Welcome to Acta Agronomica Sinica，

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

• RESEARCH NOTES • Previous Articles     Next Articles

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)

RichHTML372

18

PDF

1170

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
[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
[1] Hu Chuan, Zhao Kai-Nan, Huang Xiu-Li, Wu Jin-Zhi, Ren Kai-Ming, Wang He-Zheng, Fu Guo-Zhan, Huang Ming, Li You-Jun. Effects of tillage methods and nitrogen rates on yield and quality of dryland wheat under one-off irrigation [J]. Acta Agronomica Sinica, 2026, 52(6): 1830-1846.
[2] Ma Sheng-Qian, Wang Zhi-Ping, Chen Hao-Tian, Dou Shu-Xian, Zhang Yan, Deng Ai-Xing, Zhang Wei-Jian, Yuan Xiang-Yang, Song Zhen-Wei. Effects of tillage methods and nitrogen application rate on maize yield and soil aggregates in northeastern China under straw returning [J]. Acta Agronomica Sinica, 2026, 52(6): 1802-1816.
[3] Tang Kuan-Qiang, Li Gong-Yun, Song Mei-Yi, Zhao Xue, Chang Chun-Ling. Genome-wide association analysis and prediction model construction for soybean plant height [J]. Acta Agronomica Sinica, 2026, 52(6): 1743-1756.
[4] Zhang Si-Si, Zhao Xiang-Hui, Zhou Yang, Yao Yun-Feng, Zhu Rong-Yu, Dong Yuan-Jie, Hu Guo-Qing, Xu Tong, Liu Zhao-Xin. Effects of plowing and green manure returning in winter fallow period on soil physicochemical properties and yield in continuously cropped peanut [J]. Acta Agronomica Sinica, 2026, 52(5): 1472-1486.
[5] Zhang Ning-Ning, Teng Yu-Fei, Ren Na-Na, Wei Xing-Zhuo, Yan Shu-Hao, Fan Ke-Xin, Wang Yong-Hong, Chen Wen-Kang, Zhang Xing-Hua, Zhu Wan-Chao, Xu Shu-Tu, Xue Ji-Quan. Phenotypic evaluation and plasticity analysis of drought resistance in 201 maize inbred lines [J]. Acta Agronomica Sinica, 2026, 52(5): 1309-1325.
[6] Wang Yu-Cheng, Zhang Lu, Liu A-Kang, Huang Jian-Liang, Peng Shao-Bing, Yuan Shen. Strategies and prospects for large-scale crop yield improvement based on yield gap [J]. Acta Agronomica Sinica, 2026, 52(5): 1279-1290.
[7] Zhao Jia-Xue, Zhou Long-Hao, Guo Qi-Yuan, Shang Lun-Xiao, Wang Han, Liu Zhi-Tao, Chen Xi, Zhang Xiao-Pei, Song Xian-Liang, Ahmedov Miraziz Baltaevich, Mao Li-Li. Long-term stubble return and subsoiling enhance cotton yields in coastal saline-alkali soils by improving soil conditions and photosynthetic characteristics [J]. Acta Agronomica Sinica, 2026, 52(5): 1548-1560.
[8] Guo Xing-Yu, Hu Dan, Lin Su-Qi, Wang Meng-Kai, Tan Wen-Feng, Huang Chuan-Qin. Biochar combined with chemical fertilizer increases maize yield and soil ecosystem multifunctionality in an intercropped maize-soybean [J]. Acta Agronomica Sinica, 2026, 52(5): 1536-1547.
[9] Zhang Zhen, Feng Lian-Jie, Shi Yu, Yu Zhen-Wen, Zhang Yong-Li. Yield formation of wheat with different ear types under water-saving supplementary irrigation conditions [J]. Acta Agronomica Sinica, 2026, 52(5): 1522-1535.
[10] Yan An, Jiang Kun-Wei, Wang Rong-Yuan, Tian Lin, Zhang Lu, Wang Yun, Xu Jian-Long. Identification and cloning of SVN7 controlling small vascular bundle number in the rice flag leaf [J]. Acta Agronomica Sinica, 2026, 52(5): 1364-1372.
[11] Liu Xin-Meng, Ren Hao, Zhang Ji-Bo, Zhang Ji-Wang, Zhao Bin, Ren Bai-Zhao, Liu Peng, Wang Hong-Zhang. Physiological mechanisms of methyl jasmonate (MeJA) alleviating the effects of heat stress on ear differentiation in maize [J]. Acta Agronomica Sinica, 2026, 52(5): 1561-1572.
[12] Wang Zhuang-Zhuang, Wu Zi-Jun, Zhang Yong-Xin, Zhang Xin-Yuan, Yuan Li-Xue, Chen Ru-Xue, Liu Shi-Ju, Duan Jian-Zhao, Feng Wei, Wang Tong-Chao, Wang Yong-Hua. Optimized water-nitrogen synergy enhances winter wheat yield and nitrogen use efficiency in clay-loam fluvo-aquic soils of southeastern Henan, China [J]. Acta Agronomica Sinica, 2026, 52(5): 1501-1521.
[13] Zhang Hong-Rong, Wang Fei-Er, Li Pan, Qiu Hai-Long, Zhu Jing, Zhao Lian-Hao, Nan Yun-You, He Wei, Fan Zhi-Long, Hu Fa-Long, Chai Qiang, Yin Wen. Photosynthetic characteristics of 20% reduced irrigation combined with 25% organic substitution for chemical fertilizer in increasing silage maize yield [J]. Acta Agronomica Sinica, 2026, 52(5): 1487-1500.
[14] Hou Si-Yu, Wang Guo-Cui, Wei Jin-Gui, Xie Wei-Xin, Yin Wen, Fan Zhi-Long, Chai Qiang, Hu Fa-Long. Effects of green manure combined with chemical nitrogen fertilizer on dry matter accumulation and yield formation of wheat in arid irrigation areas of northwestern China [J]. Acta Agronomica Sinica, 2026, 52(4): 1208-1219.
[15] Shang Yun-Qiu, Zhao Zhu, Chen Huan, Ding Yong-Gang, Qiao Yu-Qiang, Li Wei, Zhang Xiang-Qian, Cao Cheng-Fu, Du Shi-Zhou. Effects of long-term tillage practices on grain-filling and yield formation in rain-fed wheat [J]. Acta Agronomica Sinica, 2026, 52(4): 1236-1250.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd