Back
 AS  Vol.11 No.4 , April 2020
Winter Wheat Crop Height Estimation Using Small Unmanned Aerial System (sUAS)
Abstract: Deploying the small Unmanned Aerial System (sUAS) for data collection of high-resolution images is a big potential in determining crop physiological parameters. The advantage of using sUAS technology is the ability to acquire a high-resolution orthophoto and a 3D Model which is highly suitable for plant height monitoring. Plant height estimation has a big impact in the growth and development of wheat because it is essential for obtaining biomass, which is a factor for higher crop yield. Plant height is an indicator of high yield estimation and it correlates to biomass, nitrogen content, and other plant growth parameters. The study is aimed to determine an accurate height of wheat using the sUAS generated Digital Surface Model (DSM). A high-resolution imagery between 1.0 - 1.2 cm/pixel was obtained from a 35 m altitude with area coverage of 1.01 hectares. The DSM and orthophoto were generated from the sUAS, and the computed wheat heights were derived from the difference of Digital Elevation Model (DEM) and DSM data. Field measurement using steel tape was done for ground truth. The sUAS-based wheat height data were evaluated using the ground truth of 66 wheat-rows by applying correlation and linear regression analysis. Datasets were collected from three different flight campaigns (March 2018-May 2018). The sUAS-based wheat height data were significantly correlated, obtaining the result of R2 = 0.988, R2 = 0.996 and R2 = 0.944 for the month of March, April and May 2018 respectively. The significance of linear regression results was also validated by computing for the p-value. The p-value results were 0.00064, 0.0000824 and 0.0058 respectively. The main concern is the lodging of winter wheat, especially during the month of April which affects the recording of the plant’s height. Because some of the wheat plants are now lying on the ground, so measurements are done vertically. Nonetheless, the results showed that sUAS technology is highly suitable for many agricultural applications.
Cite this paper: Villareal, M. , Tongco, A. and Maja, J. (2020) Winter Wheat Crop Height Estimation Using Small Unmanned Aerial System (sUAS). Agricultural Sciences, 11, 355-368. doi: 10.4236/as.2020.114021.
References

[1]   Breiman, A. and Graur, D. (2013) Wheat Evolution. Israel Journal of Plant Sciences, 43, 85-98.
https://doi.org/10.1080/07929978.1995.10676595

[2]   Lee, D.S. and Shan, J. (2003) Combining Lidar Elevation Data and Ikonos Multispectral Imagery for Coastal Classification Mapping. Marine Geodesy, 26, 117-127.
https://doi.org/10.1080/01490410306707

[3]   Bork, E. and Su, J. (2007) Integrating LIDAR Data and Multispectral Imagery for Enhanced Classification of Rangeland Vegetation: A Meta-Analysis. Remote Sensing of Environment, 111, 11-24.
https://doi.org/10.1016/j.rse.2007.03.011

[4]   Geerling, G., Labrador-Garcia, M., Clevers, J.G.P., Ragas, A.M. and Smits, A.J. (2007) Classification of Floodplain Vegetation by Data Fusion of Spectral (CASI) and Lidar Data. International Journal of Remote Sensing, 28, 4263-4284.
https://doi.org/10.1080/01431160701241720

[5]   Kempeneers, P., Deronde, B., Provoost, S. and Houthuys, R. (2009) Synergy of Airborne Digital Camera and Lidar Data to Map Coastal Dune Vegetation. Journal of Coastal Research, 53, 73-82.
https://doi.org/10.2112/SI53-009.1

[6]   De Souza, C.H.W., Lamparelli, R.A.C., Rochaa, J.V. and Magalhães, P.S.G. (2017) Height Estimation of Sugarcane Using an Unmanned Aerial System (UAS) Based on Structure from Motion (SfM) Point Clouds. International Journal of Remote Sensing, 38, 2218-2230.
https://doi.org/10.1080/01431161.2017.1285082

[7]   Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M.L. and Bareth, G. (2015) Combining UAV-Based Plant Height from Crop Surface Models, Visible, and near Infrared Vegetation Indices for Biomass Monitoring in Barley. International Journal of Applied Earth Observation & Geoinformation, 39, 79-87.
https://doi.org/10.1016/j.jag.2015.02.012

[8]   Li, W., et al. (2016) Remote Estimation of Canopy Height and Aboveground Biomass of Maize Using High-Resolution Stereo Images from a Low-Cost Unmanned Aerial Vehicle System. Ecological Indicators, 67, 637-648.
https://doi.org/10.1016/j.ecolind.2016.03.036

[9]   Houghton, A., Hall, F. and Goetz, J. (2009) Importance of Biomass in the Global Carbon Cycle. Journal of Geophysical Research: Biogeosciences, 114, 1-13.
https://doi.org/10.1029/2009JG000935

[10]   Badhwar, D. and Macdonald, B. (1986) Satellite-Derived Leaf-Area-Index and Vegetation Maps as Input to Global Carbon Cycle Models—A Hierarchical Approach. International Journal of Remote Sensing, 7, 265-281.
https://doi.org/10.1080/01431168608954680

[11]   Liu, R., Chen, M., Liu, J., Deng, F. and Sun, R. (2007) Application of a New Leaf Area Index Algorithm to China’s Landmass Using MODIS Data for Carbon Cycle Research. Journal of Environmental Management, 85, 649-658.
https://doi.org/10.1016/j.jenvman.2006.04.023

[12]   Anthony, D., Elbaum, S., Lorenz, A. and Detweiler, C. (2014) On Crop Height Estimation with UAVs. IROS 2014 Conference Digest, IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, 14-18 September 2014, 4805-4812.
https://doi.org/10.1109/IROS.2014.6943245

[13]   Selkowitz, D.J., Green, G., Peterson, B. and Wylie, B. (2012) A Multi-Sensor Lidar, Multi-Spectral and Multi-Angular Approach for Mapping Canopy Height in Boreal Forest Regions. Remote Sensing of Environment, 121, 458-471.
https://doi.org/10.1016/j.rse.2012.02.020

[14]   Ehlert, D., Adamek, R. and Horn, H.-J. (2009) Laser Rangefinder-Based Measuring of Crop Biomass under Field Conditions. Precision Agriculture, 10, 395-408.
https://doi.org/10.1007/s11119-009-9114-4

[15]   Lati, R.N., Filin, S. and Eizenberg, H. (2013) Estimating Plant Growth Parameters Using an Energy Minimization-Based Stereovision Model. Computers and Electronics in Agriculture, 98, 260-271.
https://doi.org/10.1016/j.compag.2013.07.012

[16]   Martin, K.L., Anderson, R.H., Arnall, D.B., Brixcy, K.D., Casillas, M.A., Chung, B., Dobey, B.C., Kamenidou, S.K., Kariuki, S.K., Katsalirou, E.E., Morris, J.C., Moss, J.Q., Rohla, C.T., Sudbury, B.J., Tubana, B.S. and Raun, W.R. (2005) Mid-Season Prediction of Wheat-Grain Yield Potential Using Plant, Soil, and Sensor Measurements. Journal of Plant Nutrition, 29, 873-897.
https://doi.org/10.1080/01904160600649187

[17]   Berry, P.M., Sterling, M., Baker, C.J., Spink, J. and Sparkes, D.L. (2003) A Calibrated Model of Wheat Lodging Compared with Field Measurements. Agricultural and Forest Meteorology, 119, 167-180.
https://doi.org/10.1016/S0168-1923(03)00139-4

[18]   Chapman, S., Merz, T., Chan, A., Jackway, P., Hrabar, S., Dreccer, M., Holland, E., Zheng, B., Ling, T. and Jimenez-Berni, J. (2014) Pheno-Copter: A Low-Altitude, Autonomous Remote-Sensing Robotic Helicopter for High-Throughput Field-Based Phenotyping. Agronomy, 4, 279-301.
https://doi.org/10.3390/agronomy4020279

[19]   Gevaert, C.M., Suomalainen, J., Tang, J. and Kooistra, L. (2015) Generation of Spectral-Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 3140-3146.
https://doi.org/10.1109/JSTARS.2015.2406339

[20]   Rasmussen, J., Nielsen, J., Garcia-Ruiz, F., Christensen, S. and Streibig, C. (2013) Potential Uses of Small Unmanned Aircraft Systems (UAS) in Weed Research. Weed Research, 53, 242-248.
https://doi.org/10.1111/wre.12026

[21]   Zhang, C. and Kovacs, J.M. (2012) The Applications of Small Unmanned Aerial Systems for Precision Agriculture: A Review. Precision Agriculture, 13, 693-712.
https://doi.org/10.1007/s11119-012-9274-5

[22]   Lopez-Granados, F., Torres-Sanchez, J., Serr no-Perez, A., de Castro, A., Mesas-Carrascosa, F. and Pena, J. (2016) Early Season Weed Mapping in Sunflower Using UAV Technology: Variability of Herbicide Treatment Maps against Weed Thresholds. Precision Agriculture, 17, 183-199.
https://doi.org/10.1007/s11119-015-9415-8

[23]   Honkavaara, E., Saari, H., Kaivosoja, J., Polonen, I., Hakala, T. and Litkey, P. (2013) Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture. Remote Sensing, 5, 5006-5039.
https://doi.org/10.3390/rs5105006

[24]   Li, X., Lee, W.S., Li, M., Ehsani, R., Mishra, A.R. and Yang, C. (2012) Spectral Difference Analysis and Airborne Imaging Classification for Citrus Greening Infected Trees. Computers and Electronics in Agriculture, 83, 32-46.
https://doi.org/10.1016/j.compag.2012.01.010

[25]   Berni, J.A.J., Zarco-Tejada, P.J., Suarez, L. and Fereres, E. (2009) Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring from an Unmanned Aerial Vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47, 722-738.
https://doi.org/10.1109/TGRS.2008.2010457

[26]   Bendig, J.V. (2015) Unmanned Aerial Vehicles (UAVs) for Multi-Temporal Crop Surface Modelling. A New Method for Plant Height and Biomass Estimation Based on RGB-Imaging. PhD Thesis, Universität zu Köln.

[27]   Li, W., Niu, Z., Chen, H., Li, D., Wu, M. and Zhao, W. (2016) Remote Estimation of Canopy Height and above Ground Biomass of Maize Using High-Resolution Stereo Images from a Low-Cost Unmanned Aerial Vehicle System. Ecological Indicators, 67, 637-648.
https://doi.org/10.1016/j.ecolind.2016.03.036

[28]   Sankaran, S., Khot, R., Espinoza, Z., Jarolmasjed, S., Sathuvalli, R., Vandemark, J., Miklas, N., Carter, H., Pumphrey, O. and Knowles, R. (2015) Low-Altitude, High-Resolution Aerial Imaging Systems for Row and Field Crop Phenotyping: A Review. European Journal of Agronomy, 70, 112-123.
https://doi.org/10.1016/j.eja.2015.07.004

[29]   Turner, D., Lucieer, A. and Watson, C. (2012) An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds. Remote Sensing, 4, 1392-1410.
https://doi.org/10.3390/rs4051392

[30]   Zarco-Tejada, P.J., Diaz-Varelaa, R., Angileria, V. and Loudjania, P. (2014) Tree Height Quantification Using Very High-Resolution Imagery Acquired from an Unmanned Aerial Vehicle (UAV) and Automatic 3D Photo-Reconstruction Methods. European Journal of Agronomy, 55, 89-99.
https://doi.org/10.1016/j.eja.2014.01.004

[31]   Abdul Salam, R., Osman, A. and Zawawi Talib, A. (2007) Underwater Image Enhancement Using an Integrated Colour Model. IAENG International Journal of Computer Science, 34, 2.

[32]   Passoni, D., Pinto, L. and Sona, G. (2014) Use of Unmanned Aerial Systems for Multispectral Survey and Tree Classification: A Test in a Park Area of Northern Italy. European Journal of Remote Sensing, 47, 251-269.
https://doi.org/10.5721/EuJRS20144716

[33]   Pinto, L., Pagliari, D., Passoni, D. and Gini, R. (2014) Experimental Analysis of Different Software Packages for Orientation and Digital Surface Modelling from UAV Images. Earth Science Informatics, 7, 97-107.
https://doi.org/10.1007/s12145-013-0142-2

[34]   Fritz, A., Kattenborn, T. and Koch, B. (2013) UAV-Based Photogrammetric Point Clouds—Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W2, Rostock, 4-6 September 2013, 141-146.
https://doi.org/10.5194/isprsarchives-XL-1-W2-141-2013

[35]   Peckham, R.J. and Jordan, G. (2007) Digital Terrain Modelling: Development and Applications in a Policy Support Environment. Springer, Berlin.
https://doi.org/10.1007/978-3-540-36731-4

[36]   Demir, N., Sönmez, N.K., Akar, T. and üna, S. (2018) Automated Measurement of Plant Height of Wheat Genotypes Using a DSM Derived from UAV Imagery. 2nd International Electronic Conference on Remote Sensing, Vol. 2, 350.
https://doi.org/10.3390/ecrs-2-05163

[37]   GIS Geography (2018) DEM, DSM & DTM Differences—A Look at Elevation Models in GIS.
https://gisgeography.com/dem-dsm-dtm-differences

 
 
Top