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作物学报 ›› 2020, Vol. 46 ›› Issue (9): 1448-1455.doi: 10.3724/SP.J.1006.2020.04020

• 研究简报 • 上一篇    下一篇

基于无人机遥感图像的苎麻产量估测研究

付虹雨1(), 崔国贤1,2,*(), 李绪孟2,*(), 佘玮1, 崔丹丹1, 赵亮1, 苏小惠1, 王继龙1, 曹晓兰1, 刘婕仪1, 刘皖慧1, 王昕惠1   

  1. 1 湖南农业大学苎麻研究所, 湖南长沙 410128
    2 湖南农业大学农学院, 湖南长沙 410128
  • 收稿日期:2020-02-01 接受日期:2020-04-15 出版日期:2020-09-12 网络出版日期:2020-04-26
  • 通讯作者: 崔国贤,李绪孟
  • 作者简介:付虹雨, E-mail: 347180050@qq.com
  • 基金资助:
    本研究由国家重点研发计划项目(2018YFD0201106);国家现代农业产业技术体系(麻类)建设专项(CARS-16-E11);国家自然科学基金项目(31471543);湖南省重点研发计划项目资助(2017NK2382)

Estimation of ramie yield based on UAV (Unmanned Aerial Vehicle) remote sensing images

FU Hong-Yu1(), CUI Guo-Xian1,2,*(), LI Xu-Meng2,*(), SHE Wei1, CUI Dan-Dan1, ZHAO Liang1, SU Xiao-Hui1, WANG Ji-Long1, CAO Xiao-Lan1, LIU Jie-Yi1, LIU Wan-Hui1, WANG Xin-Hui1   

  1. 1 Ramie Research Institute of Hunan Agricultural University, Changsha 410128, Hunan, China
    2 College of Agriculture, Hunan Agricultural University, Changsha 410128, Hunan, China
  • Received:2020-02-01 Accepted:2020-04-15 Published:2020-09-12 Published online:2020-04-26
  • Contact: Guo-Xian CUI,Xu-Meng LI
  • Supported by:
    National Key Research and Development Program of China(2018YFD0201106);China Agriculture Research System(CARS-16-E11);National Natural Science Foundation of China(31471543);Key Research and Development Program of Hunan Province(2017NK2382)

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摘要：

本研究旨在探索一种利用无人机-RGB系统提取的苎麻株高和可见光图像光谱信息估测产量的新方法。试验于2019年在湖南农业大学耘园苎麻基地进行, 利用无人机搭载高清数码相机获取二季苎麻苗期和成熟期的图像。首先利用Pix4D mapper生成苎麻冠层2个生育期的数字表面模型和高清数码正射图像; 然后基于数字表面模型采用“差分法”计算试验小区的平均株高(DSM-based H); 基于正射图像提取试验小区RGB通道均值, 进而计算遥感图像数码变量和植被指数, 分析苎麻种质间的图像光谱表型性状和产量株高比性状的差异性与多样性; 最后采用逐步回归方法建立苎麻产量预测模型, 并对各项产量解释因子进行相关性分析。结果表明: (1)基于无人机-RGB系统遥感株高与实测株高显著相关(r=0.90), 修正遥感株高的均方根误差为0.04 m。(2)苎麻产量与株高信息存在极显著相关性(r=0.91), 而与图像光谱表型相关性不明显。(3)融合遥感图像株高和种质特征差异构建的苎麻产量估测模型精度较高, R2=0.85, RMSE=0.71。因此, 基于无人机遥感图像的苎麻产量估测是可行的, 这对苎麻种质特征评价和产量估测具有重要的意义。

关键词: 无人机, 苎麻, 遥感图像, 株高, 产量

Abstract:

This paper provides a new method to estimate ramie yield by integrating plant height and germplasm characteristics obtained by UAV-RGB system. The experiment was carried out in the ramie experimental area of Yunyuan base of Hunan Agricultural University in 2019, and the images of ramie in the seedling and mature stages were obtained by using a high-definition digital camera mounted on a drone. Firstly, Pix4D mapper was used to generate the digital surface model and ortho-image of ramie canopy in two growth periods. Based on the DSM, we used “difference method” to calculate the average plant height of the experimental plot. RGB channel mean value of experimental plot was extracted based on orthography, and then digital image variables and vegetation index were calculated. Then, the difference and diversity of spectral phenotypic characters and yield/plant height ratio characters among the germplasm of ramie were analyzed. Finally, stepwise regression method was used to establish the ramie yield prediction model, and correlation analysis was carried out for each yield interpretation factor. There was a significant correlation between DSM-based H and the measured plant height (r = 0.90), with RMSE of 0.04 for the linear model established based on the corrected plant height and the measured plant height. Plant height information was significantly correlated with yield (r = 0.91), while spectral phenotype information was not significantly correlated with yield. The ramie yield prediction model established by the fusion of plant height and germplasm characteristics was highly accurate, with R2 of 0.85 and RMSE of 0.71. Therefore, this study has important practical significance for resource management and yield estimation of ramie germplasm.

Key words: UAV, ramie, remote sensing images, plant height, yield

图1

试验小区分布图"

图2

基于无人机遥感图像的株高测量原理"

表1

光谱植被指数及数码图像变量定义"

变量
Variable		 定义
Definition		 参考来源Source
R r=R/(R+G+B)
G g=G/(R+G+B)
B b=B/(R+G+B)
g/r g/r=g/r
g/b g/b=g/b
r/b r/b=r/b
GLA GLA=(2*G-R-B)/(2*G+R+B) [19]
ExR ExR=1.4R-G [15]
ExG ExG=2*G-R-B [15]
ExGR ExGR=ExG-1.4R-G [20]
WI (G-B)/(R-G) [20]

图3

苗期和成熟期DSM"

图4

DSM提取株高的精度分析"

图5

无人机遥感图像DSM测定株高模型的验证"

表2

苎麻种质资源差异与多样性分析"

性状
Trait		 r G b g/r g/b r/b rgbVI WI GLA a 平均值Mean
平均值Mean 0.31 0.43 0.25 1.39 1.72 1.23 0.05 -1.99 0.26 1.45
最小值Min. 0.29 0.42 0.21 1.30 1.49 1.03 0.049 -2.81 0.24 0.91
最大值Max. 0.34 0.45 0.28 1.51 2.05 1.58 0.05 -1.50 0.33 2.05
变异系数CV (%) 3.43 1.68 6.06 3.00 7.78 9.79 2.37 14.87 6.25 20.90 7.61
多样性指数H' 1.41 1.33 1.42 1.38 1.39 1.44 1.21 1.44 2.25 1.49 1.48

表3

苎麻产量与解释因子的相关系数"

产量Yield E DSM-based H WI ExGR ExR ExG GLA r g b g/r g/b r/b
产量Yield 1 0.91 0.61 0.32 -0.05 -0.19 -0.14 -0.21 -0.25 -0.27 0.34 0.12 -0.33 -0.30
E 1 0.57 0.29 0.02 -0.15 -0.21 -0.20 -0.23 -0.27 0.32 0.11 -0.31 -0.27
DSM-based H 1 0.25 0.08 -0.22 -0.06 0.00 -0.26 -0.06 0.24 0.22 -0.23 -0.26
WI 1 0.56 -0.84 -0.49 -0.17 -0.79 -0.28 0.77 0.64 -0.71 -0.80
ExGR 1 -0.64 -0.52 0.05 -0.44 0.27 0.22 0.54 -0.13 -0.32
ExR 1 0.09 -0.24 0.81 -0.23 -0.54 -0.88 0.40 0.66
ExG 1 0.66 0.25 0.63 -0.51 0.03 0.58 0.43
GLA 1 0.03 0.75 -0.39 0.30 0.51 0.26
r 1 0.14 -0.87 -0.90 0.77 0.95
g 1 -0.60 0.30 0.74 0.43
b 1 0.58 -0.98 -0.98
g/r 1 -0.42 -0.72
g/b 1 0.93
r/b 1

图6

苎麻产量估测模型的检验"

表4

后向逐步回归构建产量估测模型的过程及结果分析"

自变量个数
Number of independent variables		 变量组成
Parameter		 R2 RMSE
13 DSM-based H, E, WI, ExGR, ExR, ExG, GLA, r, g, b, g/r, g/b, r/b 0.88 0.30
8 DSM-based H, E, ExGR, ExR, r, b, g/r, r/b 0.88 0.28
6 DSM-based H, E, ExGR, r, ExR, b 0.88 0.28
5 DSM-based H, E, ExGR, ExR, r, 0.87 0.28
3 DSM-based H***, E***, ExGR* 0.86 0.28
2 DSM-based H**, E*** 0.84 0.30
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