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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.
|Number of pages||6|
|Publication status||Published - 28 Jul 2023|
|Event||ICML 2023 Workshop: Synergy of Scientific and Machine Learning Modeling - Honolulu, Hawaii, USA, Honolulu, United States|
Duration: 28 Jul 2023 → 28 Jul 2023
|Workshop||ICML 2023 Workshop: Synergy of Scientific and Machine Learning Modeling|
|Abbreviated title||SynS & ML ICML|
|Period||28/07/23 → 28/07/23|
- crop growth modeling
- hybrid modeling
- machine learning
- transfer learning
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