| 无人机遥感解析田间作物表型信息研究进展 |
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| 引用本文： | 刘建刚,赵春江,杨贵军,于海洋,赵晓庆,徐波,牛庆林.无人机遥感解析田间作物表型信息研究进展[J].农业工程学报,2016,32(24):98-106. |
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| 作者姓名： | 刘建刚 赵春江 杨贵军 于海洋 赵晓庆 徐波 牛庆林 |
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| 作者单位： | 1. 北京农业信息技术研究中心，北京 100097; 国家农业信息化工程技术研究中心，北京 100097; 农业部农业信息技术重点实验室，北京 100097;2. 北京农业信息技术研究中心，北京 100097; 国家农业信息化工程技术研究中心，北京 100097;3. 北京农业信息技术研究中心,北京,100097;4. 北京农业信息技术研究中心，北京 100097; 河南理工大学测绘与国土信息工程学院焦作 454000 |
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| 基金项目： | 北京市自然科学基金项目（4141001）；国家自然科学基金项目（41471285，61661136003）；北京市农林科学院科技创新能力建设项目（KJCX20140417）；北京市博士后工作经费资助项目。 |
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| 摘 要： | 田间作物表型信息是揭示作物生长发育规律及其与环境关系的重要依据,传统的田间试验取样和车载高通量平台测定作物性状参数的方法耗时耗力,且空间覆盖不全,限制了作物科学研究的快速发展,而以无人机为代表的近地遥感高通量表型平台凭借机动灵活、成本低、空间覆盖广的优势成为获取田间作物表型信息的重要手段。该文根据国内外无人机遥感平台解析作物表型信息的最新研究成果,针对不同传感器类型分析了无人机遥感解析作物表型信息的应用及其不足,总结了遥感定量反演作物表型信息的方法体系,展望了无人机载遥感技术在作物表型信息解析方面的应用前景。该项研究成果对推广无人机遥感平台获取田间作物表型信息、提高复杂农田环境作物长势信息的解析和辨识能力具有重要意义。
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| 关 键 词： | 无人机 传感器 遥感 作物 表型信息 |
| 收稿时间： | 2016/9/14 0:00:00 |
| 修稿时间： | 2016/11/14 0:00:00 |
Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform |
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| Liu Jiangang,Zhao Chunjiang,Yang Guijun,Yu Haiyang,Zhao Xiaoqing,Xu Bo and Niu Qinglin.Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(24):98-106. |
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| Authors: | Liu Jiangang Zhao Chunjiang Yang Guijun Yu Haiyang Zhao Xiaoqing Xu Bo and Niu Qinglin |
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| Institution: | 1. Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China; 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; 3. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China,1. Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China; 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; 3. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China,1. Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China; 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China; 3. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China,1. Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China; 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China,1. Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China,1. Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China; 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China and 1. Beijing Research Center for Information Technology in Agricultural, Beijing 100097, China;4. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China |
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| Abstract: | The field-based phenotyping plays an important role in exploring the laws of crop growth and development. Accurately and rapidly acquiring phenotype of plants or cells in different environments can accelerate the efficiency of understanding the response of crops to environmental factors, which can provide theoretical and technical support for accelerating the development of crop science and prompting the sustainable development of agriculture in China. Traditional method for the determination of crop parameters is based on field sampling and mechanized high-throughput platform, which is time-consuming, and has low efficiency and incomplete spatial coverage. The development of crop science is limited by the quick and accurate acquisition of field-based phenotyping. Proximal remote sensing phenotyping platform represented by the unmanned aerial vehicle (UAV) can be timely, rapid, noninvasive and efficient access to crop phenotypes, which makes it become an important means to obtain crop phenotypic information and a powerful tool to study the phenotypes. According to the latest research achievements of UAV-RSP (remote sensing platform) in analyzing the crop phenotype, the review described the advantage and disadvantage of the application of UAV-RSP with different types of sensors to field-based phenotyping, summed up the methods for quantitative remote sensing inversion of crop phenotypic information, and prospected the application of UAV-RSP to field-based phenotyping. UAV-RSP has the advantages of flexible motor, being suitable for complex field environment, timely data acquisition, high efficiency and low cost, which can be used to rapid and cost?effective phenotype judge for a large number of plots and field trials of massive germplasm. The vehicles with flying capacity without manned control have several types, ranging from multicopters and helicopters to fixed wing. The most frequently used sensors in UAV-RSP include digital camera, multispectral sensor, hyperspectral sensor, thermal imager and Light Detection and Ranging (LiDAR) systems. The UAV-RSP have been used for acquiring crop traits including morphological parameters, spectrum and texture characteristics, physiological traits, and response to abiotic/biotic stress under different environments. However, the field-based phenotyping by UAV-RSP usually focuses on wheat, maize, rice and sorghum, and there are more crops that need to be investigated. And the crop phenotyping by remote sensing lacks the fusion of multi-source data. Strategies for high-throughput field-based phenotyping were investigated with different methods, which showed obvious difference of estimation accuracy. The accuracy of retrieval model for estimating field-based phenotyping depends on the climate, crops and their growth stages. Methods such as machine learning, spectral retrieval, analysis of canopy temperature and comprehensive evaluation have been widely used for field-based phenotyping by researchers. However, there exist some problems that restrict the application of UAV-based crop phenotyping. The shortcomings include the imperfection of agricultural aviation regulations, the deficiencies of phenotyping for massive germplasm, the lack of method for fast hyperspectral and LiDAR data management, the high price of sensors and the uncertainty of remote sensing retrieval model. The developments of UAV-based phenotyping are as follows: the combination of crop breeding and UAV remote sensing, the efficient data processing, the well-informed analysis of multi-source data, and the development of low-cost sensor. The results of this study have important significance for promoting the application of UAV-RSP and speeding up the development of crop science. |
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| Keywords: | unmanned aerial vehicle sensors remote sensing crops phenotyping |
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