# Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction

arXiv: https://arxiv.org/abs/2510.18648 · HTML: https://mlsanity.com/paper/2510.18648/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Bayesian Deep Learning and a Probabilistic Perspective of Generalization](https://mlsanity.com/paper/2002.08791/) (2020)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data](https://mlsanity.com/paper/1612.08544/) (2016)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Incorporating Domain Knowledge into Deep Neural Networks](https://mlsanity.com/paper/2103.00180/) (2021)
- [Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt](https://mlsanity.com/paper/2008.04060/) (2020)
- [Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction](https://mlsanity.com/paper/2401.11844/) (2024)
- [Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level](https://mlsanity.com/paper/2308.08948/) (2023)

## Built on by (most cited first, 1 of 1)

- [YieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction](https://mlsanity.com/paper/2604.00940/) (2026)

## Abstract (arXiv)

Water is essential for agricultural productivity. Assessing water shortages and reduced yield potential is a critical factor in decision-making for ensuring agricultural productivity and food security. Crop simulation models, which align with physical processes, offer intrinsic explainability but often perform poorly. Conversely, machine learning models for crop yield modeling are powerful and scalable, yet they commonly operate as black boxes and lack adherence to the physical principles of crop growth. This study bridges this gap by coupling the advantages of both worlds. We postulate that the crop yield is inherently defined by the water availability. Therefore, we formulate crop yield as a function of temporal water scarcity and predict both the crop drought stress and the sensitivity to water scarcity at fine-scale resolution. Sequentially modeling the crop yield response to water enables accurate yield prediction. To enforce physical consistency, a novel physics-informed loss function is proposed. We leverage multispectral satellite imagery, meteorological data, and fine-scale yield data. Further, to account for the uncertainty within the model, we build upon a deep ensemble approach. Our method surpasses state-of-the-art models like LSTM and Transformers in crop yield prediction with a coefficient of determination (R2-score) of up to 0.82 while offering high explainability. This method offers decision support for industry, policymakers, and farmers in building a more resilient agriculture in times of changing climate conditions.