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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
[1] Becker-Reshef I, Vermote E, Lindeman M, Justice C. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens Environ, 2010,114:1312-1323.
[2] Michael S, Antje G, Franziska G, Michael P. Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery. Remote Sens, 2016,8:706.
[3] Zhang J, Yang C H, Song H B, Zhang G Z. Evaluation of an airborne remote sensing platform consisting of two consumer-grade cameras for crop identification. Remote Sens, 2016,8:257.
[4] 李明, 黄愉淇, 李绪孟, 彭冬星, 谢景鑫. 基于无人机遥感图像的水稻种植信息提取. 农业工程学报, 2018,34(4):108-111.
Li M, Huang Y Q, Li X M, Peng D X, Xie J X. Extraction of rice planting information based on remote sensing image from UAV. Trans CSAE, 2018,34(4):108-111 (in Chinese with English abstract).
[5] Juliane B, Andreas B, Georg B. UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability. Photogr Fernerk Geoinf, 2013,6:551-562.
[6] 杨琦, 叶豪, 黄凯, 查元源, 史良胜. 利用无人机图像构建作物表面模型估测甘蔗LAI. 农业工程学报, 2017,33(8):112-119.
Yang Q, Ye H, Huang K, Zha Y Y, Shi L S. Estimation of leaf area index of sugarcane using crop surface model based on UAV image. Trans CSAE, 2017,33(8):112-119 (in Chinese with English abstract).
[7] Malambo L, Popescu S C, Murray S C. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. Int J Appl Earth Observ Geoinf, 2018,64:31-42.
[8] Hu P C, Chapman S, Chapman S C, Wang X M, Andries P, Duan T, David J, Guo Y, Zheng B Y. Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: example for sorghum breeding. Eur J Agron, 2018,95:24-32.
[9] Juliane B, Andreas B, Simon B, Janis B, Silas E, Georg B. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sens, 2014,6:10395-10412.
[10] Juliane B, Andreas B, Georg B. Introducing a low-cost mini-UAV for thermal-and multispectral-imaging. Remote Sens Spatial Inf Sci, 2012, XXXIX-B1:345-349.
[11] Gašparović M, Seletković A, Alen B, Ivan B. The evaluation of Photogrammetry-based DSM from low-cost UAV by lidar-based DSM. South-East Eur For, 2017,8:117-125.
[12] 田明璐, 班松涛, 常庆瑞, 罗丹, 王力, 王烁. 基于低空无人机成像光谱仪图像估算棉花叶面积指数. 农业工程学报, 2016,32(21):102-108.
Tian M L, Ban S T, Chang Q R, Luo D, Wang L, Wang S. Use of hyperspectral images from UAV-based imaging spectrora diometer to estimate cotton leaf area index. Trans CSAE, 2016,32(21):102-108 (in Chinese with English abstract).
[13] 陈仲新, 任建强, 唐华俊, 史云, 冷佩, 刘佳, 王利民, 吴文斌, 姚艳敏, 哈斯图亚. 农业遥感研究应用进展与展望. 遥感学报, 2016,20:748-767.
Chen Z X, Ren J G, Tang H J, Shi Y, Leng P, Liu J, Wang L M, Wu W B, Yao Y M, Hasituya. Progress and perspectives on agricultural remote sensing research and applications. J Remote Sens, 2016,20:748-767 (in Chinese with English abstract).
[14] 李井会, 朱丽丽, 宋述尧. 数字图像技术在马铃薯氮素营养诊断中的应用. 安徽农业科学, 2012,40:3303-3305.
Li J H, Zhu L L, Song S Y. Application of digital image technology in diagnosis of potato nitrogen nutrition. J Anhui Agric Sci, 2012,40:3303-3305 (in Chinese with English abstract).
[15] Hunt J E R, Cavigelli M, Daughtry C S T, James E, Charles L. Walthall. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agric, 2005,6:359-378.
[16] 李长春, 牛庆林, 杨贵军, 冯海宽, 刘建刚, 王艳杰. 基于无人机数码图像的大豆育种材料叶面积指数估测. 农业机械学报, 2017,48(8):147-158.
Li C C, Niu Q L, Yang G J, Feng H K, Liu J G, Wang Y J. Estimation of leaf area index of soybean breeding materials based on UAV digital images. Trans CSAM, 2017,48(8):147-158 (in Chinese with English abstract).
[17] Anjin C, Jinha J, Murilo M, Landivar J. Crop height monitoring with digital imagery from unmanned aerial system. Comp Electron Agric, 2017,141:232-237.
[18] 牛庆林, 冯海宽, 杨贵军. 基于无人机数码图像的玉米育种材料株高和LAI监测. 农业工程学报, 2018,34(5):73-81.
Niu Q L, Feng H K, Yang G J. Plant height and LAI monitoring of maize breeding materials based on UAV digital image. Trans CSAE, 2018,34(5):73-81 (in Chinese with English abstract).
[19] Watanabe K, Guo W, Arai K. High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front Plant Sci, 2017,8:421.
doi: 10.3389/fpls.2017.00421 pmid: 28400784
[20] Juliane B, Kang Y, Helge A, Andreas B, Simon B, Janis B, Martin L, Georg B. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int J Appl Earth Observ Geoinf, 2015,39:79-87.
doi: 10.1016/j.jag.2015.02.012
[21] Geipel J, Link J, Claupein W. Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system. Remote Sens, 2014,6:10335.
[22] Li W, Niu Z, Chen H Y, Dong L, Wu M Q, Zhao W. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecol Indicators, 2016,67:637-648.
doi: 10.1016/j.ecolind.2016.03.036
[23] Louhaichi M, Borman M M, Johnson D E. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto Int, 2001,16:65-70.
[24] Woebbecke D, Meyer G, Bargen K V, Mortensen D. Color indices for weed identification under various soil, residue, and lighting conditions. Am Soc Agric Biol Eng, 1995,38:259-269.
[25] Shannon C E, Weaver W. The Mathem Atical Theory of Communication. Urbana: University of Illinoispress, 1994. pp 3-14.
[26] Jay S, Rabatel G, Hadoux X, Moura D, Gorretta N. In-field crop row phenotyping from 3D modeling performed using structure from motion. Computers Electron Agric, 2015,110:70-77
doi: 10.1016/j.compag.2014.09.021
[27] 马稚昱, 清水浩, 辜松. 基于机器视觉的菊花生长自动无损监测技术. 农业工程学报, 2010,26(9):213-219.
Ma Z Y, Qing S H, Gu S. Non-destructive measurement system for plant growth information based on machine vision. Trans CSAE, 2010,26(9):203-209 (in Chinese with English abstract).
[28] Xu W, Daljit S, Sandeep M, Geoffrey M, Jesse P. Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies. Plant Methods, 2018,14:53.
doi: 10.1186/s13007-018-0324-5 pmid: 29997682
[29] Holman F, Riche A B, Michalski A, Castle M, Wooster M, Hawkesford M. High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens, 2016,8:1031.
doi: 10.3390/rs8121031
[30] Tonkin T N, Midgley N G. Ground-control networks for image based surface reconstruction: an investigation of optimum survey designs using UAV derived imagery and structure-from-motion photogrammetry. Remote Sens, 2016,8:786.
doi: 10.3390/rs8090786
[31] Munoz J D, Finley A O, Gehl R, Kravchenko S. Nonlinear hierarchical models for predicting cover crop biomass using normalized difference vegetation index. Remote Sens Environ, 2010,114:2833-2840.
doi: 10.1016/j.rse.2010.06.011
[32] Meyer G E, Neto J C. Verification of color vegetation indices for automated crop imaging applications. Comp Electron Agric, 2008,63:282-293.
doi: 10.1016/j.compag.2008.03.009
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