Assessment of in-season cotton nitrogen status and lint yield prediction from unmanned aerial system imagery

Remote Sensing

Volumn 9, Issue 11, 2017, Pages

  Ballester, Carlos ^a   Hornbuckle, John ^a   Brinkhoff, James ^a   Smith, John ^b   Quayle, Wendy ^a  

^a DEAKIN UNIVERSITY   (Australia)

^b NSW DEPARTMENT OF PRIMARY INDUSTRIES   (Australia)

Author keywords

Multi spectral imagery; Nitrogen uptake; Plant nitrogen content; Precision agriculture

Indexed keywords

CHLOROPHYLL; CRACKS; CROPS; FERTILIZERS; FORECASTING; INFRARED DEVICES; NITROGEN; SPECTROSCOPY; UNMANNED AERIAL VEHICLES (UAV); VEGETATION;

COTTON (GOSSYPIUM HIRSUTUM L.); MULTI-SPECTRAL IMAGERY; NITROGEN CONTENT; NITROGEN UPTAKE; NORMALIZED DIFFERENCE VEGETATION INDEX; PRECISION AGRICULTURE; SPATIAL AND TEMPORAL VARIABILITY; VISIBLE ATMOSPHERICALLY RESISTANT INDICES;

COTTON;

EID: 85034731862     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9111149     Document Type: Article

References (36)

1. Gerik, T.J.; Oosterhuis, D.M.; Torbert, H.A. Managing cotton nitrogen supply. Adv. Agron. 1998, 64, 115-147
2. World Fertilizer Trends and Outlook to 2018; FAO: Rome, Italy, 2015; p. 66
3. Ali, N. Review: Nitrogen utilization features in cotton crop. Am. J. Plant Sci. 2015, 6, 987-1002
4. Good, A.G.; Beatty, P.H. Fertilizing nature: A tragedy of excess in the commons. PLoS Biol. 2011, 9, e1001124
5. Snyder, C.S.; Davidson, E.A.; Smith, P.; Venterea, R.T. Agriculture: Sustainable crop and animal production to help mitigate nitrous oxide emissions. Curr. Opin. Environ. Sustain. 2014, 9-10, 46-54
6. Cottoninfo. Cottoninfo on-Farm Nitrogen Trials and N-Use Practices. Available online: https://www. cottoninfo.com.au/publications/cottoninfo-nitrogen-trials-report (accessed on 8 November 2017)
7. Zhou, G.; Yin, X. Assessing nitrogen nutritional status, biomass and yield of cotton with ndvi, spad and petiole sap nitrate concentration. Exp. Agric. 2017, 1-18
8. Mulla, D.J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng. 2013, 114, 358-371
9. Buscaglia, H.J.; Varco, J.J. Early detection of cotton leaf nitrogen status using leaf reflectance. J. Plant Nutr. 2002, 25, 2067
10. Tarpley, L.; Reddy, K.R.; Sassenrath-Cole, G.F. Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Sci. 2000, 40, 1814-1819
11. Read, J.J.; Tarpley, L.; McKinion, J.M.; Reddy, K.R. Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton. J. Environ. Qual. 2002, 31, 1442-1452
12. Raper, T.B.; Varco, J.J. Canopy-scale wavelength and vegetative index sensitivities to cotton growth parameters and nitrogen status. Precis. Agric. 2015, 16, 62-76
13. Yang, C.; Bradford, J.M.; Wiegand, C.L. Airborne multispectral imagery for mapping variable growing conditions and yields of cotton, grain sorghum, and corn. Trans. ASAE 2001, 44, 1983-1994
14. Raper, T.B.; Varco, J.J.; Hubbard, K.J. Canopy-based normalized difference vegetation index sensors for monitoring cotton nitrogen status. Agron. J. 2013, 105, 1345-1354
15. Gutierrez, M.; Norton, R.; Thorp, K.R.;Wang, G. Association of spectral reflectance indices with plant growth and lint yield in upland cotton. Crop Sci. 2012, 52, 849-857
16. Zarco-Tejada, P.J.; Ustin, S.L.; Whiting, M. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agron. J. 2005, 97, 641-653
17. Zhao, D.; Reddy, K.R.; Kakani, V.G.; Read, J.J.; Koti, S. Canopy reflectance in cotton for growth assessment and lint yield prediction. Eur. J. Agron. 2007, 26, 335-344
18. Zhang, C.; Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: A review. Precis. Agric. 2012, 13, 693-712
19. Salamí, E.; Barrado, C.; Pastor, E. Uav flight experiments applied to the remote sensing of vegetated areas. Remote Sens. 2014, 6, 11051-11081
20. Muharam, F.; Maas, S.; Bronson, K.; Delahunty, T. Estimating cotton nitrogen nutrition status using leaf greenness and ground cover information. Remote Sens. 2015, 7, 7007-7028
21. Maresma, á.; Ariza, M.; Martínez, E.; Lloveras, J.; Martínez-Casasnovas, J. Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (zea mays l.) from a standard uav service. Remote Sens. 2016, 8, 973
22. Caturegli, L.; Corniglia, M.; Gaetani, M.; Grossi, N.; Magni, S.; Migliazzi, M.; Angelini, L.; Mazzoncini, M.; Silvestri, N.; Fontanelli, M.; et al. Unmanned aerial vehicle to estimate nitrogen status of turfgrasses. PLoS ONE 2016, 11, e0158268
23. Hunt, E.R.; Horneck, D.A.; Spinelli, C.B.; Turner, R.W.; Bruce, A.E.; Gadler, D.J.; Brungardt, J.J.; Hamm, P.B. Monitoring nitrogen status of potatoes using small unmanned aerial vehicles. Precis. Agric. 2017, 1-20
24. Zhu, J.; Wang, K.; Deng, J.; Harmon, T. Quantifying nitrogen status of rice using low altitude uav-mounted system and object-oriented segmentation methodology. In Proceedings of the ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, San Diego, CA, USA, 30 August-2 September 2009; pp. 603-609
25. Isbell, R.F. The Australian Soil Classification; CSIRO: Collingwood, Australia, 2002; Volume 4
26. Grabham, M. Bankless Channel Irrigation Systems. In WATERpack-A Guide for Irrigation Management in Cotton and Grain Farming Systems, 3rd ed.; Cotton Research and Development Corporation (CRDC): Narrabri, Australia, 2012; pp. 388-391
27. Hunt, E.R.; Doraiswamy, P.C.; McMurtrey, J.E.; Daughtry, C.S.T.; Perry, E.M.; Akhmedov, B. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int. J. Appl. Earth Obs. GeoInf. 2013, 21, 103-112
28. Gitelson, A.A.; Kaufman, Y.J.; Stark, R.; Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 2002, 80, 76-87
29. 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 Earth Resources Technology Satellite-1 Symposium, Washington, DC, USA, 10-14 December 1973; Freden, S.C., Mercanti, E.P., Becker, M.A., Eds.; NASA: Washington, DC, USA, 1974; pp. 309-317
30. Gitelson, A.A.; Merzlyak, M.N. Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sens. 1997, 18, 2691-2697
31. Haboudane, D.; Miller, J.R.; Tremblay, N.; Zarco-Tejada, P.J.; Dextraze, L. Integration of hyperspectral vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 2002, 81, 416-426
32. Li, F.; Miao, Y.; Hennig, S.D.; Gnyp, M.L.; Chen, X.; Jia, L.; Bareth, G. Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precis. Agric. 2010, 11, 335-357
33. Yu, K.; Leufen, G.; Hunsche, M.; Noga, G.; Chen, X.; Bareth, G. Investigation of leaf diseases and estimation of chlorophyll concentration in seven barley varieties using fluorescence and hyperspectral indices. Remote Sens. 2014, 6, 64-86
34. Yu, K.; Li, F.; Gnyp, M.L.; Miao, Y.; Bareth, G.; Chen, X. Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the northeast china plain. ISPRS J. Photogramm. Remote Sens. 2013, 78, 102-115
35. Yang, C.; Greenberg, S.M.; Everitt, J.H.; Fernandez, C.J. Assessing cotton defoliation, regrowth control and root rot infection using remote sensing technology. Int. J. Agric. Biol. Eng. 2011, 4, 1-11
36. Rochester, I.J. Nutrient uptake and export from an australian cotton field. Nutr. Cycl. Agroecosyst. 2007, 77, 213-223