Integrating processed-based models and machine learning for crop yield
  prediction
Michiel G. J. Kallenberg Bernardo Maestrini Ron van Bree Paul Ravensbergen Christos Pylianidis Frits van Evert Ioannis N. Athanasiadis
Crop yield prediction typically involves the utilization of either
theory-driven process-based crop growth models, which have proven to be
difficult to calibrate for local conditions, or data-driven machine learning
methods, which are known to require large datasets. In this work we investigate
potato yield prediction using a hybrid meta-modeling approach. A crop growth
model is employed to generate synthetic data for (pre)training a convolutional
neural net, which is then fine-tuned with observational data. When applied in
silico, our meta-modeling approach yields better predictions than a baseline
comprising a purely data-driven approach. When tested on real-world data from
field trials (n=303) and commercial fields (n=77), the meta-modeling approach
yields competitive results with respect to the crop growth model. In the latter
set, however, both models perform worse than a simple linear regression with a
hand-picked feature set and dedicated preprocessing designed by domain experts.
Our findings indicate the potential of meta-modeling for accurate crop yield
prediction; however, further advancements and validation using extensive
real-world datasets is recommended to solidify its practical effectiveness.