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.

使用视觉转换器 (ViT) 的预训练骨干网络与低秩适应 (LoRA) 在 NIR 领域的下游任务中，通过广泛实验表明，能够实现对 NIR 图像的最佳性能。

远程感知中基于近红外和低秩适应的视觉变换网络

Near-Infrared and Low-Rank Adaptation of Vision Transformers in Remote  Sensing

Monitoring plant health is crucial for maintaining agricultural productivity
and food safety. Disruptions in the plant's normal state, caused by diseases,
often interfere with essential plant activities, and timely detection of these
diseases can significantly mitigate crop loss. In this study, we propose a deep
learning-based approach for efficient detection of plant diseases using
drone-captured imagery. A comprehensive database of various plant species,
exhibiting numerous diseases, was compiled from the Internet and utilized as
the training and test dataset. A Convolutional Neural Network (CNN), renowned
for its performance in image classification tasks, was employed as our primary
predictive model. The CNN model, trained on this rich dataset, demonstrated
superior proficiency in crop disease categorization and detection, even under
challenging imaging conditions. For field implementation, we deployed a
prototype drone model equipped with a high-resolution camera for live
monitoring of extensive agricultural fields. The captured images served as the
input for our trained model, enabling real-time identification of healthy and
diseased plants. Our approach promises an efficient and scalable solution for
improving crop health monitoring systems.

本研究提出了一种基于深度学习的方法，利用无人机捕获的图像实现高效的作物病害检测，该方法在丰富的植物物种和疾病数据库上训练，使用卷积神经网络作为主要预测模型，通过实现世界上第一款配备高分辨率相机的无人机进行实时监测，为提高农作物健康监测系统提供了一种高效可行的解决方案。