# Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization

arXiv: https://arxiv.org/abs/2401.07175 · HTML: https://mlsanity.com/paper/2401.07175/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 13 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery](https://mlsanity.com/paper/2010.08168/) (2020)
- [Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization](https://mlsanity.com/paper/2206.07837/) (2022)
- [Seeing Through Clouds in Satellite Images](https://mlsanity.com/paper/2106.08408/) (2021)
- [Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran](https://mlsanity.com/paper/2007.12475/) (2020)
- [Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources](https://mlsanity.com/paper/2308.09766/) (2023)

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

- [Enabling Adoption of Regenerative Agriculture through Soil Carbon Copilots](https://mlsanity.com/paper/2411.16872/) (2024)

## Abstract (arXiv)

Monitoring organic matter is pivotal for maintaining soil health and can help inform sustainable soil management practices. While sensor-based soil information offers higher-fidelity and reliable insights into organic matter changes, sampling and measuring sensor data is cost-prohibitive. We propose a multi-modal, scalable framework that can estimate organic matter from remote sensing data, a more readily available data source while leveraging sparse soil information for improving generalization. Using the sensor data, we preserve underlying causal relations among sensor attributes and organic matter. Simultaneously we leverage inherent structure in the data and train the model to discriminate among domains using contrastive learning. This causal and contrastive constraint minimization ensures improved generalization and adaptation to other domains. We also shed light on the interpretability of the framework by identifying attributes that are important for improving generalization. Identifying these key soil attributes that affect organic matter will aid in efforts to standardize data collection efforts.