Título:	Mapping coffee management zones using satellite-derived indices and clustering-based methods: a statistical differentiation approach.
Autor:	SPERANZA, E. A. FERREIRA, E. J. BASSOI, L. H.
Afiliación:	EDUARDO ANTONIO SPERANZA, CNPTIA; EDNALDO JOSE FERREIRA, CNPDIA; LUIS HENRIQUE BASSOI, CNPDIA.
Año:	2025
Referencia:	In: SIMPÓSIO NACIONAL DE INSTRUMENTAÇÃO AGROPECUÁRIA, 5., 2025, São Carlos. Anais [...]. São Carlos: Embrapa Instrumentação, 2025. p. 639-643.
Descripción:	Abstract: The study of spatio-temporal variability in soil and crop parameters is a crucial first step for the adoption of precision agriculture (PA). Recent research has utilized yield data, soil attributes, multispectral images, and machine learning algorithms to monitor coffee production. This work proposes a method for delineating management zones using spectral bands and vegetation indices derived from satellite imagery, clustering algorithms, and statistical analysis to identify high-quality coffee areas. It offers a data-driven approach to enhance PA practices in coffee plantations.
Thesagro:	Coffea Arábica Cafeicultura Sensoriamento Remoto Satélite
NAL Thesaurus:	Remote sensing Vegetation index Cluster analysis
Palabras clave:	Índices de vegetação Análise de agrupamento Satellite Vegetation indices Clustering analysis
ISSN:	2358-9132
Notas:	Editores: Paulo Sergio de Paula Herrmann Junior, Henriette Monteiro Cordeiro de Azeredo, Maria Fernanda Berlingieri Durigan, Luís Henrique Bassoi.
Tipo de Material:	Artigo em anais e proceedings
Acceso:	openAccess
Aparece en las colecciones:	Artigo em anais de congresso (CNPTIA)