpdRXNXVWC60jXX0W3rGvBR56JFvWndF0SLxodi9AK-i550o1dQQ9MdowOrKw8nuzhG7_d3b_PHePn8sJjPlnHOmehiwqVWBS1kIlMQCUjMtKQyfLTQ2mChGVWGFylnVlphuRHSgJI510lhrWRjdDHMDWI--6Ahq0tvoKp0A673GRVcqFRRxgN69gtdu75tgrpAqRTTVARjxmgyUCtdQVY21nWtNuEVUJcmHG_LkJ9JlRIhE7odezU0mNZ534LNNm1Z6_Y7Izjbmp8F87Od-YE-H2jXb_4BLwcwL13Y-if7A-YkkYg</recordid><startdate>20211001</startdate><enddate>20211001</enddate><creator>Moses-Gonzales, Nathan</creator><creator>Brewer, Michael J</creator><general>Entomological Society of America</general><general>Oxford University Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-5556-8304</orcidid><orcidid>https://orcid.org/0000-0002-4159-4138</orcidid></search><sort><creationdate>20211001</creationdate><title>A Special Collection: Drones to Improve Insect Pest Management</title><author>Moses-Gonzales, Nathan ; Brewer, Michael J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b435t-146a9d2d6768e57e603a6267e6a5aac05a329c4d843f6f5f4c56ce96b4a7dff63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>actuation drones</topic><topic>Biological control</topic><topic>Case studies</topic><topic>Data management</topic><topic>Data storage</topic><topic>Deep learning</topic><topic>Drones</topic><topic>Entomology</topic><topic>Flight</topic><topic>Geospatial data</topic><topic>Imaging systems</topic><topic>Industrial research</topic><topic>Information storage and retrieval</topic><topic>insect pest management</topic><topic>Insect pests</topic><topic>Insects</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Pest control</topic><topic>Pests</topic><topic>R&D</topic><topic>Research & development</topic><topic>Spatial analysis</topic><topic>spectral imaging technology</topic><topic>Technology transfer</topic><topic>unmanned aerial system</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Moses-Gonzales, Nathan</creatorcontrib><creatorcontrib>Brewer, Michael J</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</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>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</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 China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of economic entomology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Moses-Gonzales, Nathan</au><au>Brewer, Michael J</au><au>Rodriguez-Saona, Cesar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Special Collection: Drones to Improve Insect Pest Management</atitle><jtitle>Journal of economic entomology</jtitle><date>2021-10-01</date><risdate>2021</risdate><volume>114</volume><issue>5</issue><spage>1853</spage><epage>1856</epage><pages>1853-1856</pages><issn>0022-0493</issn><eissn>1938-291X</eissn><abstract>The Special Collection Drones to Improve Insect Pest Management presents research and development of unmanned (or uncrewed) aircraft system (UAS, or drone) technology to improve insect pest management. 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The Collection is also intended to stimulate discussion on the role of JEE as a publication venue for future articles on drones as well as other cybernectic-physical systems, big data analyses, and deep learning processes. While these technologies have their genesis in fields arguably afar from the discipline of entomology, we propose that interdisciplinary collaboration is the pathway for applications research and technology transfer leading to an acceleration of research and development of these technologies to improve pest management.</abstract><cop>US</cop><pub>Entomological Society of America</pub><doi>10.1093/jee/toab081</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0001-5556-8304</orcidid><orcidid>https://orcid.org/0000-0002-4159-4138</orcidid><oa>free_for_read</oa></addata></record> |
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ispartof | Journal of economic entomology, 2021-10, Vol.114 (5), p.1853-1856 |
issn | 0022-0493 1938-291X |
language | eng |
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source | Oxford University Press Journals All Titles (1996-Current); Alma/SFX Local Collection |
subjects | actuation drones Biological control Case studies Data management Data storage Deep learning Drones Entomology Flight Geospatial data Imaging systems Industrial research Information storage and retrieval insect pest management Insect pests Insects Learning algorithms Machine learning Pest control Pests R&D Research & development Spatial analysis spectral imaging technology Technology transfer unmanned aerial system |
title | A Special Collection: Drones to Improve Insect Pest Management |
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