Análise de imagens obtidas com drone

Abstract

Precision agriculture emerges as a crucial alternative to overcome the challenges of traditional agriculture, such as low productivity, environmental degradation, and increasing food demand. This innovative approach utilizes advanced technologies, such as drones, to collect and analyze real-time data, enabling producers to make more precise and efficient decisions. In this study, we evaluated the feasibility of using drones to monitor soybean and corn intercropping direct planting areas. RGB images collected by drones were processed to generate vegetation indices (GLI, NGDRI, and RI) and classify areas according to the presence of machine-caused rutting and planting failures. The combination of GLI and RI indices provided a more consistent and accurate analysis compared to the GLI and NGDRI combination. Through this analysis, it was possible to clearly identify planting failures and rutting trails, allowing producers to take corrective measures to optimize crop management and increase productivity.