Abstract The current methods of phenotyping for breeding lines require a lot of time, labor and cost. In recent years, unmanned aerial system (UAS) has paved the way for the development of field high-throughput phenotyping for crops rapidly. Different sensors such as regular RGB camera (Red, Green and Blue), multispectral imaging camera (several wavebands), hyperspectral imaging camera (hundreds and even thousands of wavebands), thermal imaging sensor and light detection and ranging (LiDAR) sensor can be placed on unmanned aerial vehicle (UAV) to collect remote sensing data in field-scale trials. Based on this technique, the plant traits (e.g., yield, biomass, height, and leaf area index) can be estimated non-destructively, which is critical for high-throughput phenotyping in agriculture. Compared with ground vehicle-based sensors, UAS can increase throughput and frequency for phenotyping. It is low-cost and could provide high-resolution images compared with satellite-based technique. Based upon the phenotypic traits, those crops with high yield and strong stress resistance (e.g., disease resistance and salt resistance) can be selected, which could finally improve the production. This paper talked about the plant high-throughput phenotyping traits based on the sensors on the UAV. Also, the challenges and obstacles of UAV (e.g., flight safety, flight altitude, flight time, and sensor accuracy) were analyzed. In order to provide the updated information of the relationships between remote sensing information taken from UAV and plant phenotyping traits, we summarized the sensors, plants and traits reported in previous research articles. As a result, the review can be very useful for researchers to use appropriate UAV-based sensors to carry out plant phenotyping experiments, and for farmers to use this advanced technology in managing agricultural production.

中文翻译：

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使用无人机传感器对植物高通量表型性状进行综述

摘要 当前的育种系表型分析方法需要大量的时间、人力和成本。近年来，无人机系统（UAS）为作物田间高通量表型的快速发展铺平了道路。可以放置不同的传感器，如常规RGB相机（红、绿、蓝）、多光谱成像相机（几个波段）、高光谱成像相机（数百甚至数千波段）、热成像传感器和光探测和测距（LiDAR）传感器无人机 (UAV) 以在现场规模试验中收集遥感数据。基于这种技术，可以无损地估计植物性状（例如，产量、生物量、高度和叶面积指数），这对于农业中的高通量表型分析至关重要。与地面车辆传感器相比，UAS 可以提高表型分析的吞吐量和频率。与基于卫星的技术相比，它成本低，可以提供高分辨率的图像。根据表型性状，可以筛选出产量高、抗逆性强（如抗病、抗盐）的作物，最终提高产量。本文讨论了基于无人机上传感器的植物高通量表型性状。此外，还分析了无人机的挑战和障碍（例如，飞行安全、飞行高度、飞行时间和传感器精度）。为了提供无人机遥感信息与植物表型性状之间关系的最新信息，我们总结了之前研究文章中报道的传感器、植物和性状。因此，