Multi-Modal Spatio-Temporal Graph Neural Network with Mixture of Experts for Soil Organic Carbon Prediction
arXiv:2606.16580v1 Announce Type: new Abstract: Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning. Existing approaches face two limitations: they pair hand-crafted covariates with classical ML or single-modal deep models that miss rich spectral and temporal information, and grid-based architectures ignore the irregular spatial structure of field measurements. We introduce SpTGNN, a multi-modal spatio-temporal graph neural network addressing both. SpTGNN represents soil measurements as nodes in a heterogeneous grap
The increasing sophistication of multi-modal AI models and graph neural networks enables addressing complex environmental prediction challenges like soil organic carbon estimation with higher accuracy.
Accurate soil organic carbon prediction is crucial for global agricultural sustainability, informing land use policies, optimizing fertilization, and potentially contributing to carbon sequestration efforts.
This advancement enables more precise and data-rich understanding of soil health, moving beyond traditional methods that overlook critical spectral, temporal, and irregular spatial data.
- · Agriculture sector
- · Environmental monitoring companies
- · Precision farming technology providers
- · AI/ML researchers in remote sensing
- · Traditional soil analysis firms
- · Farmers relying solely on conventional methods
Improved agricultural yield and reduced fertilizer use through better soil management.
Enhanced capabilities for carbon market verification and land-use compliance reporting.
Potential for new financial instruments tied to soil health and carbon sequestration performance at scale.
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Read at arXiv cs.LG