Assessment of sugarcane production using regression models and RGB vegetation indices data.
No hay miniatura disponible
Fecha
Título de la revista
ISSN de la revista
Título del volumen
Editor
Resumen
Descripción
Productivity estimates play a crucial role for sugarcane producers and sugar mills in planning production, aligning it with demand forecasts. Manual estimations demand considerable effort and time, prompting exploration into alternative productivity estimation methods such as aerial imaging using drones. Within imaging techniques, productivity estimation occurs indirectly through the analysis of vegetation indices. The widely recognized vegetation index, NDVI, necessitates costly near-infrared (NIR) cameras, making it inaccessible to many producers. Our approach utilized drone imagery captured by more affordable RGB cameras, which are feasible for a larger number of producers. We applied six regression models alongside a stacking model that amalgamated these six models for estimating sugarcane production using the eight RGB vegetation indices. Initial tests revealed a Mean Absolute Percentage Error (MAPE) of less than 13%. This level of accuracy is considered favorable when benchmarked against similar studies and presents encouraging prospects for future research.
CONTECSI USP 2024. Evento virtual.
CONTECSI USP 2024. Evento virtual.
Palabras clave
Drone, Aprendizado de máquina, Machine learning, Cana de Açúcar, Rendimento, Unmanned aerial vehicles, Yields, Sugarcane