Pre-Harvest Fresh Biomass Estimation of Lavandula × intermedia from UAV-RGB Images: A Controlled Evaluation of FiLM-Based Visual-Numerical Fusion

Estimating fresh biomass prior to harvest can facilitate the planning of lavender harvesting. This study assessed the integration of UAV-RGB visual and numerical data using 459 original images, representing 287 plant-year units from three distinct field-year campaigns. A fixed grouped partition facilitated 135 runs across five input/fusion groups, nine backbones, and three training seeds. The Model17-FiLM demonstrated a seed-level mean R2 of 0.934 ± 0.010 and an RMSE of 178.2 ± 13.8 g, in contrast to the RGB-only Base model, which showed 0.910 ± 0.021 and 208.4 ± 25.6 g. When predictions were averaged across all nine backbones and three seeds, the RMSE decreased from 196.78 g for the Base model to 166.27 g for Model16 and 163.32 g for Model17-FiLM. Pointwise bootstrap intervals indicated the benefit of incorporating the descriptor branch, although the contribution to harvest timing remained uncertain. FiLM produced lower point-estimate errors compared to concatenation, without consistent multiplicity-adjusted support. The numerical-only RF17 model resulted in an RMSE of 423.52 g. Model21 did not enhance predictive accuracy and included redundant nominal scale inputs. The restriction to persistent plants maintained the principal ranking, while audits of acquisition sessions and markers revealed contextual sensitivity. Expanded Grad-CAM analysis demonstrated a strong dependence on the backbone. These findings endorse the evaluated approach within this dataset, though caution is advised against general deployment without independent validation across different field-years.