Time-Aware Late Fusion for Multimodal Rice Growth-Rate Prediction from UAV Imagery and Weather Context

Abstract

Accurate crop-growth estimation from unmanned aerial vehicle (UAV) imagery is important for precision agriculture, but image-only models can struggle to represent seasonal context. This study evaluates whether combining UAV imagery with weather variables and elapsed time improves continuous rice growth-rate prediction in a single-site, publicly available rice-seedling dataset spanning multiple growing seasons. A Time-Aware Late Fusion (TALF) model is introduced in which a convolutional branch encodes image features, a multilayer perceptron encodes contextual features, and the two streams are merged only at the regression head. Relative humidity, wind speed, and elapsed time are used as contextual inputs after season-aware preprocessing. Evaluation is reported on a chronological multi-season split using internal ablations rather than external generalization claims. TALF achieved a mean absolute error (MAE) of 0.031, compared with 0.1455 for the optimized image-only baseline and 0.0890 for an early-fusion image-and-weather baseline. A secondary tolerance-based metric reached 94.9% under the reported threshold. The results indicate that weather and elapsed-time context improve prediction on this dataset and that separating image and tabular encoders until the final layers is a competitive multimodal learning design under the reported protocol.