Hybrid neural parameterization of biophysical models plus multi-task learning improves phenology prediction accuracy by 60% and cold hardiness by 40% over deployed biophysical models.
Analyzing the impact of forecast errors in the planning of wine grape harvesting operations using a multi-stage stochastic model approach.arXiv:2405.19997, May
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A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning
Hybrid neural parameterization of biophysical models plus multi-task learning improves phenology prediction accuracy by 60% and cold hardiness by 40% over deployed biophysical models.