In this work we combine representation learning capabilities of neural network with agricultural knowledge from experts to model environmental heat and drought stresses. We first design deterministic expert models which serve as a benchmark and inform the design of flexible neural-network architectures. Finally, a sensitivity analysis of the latter allows a clustering of hybrids into susceptible and resistant ones.
@article{arxiv.2111.00918,
title = {Combining expert knowledge and neural networks to model environmental stresses in agriculture},
author = {Kostadin Cvejoski and Jannis Schuecker and Anne-Katrin Mahlein and Bogdan Georgiev},
journal= {arXiv preprint arXiv:2111.00918},
year = {2021}
}
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19 pages, Winners of the 2019 Syngenta Crop Challenge