Review of Deep Learning Applications in Unmanned Aerial Vehicle Remote Sensing Images of Grass Plants

doi:10.3778/j.issn.1673-9418.2405012

Abstract

Abstract: The invasion of grass species competes for resources necessary for the growth of crops, significantly affecting crop yield and quality. Grasses such as the Artemisia genus release allergenic pollen, causing allergic reactions and impacting human health. The integration of deep learning and unmanned aerial vehicle (UAV) remote sensing technologies for the efficient identification and detection of grass species is of considerable practical significance in the prevention of plant invasion, allergen monitoring, and agricultural production management. Currently, remote sensing images face challenges such as low resolution, complex background information, and indistinct detail information. The combination of deep learning with UAV remote sensing RGB images and multispectral images addresses various application challenges, including high grass density, numerous species, wide coverage, and identification difficulties. This paper provides a comprehensive and in-depth review of the research progress on the application of deep learning technology in UAV remote sensing images of grass species. Initially, this paper elaborates on various UAV remote sensing image technologies widely used in grass species research, with a particular emphasis on the application of visible light RGB and multispectral remote sensing technologies. Subsequently, it provides a detailed summary of UAV-acquired grass species datasets, and focuses on the main network architectures and methods where deep learning is applied to grass species remote sensing images. Finally, it summarizes the current major issues when using deep learning in UAV remote sensing images of grass plants, and anticipates future development trends.

Key words: deep learning, unmanned aerial vehicle (UAV), remote sensing images, grass plants

摘要： 草类植物入侵后争夺农作物生长所需的资源，严重影响农作物的产量和品质；蒿属植物等草类植物释放过敏性花粉引起人体过敏反应，影响人们的身体健康。因此将深度学习与无人机遥感技术相结合，并对草类植物进行高效识别与检测，在植物入侵预防、过敏原监测、农业生产管理等方面具有重要的现实意义。目前遥感图像存在分辨率较低、背景信息较复杂、细节信息不明显等问题，深度学习结合无人机遥感RGB图像、多光谱图像等可以解决草类植物密度大、种类多、覆盖范围广、识别困难等各种应用问题。对深度学习技术应用于草类植物的无人机遥感图像的研究进展进行了全面而深入的综述。阐述了在草类植物研究中广泛应用的多种无人机遥感图像技术，着重介绍了可见光RGB和多光谱遥感技术的应用。详细总结了无人机航拍草类植物数据集，并重点介绍了目前将深度学习应用于草类植物遥感图像中所采用的主要网络结构和方法。最后归纳了目前草类植物无人机遥感图像应用深度学习技术面临的主要问题，并展望了未来发展趋势。

关键词: 深度学习, 无人机（UAV）, 遥感图像, 草类植物

LI Shaobo, WANG Xiaoqiang, GUO Libiao, HONG Ying, WANG Zhiguo. Review of Deep Learning Applications in Unmanned Aerial Vehicle Remote Sensing Images of Grass Plants[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(5): 1157-1176.

李少波, 王晓强, 郭利彪, 红英, 王志国. 草类植物无人机遥感图像中深度学习应用综述[J]. 计算机科学与探索, 2025, 19(5): 1157-1176.

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