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Pest-YOLO: A model for large-scale multi-class dense and tiny pest detection and counting.

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Title: Pest-YOLO: A model for large-scale multi-class dense and tiny pest detection and counting.
Authors: Wen C; College of Information and Technology, Jilin Agricultural University, Changchun, China.; Institute for the Smart Agriculture, Jilin Agricultural University, Changchun, China., Chen H; College of Information and Technology, Jilin Agricultural University, Changchun, China., Ma Z; College of Information and Technology, Jilin Agricultural University, Changchun, China., Zhang T; College of Information and Technology, Jilin Agricultural University, Changchun, China., Yang C; College of Food, Agricultural and Natural Resource Sciences, University of Minnesota, Twin Cities, MN, United States., Su H; College of Information and Technology, Jilin Agricultural University, Changchun, China.; Institute for the Smart Agriculture, Jilin Agricultural University, Changchun, China., Chen H; College of Information and Technology, Jilin Agricultural University, Changchun, China.; Institute for the Smart Agriculture, Jilin Agricultural University, Changchun, China.
Source: Frontiers in plant science [Front Plant Sci] 2022 Oct 25; Vol. 13, pp. 973985. Date of Electronic Publication: 2022 Oct 25 (Print Publication: 2022).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101568200 Publication Model: eCollection Cited Medium: Print ISSN: 1664-462X (Print) Linking ISSN: 1664462X NLM ISO Abbreviation: Front Plant Sci Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Wen+C%22">Wen C</searchLink>; College of Information and Technology, Jilin Agricultural University, Changchun, China.; Institute for the Smart Agriculture, Jilin Agricultural University, Changchun, China.<br /><searchLink fieldCode="AU" term="%22Chen+H%22">Chen H</searchLink>; College of Information and Technology, Jilin Agricultural University, Changchun, China.<br /><searchLink fieldCode="AU" term="%22Ma+Z%22">Ma Z</searchLink>; College of Information and Technology, Jilin Agricultural University, Changchun, China.<br /><searchLink fieldCode="AU" term="%22Zhang+T%22">Zhang T</searchLink>; College of Information and Technology, Jilin Agricultural University, Changchun, China.<br /><searchLink fieldCode="AU" term="%22Yang+C%22">Yang C</searchLink>; College of Food, Agricultural and Natural Resource Sciences, University of Minnesota, Twin Cities, MN, United States.<br /><searchLink fieldCode="AU" term="%22Su+H%22">Su H</searchLink>; College of Information and Technology, Jilin Agricultural University, Changchun, China.; Institute for the Smart Agriculture, Jilin Agricultural University, Changchun, China.<br /><searchLink fieldCode="AU" term="%22Chen+H%22">Chen H</searchLink>; College of Information and Technology, Jilin Agricultural University, Changchun, China.; Institute for the Smart Agriculture, Jilin Agricultural University, Changchun, China.
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  Data: <searchLink fieldCode="JN" term="%22101568200%22">Frontiers in plant science</searchLink> [Front Plant Sci] 2022 Oct 25; Vol. 13, pp. 973985. <i>Date of Electronic Publication: </i>2022 Oct 25 (<i>Print Publication: </i>2022).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Frontiers+Research+Foundation%22">Frontiers Research Foundation </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101568200 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Print <i>ISSN: </i>1664-462X (Print) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%221664462X%22">1664462X </searchLink><i>NLM ISO Abbreviation: </i>Front Plant Sci <i>Subsets: </i>PubMed not MEDLINE
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