Integrating processed-based models and machine learning for crop yield prediction
This work addresses crop yield prediction for agricultural stakeholders, but it is incremental as it shows limited practical gains over existing methods.
The study tackled potato yield prediction by integrating a process-based crop growth model with machine learning, using synthetic data for pretraining and observational data for fine-tuning, resulting in competitive performance against the crop growth model on real-world data but underperforming compared to a simple linear regression with expert-designed features.
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.