  Plant health can be monitored dynamically using multispectral sensors that
measure Near-Infrared reflectance (NIR). Despite this potential, obtaining and
annotating high-resolution NIR images poses a significant challenge for
training deep neural networks. Typically, large networks pre-trained on the RGB
domain are utilized to fine-tune infrared images. This practice introduces a
domain shift issue because of the differing visual traits between RGB and NIR
images.As an alternative to fine-tuning, a method called low-rank adaptation
(LoRA) enables more efficient training by optimizing rank-decomposition
matrices while keeping the original network weights frozen. However, existing
parameter-efficient adaptation strategies for remote sensing images focus on
RGB images and overlook domain shift issues in the NIR domain. Therefore, this
study investigates the potential benefits of using vision transformer (ViT)
backbones pre-trained in the RGB domain, with low-rank adaptation for
downstream tasks in the NIR domain. Extensive experiments demonstrate that
employing LoRA with pre-trained ViT backbones yields the best performance for
downstream tasks applied to NIR images.
