Title:	Mapping coffee management zones using satellite-derived indices and clustering-based methods: a statistical differentiation approach.
Authors:	SPERANZA, E. A. FERREIRA, E. J. BASSOI, L. H.
Affiliation:	EDUARDO ANTONIO SPERANZA, CNPTIA; EDNALDO JOSE FERREIRA, CNPDIA; LUIS HENRIQUE BASSOI, CNPDIA.
Date Issued:	2025
Citation:	In: SIMPÓSIO NACIONAL DE INSTRUMENTAÇÃO AGROPECUÁRIA, 5., 2025, São Carlos. Anais [...]. São Carlos: Embrapa Instrumentação, 2025. p. 639-643.
Description:	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
Keywords:	Índices de vegetação Análise de agrupamento Satellite Vegetation indices Clustering analysis
ISSN:	2358-9132
Notes:	Editores: Paulo Sergio de Paula Herrmann Junior, Henriette Monteiro Cordeiro de Azeredo, Maria Fernanda Berlingieri Durigan, Luís Henrique Bassoi.
Type of Material:	Artigo em anais e proceedings
Access:	openAccess
Appears in Collections:	Artigo em anais de congresso (CNPTIA)