Monitoring arabica coffee varieties with orbital remote sensing for age and yield modeling with machine learning
of coffee crop management
Arabica coffee under tropical conditions has different phenological phases. Being those factors such as temperature, photoperiod, irradiance, relief, soil, water supply, nutrients influence the physiological behavior of this crop. In this sense, the monitoring by satellite images of the coffee cycle and the identification of productive areas under production, biennially, morphological and radiometric indexes are essential for the development of the crop, as well as the quantification of nutritional characteristics are essential for the formation of productivity. The prediction of data collected by remote sensors by machine learning techniques can be used to identify areas and to predict plant productivity and, therefore, the planning of coffee crop management.