基于无人机高通量植物表型大数据分析及应用研究综述

doi:10.19788/j.issn.2096-6369.210307

Abstract

Abstract:

Plant phenotypes refer to the physical, physiological and biochemical characteristics and traits that are determined or influenced by genes and environmental factors. Accurate and rapid access to plant phenotypic information under different environmental conditions, and the analysis of the genetic and performance patterns of their genomes, can effectively promote research on the correlation between genomic and phenotypic information. The Unmanned Aerial Vehicle (UAV) high-throughput plant phenotyping platform is suitable for acquiring plant phenotypic data in field environments owing to the UAV’s mobility and flexibility, and it has the great advantages of a high data acquisition efficiency and low cost. With the help of advanced sensor technologies, such as hyperspectral imaging and LIDAR, the UAV provides a feasible way to efficiently acquire plant phenotypic data. Effective analyses and processing methods and techniques for plant phenotypic data acquired by UAVs must be employed. Thus, high-throughput plant phenotypic analyses based on UAV platforms provides an important tool for studying plant phenotypic information from the field. This paper summarizes and analyzes the latest research results of UAV-based high-throughput crop phenotyping using big data analysis technology and artificial intelligence, as well as its research principles, relevant algorithms, processes, key technologies and applications. The main focus is on big data processing and intelligent analysis techniques related to UAV-based high-throughput plant phenotype big data and to the analysis of typical phenotypes, such as plant height, leaf area index, and plant diseases. We analyzed the current research needs and provide both a summary and outlook on related applications.

Key words: phenotypic big data, unmanned aerial vehicle, high-throughput, plant phenotyping

CLC Number:

Peisen Yuan, Mingjia Xue, Yingjun Xiong, Zhaoyu Zhai, Huanliang Xu. Analysis and Application of High-throughput Plant Phenotypic Big Data Collected from Unmanned Aerial Vehicles[J].Journal of Agricultural Big Data, 2021, 3(3): 62-75.

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传感器类型	优势	不足	应用
数码相机	成本低、直观便捷的获取作物表型信息	易受环境光阴影影响，解析表型信息较少；像幅较小、影响数量多。	叶色、花期、株高、倒伏、冠层覆盖度
光谱成像仪	可以间接观测多项作物表型信息；不仅有光谱分辨能力，还有图像分辨能力	需要辐射及几何校正；高光谱数据处理较为复杂	LAI、生物量、产量、出苗率、返青率、氮含量、叶绿素含量、水分状态、蛋白质含量、净同化速率
热成像仪	可实现作物生物/非生物胁迫条件下作物生长状态的间接测定	易受环境条件的影响，很难比较不同时间的数据；难以消除土壤的影响；需频繁的校准。	净同化速率、作物水分状态、气孔导度、产量
激光雷达	丰富的点云信息，高精度的水平和垂直植被冠层结构参数	成本高，数据处理量较大，易受天气影像	株高、生物量

数码影像变量	公式	变量编码
MGRVI	MGRVI=(g2—r2)/ (g2+r2)	VI4
RGBVI	RGBVI=(g2—br)/(g2+br)	VI5
GRVI	GRVI=(g-r)/(g+r)	VI6
GLA	GLA=(2g-r-b)/(2g+r+b)	VI7
ExR	ExR=1.4r-g	VI8
ExG	ExG=2g-r-b	VI9
ExGR	ExGR=ExG-1.4r-g	VI10
CIVE	CIVE=0.441r-0.881g+ 0.3856b+18.78745	VI11
VARI	VARI=(g-r)/(g+r-b)	VI12
g/r	g/r=g/r	VI13
g/b	g/b=g/b	VI14
r/b	r/b=r/b	VI15

[1]	Jia He, Laigang Wang, Yan Guo, Yan Zhang, Xiuzhong Yang, Ting Liu, Hongli Zhang. Estimating the Leaf Area Index of Maize based on Unmanned Aerial Vehicle Multispectral Remote Sensing [J]. Journal of Agricultural Big Data, 2021, 3(4): 20-28.
[2]	Guohui Ding,Hao Xu,Mingxing Wen,Jiawei Chen,Xiue Wang,Ji Zhou. Developing cost-effective and low-altitude UAV aerial phenotyping and automated phenotypic analysis to measure key yield-related traits for bread wheat [J]. Journal of Agricultural Big Data, 2019, 1(2): 19-31.

Analysis and Application of High-throughput Plant Phenotypic Big Data Collected from Unmanned Aerial Vehicles

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