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Smart Agriculture ›› 2022, Vol. 4 ›› Issue (4): 74-83.doi: 10.12133/j.smartag.SA202209001

• 专题--大田作物智慧种植 • 上一篇    下一篇

基于无人机遥感表型监测的苎麻优质种质资源筛选方法

付虹雨(), 王薇, 廖澳, 岳云开, 许明志, 王梓薇, 陈建福, 佘玮, 崔国贤()   

  1. 湖南农业大学 农学院,湖南 长沙 410128
  • 收稿日期:2022-09-05 出版日期:2022-12-30 发布日期:2023-01-27
  • 基金资助:
    国家重点研发计划课题(2018YFD0201106);财政部和农业农村部:国家现代农业产业技术体系(CARS-16-E11);国家自然科学基金(31471543)
  • 作者简介:付虹雨(1997‒),女,博士研究生,研究方向为作物遥感。E-mail:347180050@qq.com
  • 通讯作者: 崔国贤 E-mail:347180050@qq.com;627274845@qq.com

High Quality Ramie Resource Screening Based on UAV Remote Sensing Phenotype Monitoring

FU Hongyu(), WANG Wei, LIAO Ao, YUE Yunkai, XU Mingzhi, WANG Ziwei, CHEN Jianfu, SHE Wei, CUI Guoxian()   

  1. College of Agronomy, Hunan Agricultural University, Changsha 410128, China
  • Received:2022-09-05 Online:2022-12-30 Published:2023-01-27
  • corresponding author: CUI Guoxian E-mail:347180050@qq.com;627274845@qq.com

摘要:

苎麻是重要的纤维作物之一,由于土地资源紧缺及优良品种的推广应用等原因,苎麻遗传变异和遗传多样性减少,对苎麻种质资源多样性调查和保护的需求日趋加大。基于无人机遥感的作物表型测量方法可以对不同基因型作物的生长特性进行频繁、快速、无损、精准的监测,实现作物种质资源调查,筛选特异优质品种。为了实现苎麻种质资源表型的高效综合评价,辅助筛选优势苎麻品种,本研究提出了一种基于无人机遥感影像的苎麻种质资源表型监测及筛选方法。首先,基于无人机遥感影像,利用Pix4dmapper软件生成试验区的数字地表模型(Digital Surface Model,DSM)和正射影像;然后,对苎麻种质资源关键表型参数(株高、株数、叶面积指数、叶片叶绿素含量、含水量)进行估测。基于DSM采用“差分法”提取苎麻株高,基于正射图像采用目标检测算法提取苎麻株数,采用机器学习方法估测苎麻叶面积指数(Leaf Area Index,LAI)、叶片叶绿素含量(SPAD值)、含水量;最后,根据提取的各项遥感表型参数,采用变异性分析和主成分分析方法对苎麻种质资源进行遗传多样性分析。结果表明,(1)基于无人机遥感的苎麻表型估测效果较好,株高的拟合精度为0.93,均方根误差为5.65 cm;SPAD值、含水量、LAI的拟合指标分别达到0.66、0.79、0.74,RMSE分别为2.03、2.21、0.63;(2)苎麻种质资源的遥感表型存在较大差异,LAI、株高和株数的估测值变异系数分别达到20.82%、24.61%和35.48%;(3)利用主成分分析法将苎麻种质资源的遥感表型聚类为因子1(株高、LAI)和因子2(LAI、SPAD值),因子1可用于苎麻种质资源结构特征评价,因子2可以作为高光效苎麻资源的筛选指标。本研究将为作物种质资源表型监测和育种相关分析提供参考。

关键词: 苎麻, 种质资源多样性, 表型, 无人机遥感, 数字地表模型, 机器学习

Abstract:

Ramie is an important fiber crop. Due to the shortage of land resources and the promotion of excellent varieties, the genetic variation and diversity of ramie decreased, which increased the need for investigation and protection of the ramie germplasm resources diversity. The crop phenotype measurement method based on UAV remote sensing can conduct frequent, rapid, non-destructive and accurate monitoring of different genotypes, which can fulfill the investigation of crop germplasm resources and screen specific and high-quality varieties. In order to realize efficient comprehensive evaluation of ramie germplasm phenotype and assist in screening of dominant ramie varieties, a method for monitoring and screening ramie germplasm phenotype was proposed based on UAV remote sensing images. Firstly, based on UAV remote sensing images, the digital surface model (DSM) and orthophoto of the test area were generated by Pix4dmapper. Then, the key phenotypic parameters (plant height, plant number, leaf area index, leaf chlorophyll content and water content) of ramie germplasm resources were estimated. The subtraction method was used to extract ramie plant height based on DSM, while the target detection algorithm was applied to extract ramie plant number based on orthographic images, and four machine learning methods were used to estimate the leaf area index (LAI), leaf chlorophyll content (SPAD value) and water content. Finally, according to the extracted remote sensing phenotypic parameters, the genetic diversity of ramie germplasm was analyzed by using variability analysis and principal component analysis. The results showed that: (1) The ramie phenotype estimation based on UAV remote sensing was effective, with the fitting accuracy of plant height 0.93, and the root mean square error (RMSE) 5.654 cm. The fitting indexes of SPAD value, water content and LAI were 0.66, 0.79 and 0.74, respectively, and RMSE were 2.03, 2.21 and 0.63, respectively; (2) The remote sensing phenotypes of ramie germplasm were significantly different, as the coefficients of variation of LAI, plant height and plant number reached 20.82%, 24.61% and 35.48%, respectively; (3) Principal component analysis was used to cluster the remote sensing phenotypes into factor 1 (plant height and LAI) and factor 2 (LAI and SPAD value), factor 1 can be used to evaluate the structural characteristics of ramie germplasm resources, and factor 2 can be used as the screening index of high-light efficiency ramie resources. This study could provide references for crop germplasm phenotypic monitoring and breeding correlation analysis.

Key words: ramie, diversity of germplasm resources, phenotype, UAV remote sensing, digital surface model, machine learning

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