Combining expert knowledge and neural networks to model environmental stresses in agriculture
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.
- Published in:
BMC Bioinformatics - Type:
Article - Authors:
K. Cvejoski, J. Schuecker, A.-K. Mahlein, C. Ojeda, B. Georgiev - Year:
2020
Citation information
K. Cvejoski, J. Schuecker, A.-K. Mahlein, C. Ojeda, B. Georgiev: Combining expert knowledge and neural networks to model environmental stresses in agriculture, BMC Bioinformatics, 2020, https://doi.org/10.48550/arXiv.2111.00918, Cvejoski.etal.2020,
@Article{Cvejoski.etal.2020,
author={K. Cvejoski, J. Schuecker, A.-K. Mahlein, C. Ojeda, B. Georgiev},
title={Combining expert knowledge and neural networks to model environmental stresses in agriculture},
journal={BMC Bioinformatics},
url={https://doi.org/10.48550/arXiv.2111.00918},
year={2020},
abstract={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...}}