New method for cotton fractional vegetation cover extraction based on UAV RGB images

Authors

  • Huanbo Yang 1. School of Agricultural and Food Engineering, Shandong University of Technology, Zibo 255000, Shandong, China; 2. International Precision Agriculture Aviation Application Technology Research Center, Shandong University of Technology, Zibo 255000, Shandong, China
  • Yubin Lan 1. School of Agricultural and Food Engineering, Shandong University of Technology, Zibo 255000, Shandong, China; 2. International Precision Agriculture Aviation Application Technology Research Center, Shandong University of Technology, Zibo 255000, Shandong, China
  • Liqun Lu 2. International Precision Agriculture Aviation Application Technology Research Center, Shandong University of Technology, Zibo 255000, Shandong, China; 3. School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, Shandong, China
  • Daocai Gong 1. School of Agricultural and Food Engineering, Shandong University of Technology, Zibo 255000, Shandong, China; 2. International Precision Agriculture Aviation Application Technology Research Center, Shandong University of Technology, Zibo 255000, Shandong, China
  • Jianchi Miao 1. School of Agricultural and Food Engineering, Shandong University of Technology, Zibo 255000, Shandong, China; 2. International Precision Agriculture Aviation Application Technology Research Center, Shandong University of Technology, Zibo 255000, Shandong, China
  • Jing Zhao 1. School of Agricultural and Food Engineering, Shandong University of Technology, Zibo 255000, Shandong, China; 2. International Precision Agriculture Aviation Application Technology Research Center, Shandong University of Technology, Zibo 255000, Shandong, China

DOI:

https://doi.org/10.25165/ijabe.v15i4.6207

Keywords:

cotton, UAV, visible light images, fractional vegetation cover, vegetation index threshold method, TRVI, TBVI

Abstract

As the key principle of precision farming, the distribution of fractional vegetation cover is the basis of crop management within the field serves. To estimate crop FVC rapidly at the farm scale, high temporal-spatial resolution imagery obtained by unmanned aerial vehicle (UAV) was adopted. To verify the application potential of consumer-grade UAV RGB imagery in estimated FVC, blue-green characteristic vegetation index (TBVI) and red-green vegetation index (TRVI) were proposed in this study according to the differences of the gray value among cotton vegetation, soil and shadow in the field. First, two new constructed indices and several published indices were used to extract visible light images and generate greyscale images for each of the visible light vegetation indices. Then, the thresholds of cotton vegetation and non-vegetation pixels were established based on the vegetation index threshold method which combines support vector machine classification and vegetation index. Finally, the accuracy difference in vegetation information extraction between the newly constructed and several published indices was compared. The results show that the accuracy of the information extracted by TRVI is higher than that of subdivision index of other visible light (FVC extraction precision in the first bud stage of cotton: R2=0.832, RMSE=2.307, nRMSE=4.405%; FVC extraction precision in the bud stage of cotton: R2=0.981, RMSE=1.393, nRMSE=1.984%; FVC extraction precision in the flowering stage of cotton: R2=0.893, RMSE=2.101, nRMSE=2.422%; FVC extraction precision in the boll stage of cotton: R2=0.958, RMSE=1.850, nRMSE=2.050%). Keywords: cotton, UAV, visible light images, fractional vegetation cover, vegetation index threshold method, TRVI, TBVI DOI: 10.25165/j.ijabe.20221504.6207 Citation: Yang H B, Lan Y B, Lu L Q, Gong D C, Miao J C, Zhao J. New method for cotton fractional vegetation cover extraction based on UAV RGB images. Int J Agric & Biol Eng, 2022; 15(4): 172–180.

References

Zhang L Z, Spiertz J H J, Zhang S P, Li B, Van der Werf W. Nitrogen economy in relay intercropping systems of wheat and cotton. Plant and Soil, 2008; 303(1): 55–68.

Yoshino M. Agroclimatological problems in the Taklimakan Desert and its surrounding area in NW China. Journal of Agricultural Meteorology, 2005; 61(1): 1–14.

Purevdorj T S, Tateishi R, Ishiyama T, Honda Y. Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing, 1998; 19(18): 3519–3535.

Zhang X, Liao C, Li J, Sun Q. Fractional vegetation cover estimation in arid and semi-arid environments using HJ-1 satellite hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, 2013; 21: 506–512.

Wang X, Jia K, Liang S, Zhang Y. Fractional vegetation cover estimation method through dynamic Bayesian network combining radiative transfer model and crop growth model. IEEE Transactions on Geoscience and Remote Sensing, 2016; 54(12): 7442–7450.

Wang X, Jia K, Liang S, Li Q, Wei X, Yao Y, Tu Y. Estimating fractional vegetation cover from landsat-7 ETM+ reflectance data based on a coupled radiative transfer and crop growth model. IEEE Transactions on Geoscience and Remote Sensing, 2017; 55(10): 5539–5546.

Zhang D, Mansaray L R, Jin H, Sun H, Kuang Z, Huang J. A universal estimation model of fractional vegetation cover for different crops based on time series digital photographs. Computers and Electronics in Agriculture, 2018; 151: 93–103.

Li Y, Xu J, Xia R, Wang X, Xie W. A two-stage framework of target detection in high-resolution hyperspectral images. Signal, Image and Video Processing, 2019; 13(7): 1339–1346.

Zhang F, Kung H-T, Johnson V C. Assessment of land-cover/land-use change and landscape patterns in the two national nature reserves of Ebinur Lake Watershed, Xinjiang, China. Sustainability, MDPI, 2017; 9(5): 724. doi: 10.3390/su9050724.

Zhao J, Yang H B, Lan Y B, Lu L Q, Jia P, Li Z M. Extraction method of summer corn vegetation coverage based on visible light image of unmanned aerial vehicle. Journal of Agricultural Machinery, 2019; 50(5): 232–240.

Yang D J, Zhao J, Lan Y B, Wen Y T, Pan F J, Cao D L, et al. Research on farmland crop classification based on UAV multispectral remote sensing images. Int J Precis Agric Aviat, 2021; 4(1): 29–35.

Santoso F, Redmond S J. Indoor location-aware medical systems for smart homecare and telehealth monitoring: state-of-the-art. Physiological measurement, 2015; 36(10): R53–R87.

Zhang X, Zhang F, Qi Y, Deng L, Wang X, Yang S. New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV). International Journal of Applied Earth Observation and Geoinformation, 2019; 78: 215–226.

Yang H B, Zhao J, Lan Y B, Lu L Q, Li Z M. Fraction vegetation cover extraction of winter wheat based on RGB image obtained by UAV. International Journal of Precision Agricultural Aviation, 2019; 2(2): 54–61.

Dorji P, Fearns P. Impact of the spatial resolution of satellite remote sensing sensors in the quantification of total suspended sediment concentration: A case study in turbid waters of Northern Western Australia. Plos One, 2017; 12(4): e0175042. doi: 10.1371/journal.pone.0175042.

Hu J, Yang J. Application of distributed auction to multi-UAV task assignment in agriculture. International Journal of Precision Agricultural Aviation, 2018; 1(1): 44–50.

Choi S K, Lee S K, Jung S H, Choi J W, Choi D Y, Chun S J. Estimation of fractional vegetation cover in sand dunes using multi-spectral images from fixed-wing UAV. Journal of the Korean Society of Surveying Geodesy Photogrammetry & Cartography, 2016; 34(4): 431–441.

Yang H B, Hu X, Zhao J, Hu Y H. Feature extraction of cotton plant height based on DSM difference method. International Journal of Precision Agricultural Aviation, 2021; 4(1): 59–69.

Li B, Liu R, Liu S, Liu Q, Liu F, Zhou G. Monitoring vegetation coverage variation of winter wheat by low-altitude UAV remote sensing system. Transactions of the CSAE, 2012; 28(13): 160–165. (in Chinese)

Du X Y, Wan L, Cen H Y, Chen S B, Zhu J P, Wang H Y, et al. Multi-temporal monitoring of leaf area index of rice under different nitrogen treatments using UAV images. International Journal of Precision Agricultural Aviation, 2018; 1(1): 11–18

Xu W, Lan Y, Li Y, Luo Y, He Z. Classification method of cultivated land based on UAV visible light remote sensing. International Journal of Agricultural and Biological Engineering, 2019; 12(3): 103–109.

Xiang H, Tian L. Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosystems Engineering, 2011; 108(2): 174–190.

Tian Z, Fu Y, Liu S. Rapid crops classification based on UAV low-altitude remote sensing. Transactions of the CSAE, 2013; 29(7): 109–116. (in Chinese)

Jones K J. High-scale discontinuity detection applied to Landsat images. In Wavelet Applications VI. International Society for Optics and Photonics, 1999; 3723: 114–121.

Louhaichi M, Borman M M, Johnson D E. Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 2001; 16(1): 65–70.

Meyer G E, Neto J C. Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, 2008; 63(2): 282–293.

Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 1979; 8(2): 127–150.

Michael, Schirrmann, Antje, et al. Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery. Remote Sensing, 2016, 8(9): 706. doi: 10.3390/rs8090706.

Tian Z, Fu Y, Liu S. Rapid crops classification based on UAV low-altitude remote sensing. Transactions of the CSAE, 2013; 29(7): 109–116. (in Chinese)

Rundquist B C. The influence of canopy green vegetation fraction on spectral measurements over native tallgrass prairie. Remote Sensing of Environment, 2002; 81(1): 129–135.

Song W, Mu X, Ruan G, Gao Z, Li L, Yan G. Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method. International Journal of Applied Earth Observation and Geoinformation, 2017; 58: 168–176.

Jia K, Li Y, Liang S, Wei X, Mu X, Yao Y. Fractional vegetation cover estimation based on soil and vegetation lines in a corn-dominated area. Geocarto International, 2017; 32(5): 531–540.

Zhou Q, Robson M. Automated rangeland vegetation cover and density estimation using ground digital images and a spectral-contextual classifier. International Journal of Remote Sensing, 2001; 22(17): 3457–3470.

Zhao F A, Zhang X M, Mu X D, Yao X Y, Zhou Y. Learning multi-modality features for scene classification of high-resolution remote sensing images. Journal of Computer and Communications, 2018; 6(11): 185–193.

Niu Y X, Zhang L Y, Han W T. A novel method for extracting green fractional vegetation cover from digital images. Journal of Vegetation Science, 2018; 23 (3): 406–418.

Feng Q L, Liu J T, Gong J H. UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sensing, 2015; 7(1): 1074–1094.

Downloads

Published

2022-09-04

How to Cite

Yang, H., Lan, Y., Lu, L., Gong, D., Miao, J., & Zhao, J. (2022). New method for cotton fractional vegetation cover extraction based on UAV RGB images. International Journal of Agricultural and Biological Engineering, 15(4), 172–180. https://doi.org/10.25165/ijabe.v15i4.6207

Issue

Vol. 15 No. 4 (2022): IJABE

Section

Information Technology, Sensors and Control Systems

License

IJABE is an international peer reviewed open access journal, adopting Creative Commons Copyright Notices as follows.


Authors who publish with this journal agree to the following terms:


  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).