Resumen:
Coffee production is a crucial economic, social, and cultural pillar in Latin America, facing numerous challenges, including integrating technological advancements such as multispectral imaging. This approach offers multiple advantages for coffee production;
however, a knowledge gap in the domain is the need to methodologically review the available empirical evidence to delineate the field and the study region. Therefore, this systematic mapping aims to map the scientific corpus of multispectral imagery and vegetation index implemented in coffee production in the Latin American region. The study followed the PRISMA protocol; 42 primary studies were analyzed to identify key trends and research gaps. The main result of this research is that NDVI emerged as the most widely used spectral index, with applications in estimating critical biophysical parameters such as biomass and chlorophyll content. Other indices such as GNDVI, NDRE, and SAVI also proved valuable in assessing coffee plant health and development. There
was an emerging trend to integrate multispectral imaging with machine learning techniques, promising greater accuracy in data interpretation. The study also revealed a concentration of research efforts in selected Latin American countries, particularly Brazil,
indicating opportunities to expand research in other coffee-producing regions. The study's main conclusion is that multispectral imaging, mainly through vegetation index, has emerged as a valuable tool for phenological monitoring and management of coffee production, offering several advantages over traditional methods. Finally, this review contributes to the existing knowledge base and identifies future research directions for applying multispectral imagery to sustainable coffee production in Latin America
