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.
We choose 0.01 as the largest fac- tor as it roughly represents the largest observed daily change in the base DMC-MTL models
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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.