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

• URL: http://arxiv.org/abs/2307.13466v1
• Date: Tue, 25 Jul 2023 12:51:25 GMT
• Title: Integrating processed-based models and machine learning for crop yield prediction
• Authors: Michiel G.J. Kallenberg, Bernardo Maestrini, Ron van Bree, Paul Ravensbergen, Christos Pylianidis, Frits van Evert, and Ioannis N. Athanasiadis (Wageningen University and Research, the Netherlands)
• Abstract summary: 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. When applied in silico, our meta-modeling approach yields better predictions than a baseline comprising a purely data-driven approach.
• Score: 1.3107669223114085
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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