Abstract
Coffee production is an activity of great economic and social importance in Brazil. This agribusiness segment stands out in the economy of small cities in southern Minas Gerais, as it involves family farming and the permanence of the rural population in the countryside. This study aimed to contribute and adapt geotechnology-based methods for the remote mapping of coffee and the strengthening of the permanence of this population, providing an online platform to simulate coffee trading. The municipality of Inconfidentes/MG was the study area. Orbital images of the Sentinel-2A satellite were used in the development of the study. Images were classified in a supervised way with the random forest classifier in Google Earth Engine (GEE) and later used as a data source in the online platform developed in the Application Programming Interface (API) Leaflet to simulate coffee trading involving the cryptocurrency Coffee Coin. Results allowed the identification and mapping of coffee growing areas by remote sensing and, also, to demonstrate that the online platform can help in the planning of new investments in coffee production, in addition to presenting an overview of the economic importance of coffee to the municipality.
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