论文标题
VDDNET：基于多光谱图像和深度图的葡萄病检测网络
VddNet: Vine Disease Detection Network Based on Multispectral Images and Depth Map
论文作者
论文摘要
早期发现葡萄病对于避免病毒或真菌的传播很重要。疾病的传播可能导致葡萄生产和灾难性经济后果的巨大损失，因此，该问题代表了精确农业的挑战。在本文中，我们提出了一种新的葡萄病检测系统。本文包含两个贡献：第一个是从无人驾驶汽车（UAV）获取的多光谱图像中的自动矫形器注册方法。第二个是一种称为VDDNET（藤蔓疾病检测网络）的新的深度学习体系结构。通过将其与最著名的架构进行比较来评估所提出的架构：SEGNET，U-NET，DEEPLABV3+和PSPNET。深度学习体系结构接受了多光谱数据和深度图信息的培训。提出的体系结构的结果表明，VDDNET体系结构的分数高于基本方法。此外，这项研究表明，与直接使用无人机图像的方法相比，所提出的系统具有许多优势。
Early detection of vine disease is important to avoid spread of virus or fungi. Disease propagation can lead to a huge loss of grape production and disastrous economic consequences, therefore the problem represents a challenge for the precision farming. In this paper, we present a new system for vine disease detection. The article contains two contributions: the first one is an automatic orthophotos registration method from multispectral images acquired with an unmanned aerial vehicle (UAV). The second one is a new deep learning architecture called VddNet (Vine Disease Detection Network). The proposed architecture is assessed by comparing it with the most known architectures: SegNet, U-Net, DeepLabv3+ and PSPNet. The deep learning architectures were trained on multispectral data and depth map information. The results of the proposed architecture show that the VddNet architecture achieves higher scores than the base line methods. Moreover, this study demonstrates that the proposed system has many advantages compared to methods that directly use the UAV images.
