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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/60550
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dc.creatorGabrielda Silva Vieirapt_BR
dc.creatorBruno Moraes Rochapt_BR
dc.creatorAfonso Ueslei Fonsecapt_BR
dc.creatorNaiane Maria de Sousapt_BR
dc.creatorJulio Cesar Ferreirapt_BR
dc.creatorChristian Dias Cabacinhapt_BR
dc.creatorFabrizzio Soarespt_BR
dc.date.accessioned2023-11-07T11:55:09Z-
dc.date.available2023-11-07T11:55:09Z-
dc.date.issued2022-
dc.citation.volume2pt_BR
dc.citation.spage1pt_BR
dc.citation.epage11pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.atech.2022.100056pt_BR
dc.identifier.issn2772-3755pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/60550-
dc.description.resumoLeveraged by the production of grains, oilseeds, and fresh deciduous fruits, food production has reached new heights, exceeding the amount produced in previous years and with an estimate of new records for the coming years. In this sense, technological advances are essential to reduce costs and increase quality and productivity. In this paper, we present a novel method to detect insect predation on plant leaves that uses geometric leaf properties and digital image processing techniques to construct image models. Unlike other approaches, our method detects and highlights the regions of leaves attacked by insects and segments the contours of insect bites. We evaluated our proposal considering 12 crucial crops for the world market, and it demonstrated to be effective, even in the presence of noise, image scale, and rotation. Besides, it identifies insect predation areas regardless of the plant species with precision above 90% in blueberry, corn, potato, and soybean leaves. Thus, this proposal introduces a new approach to automatic leaf analysis and contributes to reducing human effort in identifying the occurrence of pests. The code prepared by the authors is publicly available.pt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.rightsAcesso Abertopt_BR
dc.subject.otherFolhas -- Anatomiapt_BR
dc.subject.otherInsetos como agentes de controle biológico de pragaspt_BR
dc.subject.otherInteligência computacionalpt_BR
dc.subject.otherProdutividade agrícolapt_BR
dc.titleAutomatic detection of insect predation through the segmentation of damaged leavespt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.sciencedirect.com/science/article/pii/S2772375522000211pt_BR
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