Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910297928761344 |
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| author | Sharma, Somya Sharma, Swati Padilha, Rafael Kiciman, Emre Chandra, Ranveer |
| author_facet | Sharma, Somya Sharma, Swati Padilha, Rafael Kiciman, Emre Chandra, Ranveer |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07175 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization Sharma, Somya Sharma, Swati Padilha, Rafael Kiciman, Emre Chandra, Ranveer Machine Learning 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. |
| title | Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.07175 |