Open access
2026
Hybrid framework for sugarcane yield forecasting using machine learning, remote sensing, and process-based crop modeling1
The developed model combines APSIM-simulated variables, weather data, and vegetation indices to forecast end-season sugarcane yield in São Paulo, Brazil, for the period 2010-2020, offering more accurate predictions.
Daniel A. da V. Grubert, B. Orsi, I. Z. Gonçalves et al.
· Revista Brasileira de Engenh... · 0 citations