A Comprehensive Modeling Approach for Crop Yield Forecasts using
  AI-based Methods and Crop Simulation Models
Renato Luiz de Freitas Cunha Bruno Silva Priscilla Barreira Avegliano
Numerous solutions for yield estimation are either based on data-driven
models, or on crop-simulation models (CSMs). Researchers tend to build
data-driven models using nationwide crop information databases provided by
agencies such as the USDA. On the opposite side of the spectrum, CSMs require
fine data that may be hard to generalize from a handful of fields. In this
paper, we propose a comprehensive approach for yield forecasting that combines
data-driven solutions, crop simulation models, and model surrogates to support
multiple user-profiles and needs when dealing with crop management
decision-making. To achieve this goal, we have developed a solution to
calibrate CSMs at scale, a surrogate model of a CSM assuring faster execution,
and a neural network-based approach that performs efficient risk assessment in
such settings. Our data-driven modeling approach outperforms previous works
with yield correlation predictions close to 91\%. The crop simulation modeling
architecture achieved 6% error; the proposed crop simulation model surrogate
performs predictions almost 100 times faster than the adopted crop simulator
with similar accuracy levels.