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I. Z. Gonçalves

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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. · 0 citations

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