Hybrid framework for sugarcane yield forecasting using machine learning, remote sensing, and process-based crop modeling1

Abstract

ABSTRACT Accurate sugarcane yield forecasting is essential for food security, sustainable agriculture, and resource management. However, conventional methods often struggle to capture the complex interactions that influence sugarcane growth, leading to unreliable predictions. This study explores whether a hybrid approach, combining remote sensing data, process-based crop modeling (APSIM), and machine learning, can enhance yield predictions. 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. Sixteen regression models were evaluated at the municipal level. Incorporating APSIM-simulated variables as features in machine-learning models reduced the root mean square error (RMSE) of predictions between 7.7 and 26.9%. The exclusion of certain features elucidated that vegetation indices had the least impact on yield predictions. Weather data alone exerted a more significant impact on forecasts when used in process-based models compared to being directly input into machine-learning algorithms. Thus, our hybrid approach outperformed traditional methods, offering more accurate predictions. These findings could significantly enhance agricultural practices, empower farmers with improved predictive tools, and contribute to global food security and sustainable agriculture.