Title:	Advancing coffee management mapping through multisensor data and multistep ensemble learning.
Authors:	PARREIRAS, T. C. BOLFE, E. L. FURUYA, D. E. G.
Affiliation:	TAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; EDSON LUIS BOLFE, CNPTIA; DANIELLE ELIS GARCIA FURUYA, UNIVERSIDADE ESTADUAL DE CAMPINAS.
Date Issued:	2026
Citation:	In: CONVERGENCE OF RESEARCH IN DIGITAL AGRICULTURE LEADING LABS (CORDIALL) CONFERENCE, 2026, Montpellier. Book of abstracts. Paris: INRAE, 2026. p. 105.
Description:	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
Keywords:	Agricultura digital Aprendizado profundo Dados multisensor Digital agriculture Deep learning
Type of Material:	Resumo em anais e proceedings
Access:	openAccess
Appears in Collections:	Resumo em anais de congresso (CNPTIA)