Dynamic LoRA Fine-Tuning of DINOv3 for Multi-Component Pasture Biomass Estimation

Accurate estimation of pasture biomass components from imagery is essential for sustainable grazing management and precision agriculture. Conventional methods such as destructive harvesting, rising plate meters, and remote sensing are limited by scalability, reliability, or the ability to disaggregate biomass by species. We propose a parameter-efficient multi-output regression framework predicting five biomass components (dry green, dry dead, dry clover, green dry matter, and total dry biomass) from high-resolution top-view pasture images. It employs a pretrained DINOv3 Vision Transformer backbone adapted via a dynamic, depth-aware Low-Rank Adaptation (LoRA) strategy, in which the adaptation rank and scaling factor increase exponentially with layer depth: early layers encoding generic visual primitives are minimally perturbed, while deeper layers receive stronger task-specific adaptation. This schedule is effective in low-data regimes, where uniform adaptation or full fine-tuning overfits. To handle rectangular image geometry, each image is split into two square halves processed as a dual-view stream with a contrastive alignment loss. The system ensembles ViT-Large and ViT-Huge backbones with test-time augmentation across five-fold cross-validation. On the CSIRO Image2Biomass benchmark, the full pipeline attains a cross-validated weighted R-squared of 0.81, indicating that depth-aware, parameter-efficient adaptation of large vision models is effective for non-invasive biomass estimation under data scarcity.