Advancing coffee management mapping through multisensor data and multistep ensemble learning.

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dc.contributorTAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; EDSON LUIS BOLFE, CNPTIA; DANIELLE ELIS GARCIA FURUYA, UNIVERSIDADE ESTADUAL DE CAMPINAS.
dc.creatorPARREIRAS, T. C.
dc.creatorBOLFE, E. L.
dc.creatorFURUYA, D. E. G.
dc.date2026-04-22T19:49:56Z
dc.date2026-04-22T19:49:56Z
dc.date2026-04-22
dc.date2026
dc.date.accessioned2026-07-07T04:18:45Z
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.identifierIn: CONVERGENCE OF RESEARCH IN DIGITAL AGRICULTURE LEADING LABS (CORDIALL) CONFERENCE, 2026, Montpellier. Book of abstracts. Paris: INRAE, 2026. p. 105.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1186342
dc.identifier.urihttp://hdl.handle.net/123456789/457458
dc.languageeng
dc.rightsopenAccess
dc.subjectAgricultura digital
dc.subjectAprendizado profundo
dc.subjectDados multisensor
dc.subjectDigital agriculture
dc.subjectDeep learning
dc.subjectCafé
dc.subjectSensoriamento Remoto
dc.subjectRemote sensing
dc.titleAdvancing coffee management mapping through multisensor data and multistep ensemble learning.
dc.typeResumo em anais e proceedings

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