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

doi:10.19788/j.issn.2096-6369.190202

农业大数据学报 ›› 2019, Vol. 1 ›› Issue (2): 19-31.doi: 10.19788/j.issn.2096-6369.190202

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

丁国辉¹⁺,许昊¹⁺,温明星^2,³,陈佳玮¹,王秀娥^3,^*(),周济^1,^4,^*()

1.南京农业大学作物表型交叉研究中心，中英植物表型组学联合研究中心，南京 210095
2.江苏丘陵地区镇江农业科学研究所，句容 212400
3.南京农业大学作物遗传与种质创新国家重点实验室，南京 210095
4.英国厄尔汉姆研究中心（Earlham Institute）,诺维奇科研院（Norwich Research Park）,诺维奇英国 NR4 7UZ

收稿日期:2019-03-15 出版日期:2019-06-26 发布日期:2019-08-21
通讯作者: 王秀娥,周济 E-mail:xiuew@njau.edu.cn;zhou@njau.edu.cn
作者简介:丁国辉,男,博士研究生,研究方向:无人机作物表型,小麦育种;E-mail: 2018201009@njau.edu.cn;|许昊,男,硕士研究生,研究方向:植物表型、图像处理;E-mail: 2018801232@njau.edu.cn
基金资助:
江苏省面上项目(SBK2019021839);中英农业科技牛顿基金(GP131-JZ1-G)

Developing cost-effective and low-altitude UAV aerial phenotyping and automated phenotypic analysis to measure key yield-related traits for bread wheat

Guohui Ding¹⁺,Hao Xu¹⁺,Mingxing Wen^2,³,Jiawei Chen¹,Xiue Wang^3,^*(),Ji Zhou^1,^4,^*()

1.Plant Phenomics Research Center, Nanjing Agricultural University, NanJing 210095
2.Zhenjiang Academy of Agricultural Sciences, Jurong 212400
3.State Key Laboratory of Crop Genetics & Germplasm Enhancement, NanJing 210095
4.Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK

Received:2019-03-15 Online:2019-06-26 Published:2019-08-21
Contact: Xiue Wang,Ji Zhou E-mail:xiuew@njau.edu.cn;zhou@njau.edu.cn

摘要/Abstract

摘要：

多尺度表型采集技术通过多种手段获取植物图像和光谱数据,进而基于各类计算机分析算法（如,计算机视觉和机器学习）进行表型分析,得到与产量、品质和抗逆等相关的性状信息,为作物遗传育种、栽培和农业生产提供高通量、大数据的技术支撑。小麦作为我国重要的粮食作物,其关键产量性状的全生育期量化分析有重要意义。本文详细介绍了部分重要的小麦产量相关性状,并通过使用经济型低空无人机对不同关键生育时期中的一些共同的产量性状进行了规模化采集。然后,基于无人机获取的可见光图像,通过第三方专业软件Pix4D完成了全试验田的拼接和三维点云重建,并通过自主开发的性状分析算法对一些重要产量性状和植被指数等完成了自动化分析。同时,针对18个不同的小麦基因型完成了关键生育时期的株高、植被指数、叶面积指数的提取。通过实例验证了基于经济型低空无人机开展小麦产量性状采集的有效方法和高通量分析技术。本研究对降低田间作物表型研究的门槛,促进我国各研究团队采用标准化表型数据采集,统一作物表型数据规范,以及推广使用开源软件自主开发自动化分析技术平台有重要意义。

关键词: 小麦, 产量性状, 无人机表型技术, 高通量性状分析, 图像处理, 表型, 植物表型组学

Abstract:

[Multi-scale plant phenotyping technologies are capable of collecting big vision-based and spectroscopic datasets, based on which reliable phenotypic analysis can be carried out through a range of computational algorithms based on computer vision and machine learning techniques. Quantitative traits measurement is key to crop genetics, breeding, cultivation and agricultural practices, because they can be used to dynamically evaluate yield, quality and stress resistance in a high-throughput and reproducible manner. As an important staple crop in China, it is essential to establish a systematic approach to monitor wheat growth and quantify yield-related traits during key growth stages. In this work, we firstly reviewed important yield-related traits for bread wheat and then developed a field phenotyping approach to collect a number of common traits using cost-effective and low-altitude unmanned aerial vehicles (UAVs). Based on the visible spectrum images acquired in a field experiment, we utilized professional software (i.e. Pix4Dmapper) to stitch UAV sub-images as well as to reconstruct 3D point cloud to represent the whole experimental field. After this phase, we developed an automated traits analysis pipeline to produce the vegetation map (e.g. Excess-Green index, ExG) and measure important yield-related traits. We have quantified plant height, vegetation index (e.g. ExG) and leaf area index at five key growth stages for 18 wheat genotypes. Our work validates that yield-related traits can be acquired through cost-effective UAVs, which can lower the threshold of conducting field phenotyping and reliable phenotypic analysis. Our work also exhibits a promising approach for research groups and organizations to follow standardize data collection, phenotyping data ontology, as well as the utilization of open-source analytic libraries to develop high-throughput phenotypic analysis techniques in crop phenotyping research.

Key words: wheat, yield-related traits, UAV aerial phenotyping, high-throughput phenotypic analysis, image processing, phenotyping, plant phenomics

中图分类号:

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.

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.

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生育时期	小麦产量性状
分蘖期	分孽形态、株高、叶色、叶面积指数、植被指数。
拔节期	株高、叶色、叶面积指数、植被指数。
孕穗期	株高、叶色、叶面积指数、植被指数、旗叶形态。
抽穗期	株高、叶色、叶面积指数、植被指数。
开花期	株高、叶色、叶面积指数、植被指数。

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

Developing cost-effective and low-altitude UAV aerial phenotyping and automated phenotypic analysis to measure key yield-related traits for bread wheat

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