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

Research output: Contribution to conferenceConference paperAcademicpeer-review


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.
Original languageEnglish
Number of pages6
Publication statusPublished - 28 Jul 2023
EventICML 2023 Workshop: Synergy of Scientific and Machine Learning Modeling - Honolulu, Hawaii, USA, Honolulu, United States
Duration: 28 Jul 202328 Jul 2023


WorkshopICML 2023 Workshop: Synergy of Scientific and Machine Learning Modeling
Abbreviated titleSynS & ML ICML
Country/TerritoryUnited States
Internet address


  • crop growth modeling
  • hybrid modeling
  • metamodel
  • machine learning
  • transfer learning
  • potato
  • agriculture


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