Monitoring of wheat growth status and mapping of wheat yield's within-field spatial variations using color images acquired from UAV-camera System

Remote Sensing

Volumn 9, Issue 3, 2017, Pages

  Du, Mengmeng ^a   Noguchi, Noboru ^a  

^a HOKKAIDO UNIVERSITY   (Japan)

Author keywords

Agriculture remote sensing; Color vegetation index; Image processing; Precision agriculture; Unmanned aerial vehicle (UAV); Wheat yield; Winter wheat

Indexed keywords

AGRICULTURE; CAMERAS; COLOR; CROPS; FORESTRY; HARVESTING; IMAGE PROCESSING; MEAN SQUARE ERROR; REGRESSION ANALYSIS; REMOTE SENSING; STATISTICAL METHODS; UNMANNED AERIAL VEHICLES (UAV); VEGETATION; VEHICLES;

COEFFICIENT OF DETERMINATION; LEAVE-ONE-OUT CROSS-VALIDATION (LOOCV); PRECISION AGRICULTURE; ROOT MEAN SQUARE ERRORS; STEPWISE REGRESSION ANALYSIS; VEGETATION INDEX; WHEAT YIELD; WINTER WHEAT;

IMAGE ACQUISITION;

EID: 85019945266     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9030289     Document Type: Article

References (28)

1. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS. In Proceedings of the Third ERTS Symposium, NASA, Greenbelt, MD, USA, 10-14 December 1973; pp. 309-317. Available online: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19740022614.pdf (accessed on 10 December 2015).
2. Roberto, B., Paolo, R. On the use of NDVI profiles as a tool for agricultural statistics: The case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sens. Environ. 1993, 45, 311-326.
3. Basnyat, P., McConkey, B., Lafond, G.P., Moulin, A., Pelcat, Y. Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies. Can. J. Plant Sci. 2004, 84, 97-103.
4. David, J.M. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 2013, 114, 358-371.
5. Robert, N.C. Determining the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia 1956, 26, 223-286.
6. Thomas, J., Trout, L., Johnson, F., Jim, G. Remote Sensing of Canopy Cover in Horticultural Crops. Hortscience 2008, 43, 333-337.
7. Du, M.M., Noboru, N. Multi-temporal Monitoring of Wheat Growth through Correlation Analysis of Satellite Images, Unmanned Aerial Vehicle Images with Ground Variable. In Proceedings of the 5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL, Seattle, WA, USA, 14-17 August 2016.
8. Eisenbeiss, H. A Mini Unmanned Aerial Vehicle (UAV): System Overview and Image Acquisition. In Image Acquisition. In Proceedings of the International Workshop on Processing and Visualization Using High-Resolution Imagery, Pitsanulok, Thailand, 18-20 November 2004.
9. The Third National Agricultural Census Using UAV Remote Sensing. Available online: http://www.uavwrj. com/gne/427.html (accessed on 25 October 2016).
10. James, B.C., Randolph, H.W. Introduction to Remote Sensing, 5th ed., The Guilford Press: New York, NY, USA, 2011; pp. 72-102.
11. Wang, X.Q., Wang, M.M., Wang, S.Q., Wu, Y.D. Extraction of vegetation information from visible unmanned aerial vehicle images. Trans. Chin. Soc. Agric. Eng. 2015, 31, 152-159.
12. Feng, J.L., Liu, K., Zhu, Y.H., Li, Y., Liu, L., Meng, L. Application of unmanned aerial vehicles to mangrove resources monitoring. Trop. Geogr. 2015, 35, 35-42.
13. Raymond Hunt, E., Jr., Dean Hively,W., Fujikawa, S.J., McCarty, G.W. Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring. Remote Sens. 2010, 2, 290-305.
14. Rasmussen, J., Ntakos, G., Nielsen, J., Svensgaard, J., Poulsen, R.N., Christensen, S. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? Eur. J. Agron. 2016, 74, 75-92.
15. Torres-Sánchez, J., Peña, J.M., de Castro, A.I., López-Granados, F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput. Electron. Agric. 2014, 103, 104-113.
16. Woebbecke, D.M., Meyer, G.E., Von Bargen, K., Mortensen, D.A. Color Indices for Weed Identification under Various Soil, Residue and Lighting Conditions. Trans. ASAE 1995, 38, 259-269.
17. Cui, R.X., Liu, Y.D., Fu, J.D. Estimation of winter wheat biomass using visible spectral and BP based artificial neural networks. Spectrosc. Spectr. Anal. 2015, 35, 2596-2601.
18. Global Wheat Production from 1990/1991 to 2016/2017 (in Million Metric Tons). Available online: https://www.statista.com/statistics/267268/production-of-wheat-worldwide-since-1990/ (accessed on 20 October 2016).
19. Wu, Q.,Wang, C., Fang, J.J., Ji, J.W. Field monitoring of wheat seedling stage with hyperspectral imaging. Int. J. Agric. Biol. Eng. 2016, 9, 143-148.
20. Zhang, F.,Wu, B., Luo, Z.Winter wheat yield predicting for America using remote sensing data. J. Remote Sens. 2004, 8, 611-617.
21. Ministry of Agriculture, Forestry, and Fisheries. Available online: http://www.maff.go.jp/ (accessed on 10 November 2016).
22. Trimble SPS855 GNSS Modular Receiver. Available online: http://construction.trimble.com/sites/default/ files/literature-files/2016-07/SPS855-Data-Sheet-EN.pdf (accessed on 12 November 2015).
23. Weather Time-Memuro. Available online: https://weather.time-j.net/Stations/JP/memuro (accessed on 2 December 2016).
24. Júnior, O.A.D.C., Guimarães, R.F., Silva, N.C., Gillespie, A.R., Gomes, R.A.T., Silva, C.R., De Carvalho, A.P.F. Radiometric normalization of temporal images combining automatic detection of pseudo-invariant features from the distance and similarity spectral measures, density scatterplot analysis, and robust regression. Remote Sens. 2013, 5, 2763-2794.
25. Torres-Sanchez, J., Lopez-Granados, F., De Castro, A., Pena-Barragan, J.M. Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management. PLoS ONE 2013, 8, e58210.
26. Gamon, J.A., Surfus, J.S. Assessing leaf pigment content and activity with a reflectometer. New Phytol. 2013, 143, 105-117.
27. Michael, K., Dana, R. Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Neural Comput. 1999, 11, 1427-1453.
28. Feng, Q.S., Gao, X.H. Application of Excel in the Experiment Teaching of Leave-One-Out Cross Validation. Exp. Sci. Technol. 2015, 13, 49-51.