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dc.contributor.authorMEIR, Y.
dc.contributor.authorBARBEDO, J. G. A.
dc.contributor.authorKEREN, O.
dc.contributor.authorGODOY, C. V.
dc.contributor.authorAMEDI, N.
dc.contributor.authorSHALOM, Y.
dc.contributor.authorGEVA, A. B.
dc.date.accessioned2023-06-26T16:36:19Z-
dc.date.available2023-06-26T16:36:19Z-
dc.date.created2023-04-27
dc.date.issued2023
dc.identifier.citationSensors, v. 23, n. 9, 4272, 2023.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1153402-
dc.descriptionThis study investigates how the use of electroencephalograms from plant pathology experts can improve the accuracy and robustness of image-based artificial intelligence models dedicated to plant disease recognition.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectPatologia de planta
dc.subjectOndas cerebrais
dc.subjectEletroencefalograma
dc.subjectImagem digital
dc.subjectAprendizado ativo
dc.subjectInteligência artificial
dc.subjectElectroencephalogram
dc.subjectLabeling
dc.subjectActive learning
dc.titleUsing brainwave patterns recorded from plant pathology experts to increase the reliability of ai-based plant disease recognition system.
dc.typeArtigo de periódico
dc.subject.thesagroSoja
dc.subject.nalthesaurusSoybeans
dc.subject.nalthesaurusDigital images
dc.subject.nalthesaurusPlant pathology
dc.subject.nalthesaurusPlant diseases and disorders
dc.subject.nalthesaurusArtificial intelligence
dc.format.extent213 p.
riaa.ainfo.id1153402
riaa.ainfo.lastupdate2023-06-26
dc.identifier.doi10.3390/s23094272
dc.contributor.institutionYONATAN MEIR, INNEREYE LTD.; JAYME GARCIA ARNAL BARBEDO, CNPTIA; OMRI KEREN, INNEREYE LTD.; CLAUDIA VIEIRA GODOY, CNPSO; NOFAR AMEDI, INNEREYE LTD.; YAAR SHALOM, INNEREYE LTD.; AMIR B. GEVA, INNEREYE LTD., BEN GURION UNIVERSITY.
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