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dc.contributor.authorPARREIRAS, T. C.
dc.contributor.authorBOLFE, E. L.
dc.contributor.authorFURUYA, D. E. G.
dc.date.accessioned2026-04-22T19:49:56Z-
dc.date.available2026-04-22T19:49:56Z-
dc.date.created2026-04-22
dc.date.issued2026
dc.identifier.citationIn: CONVERGENCE OF RESEARCH IN DIGITAL AGRICULTURE LEADING LABS (CORDIALL) CONFERENCE, 2026, Montpellier. Book of abstracts. Paris: INRAE, 2026. p. 105.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1186342-
dc.descriptionDespite the advances, accurately identifying recently renovated and skeletonized coffee areas remains a challenge, as their altered canopy structure and reduced vigor produce spectral signatures similar to those of fallow or non-coffee areas. To address these limitations, upcoming research will focus on leveraging a space-time hybrid approach with deep learning and surface phenology modeling. Specifically, we plan to implement a workflow combining the spatial detail of Sentinel-2 with the temporal continuity of HLS.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectAgricultura digital
dc.subjectAprendizado profundo
dc.subjectDados multisensor
dc.subjectDigital agriculture
dc.subjectDeep learning
dc.titleAdvancing coffee management mapping through multisensor data and multistep ensemble learning.
dc.typeResumo em anais e proceedings
dc.subject.thesagroCafé
dc.subject.thesagroSensoriamento Remoto
dc.subject.nalthesaurusRemote sensing
riaa.ainfo.id1186342
riaa.ainfo.lastupdate2026-04-22
dc.contributor.institutionTAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; EDSON LUIS BOLFE, CNPTIA; DANIELLE ELIS GARCIA FURUYA, UNIVERSIDADE ESTADUAL DE CAMPINAS.
Aparece en las colecciones:Resumo em anais de congresso (CNPTIA)

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