Automatic detection of insect predation through the segmentation of damaged leaves

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dc.creatorGabrielda Silva Vieira
dc.creatorBruno Moraes Rocha
dc.creatorAfonso Ueslei Fonseca
dc.creatorNaiane Maria de Sousa
dc.creatorJulio Cesar Ferreira
dc.creatorChristian Dias Cabacinha
dc.creatorFabrizzio Soares
dc.date.accessioned2023-11-07T11:55:09Z
dc.date.accessioned2025-09-09T00:31:28Z
dc.date.available2023-11-07T11:55:09Z
dc.date.issued2022
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.identifier.doihttps://doi.org/10.1016/j.atech.2022.100056
dc.identifier.issn2772-3755
dc.identifier.urihttps://hdl.handle.net/1843/60550
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.rightsAcesso Aberto
dc.subjectFolhas -- Anatomia
dc.subjectInsetos como agentes de controle biológico de pragas
dc.subjectInteligência computacional
dc.subjectProdutividade agrícola
dc.titleAutomatic detection of insect predation through the segmentation of damaged leaves
dc.typeArtigo de periódico
local.citation.epage11
local.citation.spage1
local.citation.volume2
local.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.
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S2772375522000211

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