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dc.contributor.authorBARBEDO, J. G. A.
dc.date.accessioned2024-01-24T14:33:49Z-
dc.date.available2024-01-24T14:33:49Z-
dc.date.created2024-01-24
dc.date.issued2023
dc.identifier.citationSeeds, v. 2, n. 3, p. 340–356, Sept. 2023.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1161254-
dc.descriptionThis review characterizes the current state of the art of deep learning applied to soybean crops, detailing the main advancements achieved so far and, more importantly, providing an in-depth analysis of the main challenges and research gaps that still remain.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectAprendizado profundo
dc.subjectImagem digital
dc.subjectInteligência artificial
dc.subjectCulturas de soja
dc.subjectDeep learning
dc.titleDeep learning for soybean monitoring and management.
dc.typeArtigo de periódico
dc.subject.thesagroGlycine Max
dc.subject.nalthesaurusDigital images
dc.subject.nalthesaurusCrops
dc.subject.nalthesaurusArtificial intelligence
riaa.ainfo.id1161254
riaa.ainfo.lastupdate2024-01-24
dc.identifier.doihttps://doi.org/10.3390/ seeds2030026
dc.contributor.institutionJAYME GARCIA ARNAL BARBEDO, CNPTIA.
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