PastureNet - Cross-Domain Biomass Estimation

Accurate pasture biomass estimation is critical for precision grazing management yet remains challenged by the trade-off between the scalability of remote sensing and the reliability of manual sampling. To address this, we introduce PastureNet, a novel hierarchical ensemble framework that estimates biomass directly from high-resolution RGB images. Unlike traditional approaches, PastureNet synergizes diverse inductive biases by integrating three state-of-the-art Vision Transformers: DINOv3 (object-centric), SigLIP 2 (semantic-aligned), and EVA-02 (texture-sensitive). A key innovation is the integration of Zero-shot Semantic Concept Scores to inject explicit ecological domain knowledge (e.g., clover presence) into the regression pipeline, alongside a Matrix Reconciliation post-processing step that ensures biological consistency across biomass components. Evaluated on a heterogeneous Australian dataset, our method achieves a Weighted R2 of 0.70, significantly outperforming CNN baselines (0.47) and demonstrating robust generalization without requiring physical metadata at inference time.