Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction
This repository is the implementation of the paper "Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction". The method is a physics-guided approach for crop yield loss forecasting at the pixel level using temporal estimations of the water use.
The arxiv version of the paper can be found here
The original yield data is collected from:
- Data source: https://www.research-collection.ethz.ch/handle/20.500.11850/581023
- Reference: Perich, Gregor, et al. Pixel-based yield mapping and prediction from Sentinel-2 using spectral indices and neural networks. Field Crops Research 292 (2023): 108824.
Further, weather data was acquired for every field from the ERA5 archive. Simulation data was created using the PyFAO56. Further, the data preprocessed and stored in an xarray file.
The data is stored in a xaaray dataset that can be downloaded here
- For training run
python train.py -s path/to/config/file.yaml
@inproceedings{miranda2025informed,
author = {Miranda, M. and Charfuelan, M. and Valdenegro-Toro, M. and Dengel, A.},
title = {Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction},
booktitle = {Proceedings of the European Conference on Artificial Intelligence (ECAI 2025)},
year = {2025},
note = {Accepted for publication}
}
This program is licenced under Attribution-NonCommercial 4.0 International agreement.
You are free to share — copy and redistribute the material in any medium or format. You may adapt — remix, transform, and build upon the material.
NonCommercial — You may not use the material for commercial purposes.
Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.