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Acta Agronomica Sinica ›› 2020, Vol. 46 ›› Issue (9): 1448-1455.doi: 10.3724/SP.J.1006.2020.04020

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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 Online:2020-09-12 Published:2020-04-26
  • Contact: Guo-Xian CUI,Xu-Meng LI E-mail:347180050@qq.com;627274845@qq.com;xm.li@hunau.edu.cn
  • 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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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

Fig. 1

Locations of test plot"

Fig. 2

Principle of plant height measurement based on UAV remote sensing images"

Table 1

Definition of spectral vegetation index and digital image variables"

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]

Fig. 3

DSM at seeding and maturity stages"

Fig. 4

Precision analysis of DSM-based plant height"

Fig. 5

Verification of DSM-based plant height model"

Table 2

Analysis of ramie’s difference and diversity based on canopy color trait"

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

Table 3

Correlation coefficient between ramie yield and explanatory factors"

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

Fig. 6

Test of ramie yield estimation model"

Table 4

Process and result analysis of production estimation model constructed by backward stepwise regression"

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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