SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes
Graph neural networks (GNNs) offer a flexible framework for learning from scientific data with physical, biological, or functional associations. One promising domain is plant physiology, where observed responses result from several interacting processes that are difficult to isolate, even with human intervention. A key example is the A-Ci curve, which relates the net CO2 assimilation rate (Anet) to leaf intercellular CO2 concentration (Ci) and is also used to estimate photosynthetic parameters in biophysical models. However, accurate estimation requires accurate identification of the active biochemical limiting state at each curve point, which is a major source of uncertainty. Here, we ex...
| Authors | Antriksh Srivastava, Soumyashree Kar |
|---|---|
| Primary category | cs.LG |
| Published | 17 June 2026 |
| Last updated at source | 23 June 2026 |
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