Integrating processed-based models and machine learning for crop yield prediction

TL;DR

This study explores a hybrid approach combining crop growth models and machine learning to improve potato yield prediction, showing promising results but highlighting the need for further validation with real-world data.

Contribution

It introduces a novel meta-modeling approach that uses crop growth models to generate synthetic data for training neural networks in crop yield prediction.

Findings

Meta-modeling outperforms purely data-driven models in in silico tests.

On real-world data, meta-modeling is competitive with crop growth models.

Simple linear regression with domain knowledge can outperform complex models in some cases.

Abstract

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.