Yang Guijun, Liu Jiangang, Zhao Chunjiang, Li Zhenhong, Huang Yanbo, Yu Haiyang, Xu Bo, Yang Xiaodong, Zhu Dongmei, Zhang Xiaoyan, Zhang Ruyang, Feng Haikuan, Zhao Xiaoqing, Li Zhenhai, Li Heli, Yang Hao
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P. R. China, Beijing Research Center for Information Technology in AgricultureBeijing, China.
National Engineering Research Center for Information Technology in AgricultureBeijing, China.
Front Plant Sci. 2017 Jun 30;8:1111. doi: 10.3389/fpls.2017.01111. eCollection 2017.
Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.
表型分析在作物科学研究中发挥着重要作用;准确、快速地获取不同环境下植物或细胞的表型信息,有助于探索基因组的遗传和表达模式,确定基因组信息与表型信息的关联,从而提高作物产量。传统的获取作物性状(如株高、叶色、叶面积指数(LAI)、叶绿素含量、生物量和产量)的方法依赖于人工采样,既耗时又费力。配备不同传感器的无人机遥感平台(UAV-RSPs)近来已成为快速、无损高通量表型分析的重要手段,具有操作灵活便捷、可按需获取数据以及空间分辨率高的优势。无人机遥感平台是研究表型组学和基因组学的有力工具。鉴于有必要向那些希望以最小的田间工作量从大田中获取表型参数并获得高度可靠结果的用户介绍使用无人机进行田间表型分析的方法和应用,基于对Web of Science™核心合集数据库中使用无人机遥感平台进行作物表型分析的文献调查以及NERCITA的案例研究,对用于田间表型分析的无人机遥感平台这一主题的现状和前景进行了综述。文中考虑了无人机平台和遥感传感器的选择参考、无人机遥感平台分析表型性状时常用的方法和典型应用,以及无人机遥感平台在作物表型分析方面面临的挑战。该综述可为推动无人机遥感平台在作物表型分析中的应用提供理论和技术支持。