Use este identificador para citar ou linkar para este item: http://hdl.handle.net/1843/60550
Tipo: Artigo de Periódico
Título: Automatic detection of insect predation through the segmentation of damaged leaves
Autor(es): Gabrielda Silva Vieira
Bruno Moraes Rocha
Afonso Ueslei Fonseca
Naiane Maria de Sousa
Julio Cesar Ferreira
Christian Dias Cabacinha
Fabrizzio Soares
Resumo: Leveraged 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.
Assunto: Folhas -- Anatomia
Insetos como agentes de controle biológico de pragas
Inteligência computacional
Produtividade agrícola
Idioma: eng
País: Brasil
Editor: Universidade Federal de Minas Gerais
Sigla da Instituição: UFMG
Departamento: ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
Tipo de Acesso: Acesso Aberto
Identificador DOI: https://doi.org/10.1016/j.atech.2022.100056
URI: http://hdl.handle.net/1843/60550
Data do documento: 2022
metadata.dc.url.externa: https://www.sciencedirect.com/science/article/pii/S2772375522000211
Aparece nas coleções:Artigo de Periódico

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