Título:	Advancing coffee management mapping through multisensor data and multistep ensemble learning.
Autoria:	PARREIRAS, T. C. BOLFE, E. L. FURUYA, D. E. G.
Afiliação:	TAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; EDSON LUIS BOLFE, CNPTIA; DANIELLE ELIS GARCIA FURUYA, UNIVERSIDADE ESTADUAL DE CAMPINAS.
Ano de publicação:	2026
Referência:	In: CONVERGENCE OF RESEARCH IN DIGITAL AGRICULTURE LEADING LABS (CORDIALL) CONFERENCE, 2026, Montpellier. Book of abstracts. Paris: INRAE, 2026. p. 105.
Conteúdo:	Despite 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.
Thesagro:	Café Sensoriamento Remoto
NAL Thesaurus:	Remote sensing
Palavras-chave:	Agricultura digital Aprendizado profundo Dados multisensor Digital agriculture Deep learning
Tipo do material:	Resumo em anais e proceedings
Acesso:	openAccess
Aparece nas coleções:	Resumo em anais de congresso (CNPTIA)