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

摘要/Abstract

摘要：

植物表型是指基因和环境因素决定或影响的作物物理、生理、生化特征和性状。准确和快速的获取植物在各种不同环境条件下的表型信息，从而挖掘其基因组的遗传和表现规律，可有效推动有关基因组与表型信息关联性研究。无人机高通量植物表型平台凭借无人机机动灵活的特点，适合于农作物田间环境中的植物表型数据获取，具有数据获取效率高和成本低等优势，借助于图像、高光谱、激光雷达等先进传感器技术，为高效获取各类植物表型数据提供了可行的途径；与此同时，快速发展的大数据技术和智能数据分析技术为无人机所获取的植物表型图像提供有效的分析处理方法和技术。在此背景下，基于无人机平台的高通量植物表型分析，为研究田间作物表型信息提供了重要的方法和工具。本文综述了国内外无人机高通量作物表型大数据分析的最新研究成果，就其研究原理、相关算法、过程、关键技术及应用等进行总结与分析，重点讨论了应用于无人机高通量植物表型大数据分析相关的大数据处理与智能分析技术，重点分析了植物株高获取、叶面积指数、植物病害等典型的表型分析需求，并就其应用前景进行了总结和展望。

关键词: 表型大数据, 无人机, 高通量, 植物表型分析

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

中图分类号:

袁培森, 薛铭家, 熊迎军, 翟肇裕, 徐焕良. 基于无人机高通量植物表型大数据分析及应用研究综述[J]. 农业大数据学报, 2021, 3(3): 62-75.

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

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[3]	丁国辉,许昊,温明星,陈佳玮,王秀娥,周济. 基于经济型低空无人机对小麦重要产量表型性状的多生育时期获取和自动化分析[J]. 农业大数据学报, 2019, 1(2): 19-31.