Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1119503
Research center of Embrapa/Collection: Embrapa Trigo - Artigo em periódico indexado (ALICE)
Date Issued: 2020
Type of Material: Artigo em periódico indexado (ALICE)
Authors: LINS, E. A.
RODRIGUEZ, J. P. M.
SCOLOSKI, S. I.
PIVATO, J.
LIMA, M. B.
FERNANDES, J. M. C.
PEREIRA, P. R. V. da S.
LAU, D.
RIEDER, R.
Additional Information: Elison Alfeu Lins, University of Passo Fundo (UPF), Passo Fundo, Rio Grande do Sul, Brazil1; João Pedro Mazuco Rodriguez, University of Passo Fundo (UPF), Passo Fundo, Rio Grande do Sul, Brazil; Sandy Ismael Scoloski, University of Passo Fundo (UPF), Passo Fundo, Rio Grande do Sul, Brazil1; Juliana Pivato, The Brazilian Agricultural Research Corporation (Embrapa Wheat), Passo Fundo, Rio Grande do Sul, Brazil2; Marília Balotin Lima, The Brazilian Agricultural Research Corporation (Embrapa Wheat), Passo Fundo, Rio Grande do Sul, Brazil2; JOSE MAURICIO CUNHA FERNANDES, CNPT; PAULO ROBERTO VALLE DA S PEREIRA, CNPF; DOUGLAS LAU, CNPT; Rafael Rieder, University of Passo Fundo (UPF), Passo Fundo, Rio Grande do Sul, Brazil1.
Title: A method for counting and classifying aphids using computer vision.
Publisher: Computers and Eletronics in Agriculture, n. 169, 2020.
Language: en
Keywords: Aphids
Counting
Description: Aphids are insects that attack crops and cause damage directly, by consuming the sap of plants, and indirectly,by vectoring microorganisms that can cause diseases. Cereal crops are hosts for many aphid species, includingRhopalosiphumpadi(an economically important aphid species). Recording and classifying aphids are necessaryfor evaluating and predicting crop damage. Thus, serving as a basis for decision making on the utilization ofcontrolmeasures.Itcanalsobeusefultoevaluateplantresistancetoaphids.Traditionally,therecordingprocessis manual and depends on magnification and well-trained staff. The manual counting is also a time-consumingprocess and susceptible to errors. With this in mind, this paper presents a method and software to automate thecounting and classification ofRhopalosiphum padiusing image processing, computer vision, and machinelearningmethods.Thetextalsopresentsacomparisonofmanuallycountsfromexpertsandvaluesobtainedwiththe software, considering 40 samples. The results showed strong positive correlation in counting and classifi-cation(rs=0.92579)and measurement (r=0.9799).Concluding, thesoftware provedtobe reliableandusefulto aphid population monitoring studies.
NAL Thesaurus: Classification
Computer vision
Measurement
Data Created: 2020-01-28
Appears in Collections:Artigo em periódico indexado (CNPT)

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