Development and evaluation of low-altitude remote sensing systems for crop production management
Abstract
Keywords: low-altitude remote sensing, agricultural airplane, unmanned aerial vehicle (UAV), crop production management, precision agriculture
DOI: 10.3965/j.ijabe.20160904.2010
Citation: Huang Y, Thomson S J, Brand H J, Reddy K N. Development of low-altitude remote sensing systems for crop production management. Int J Agric & Biol Eng, 2016; 9(4): 1-11.
Keywords
Full Text:
PDFReferences
Stafford J V. Implementing precision agriculture in the 21st century. J. Agric. Eng. Res., 2000; 76(3): 267–275.
Zhang N, Wang M, Wang N. Precision agriculture-a worldwide overview. Comput. Electron. Agric., 2002; 36(2-3): 113–132.
Yao H, Huang Y. Remote sensing applications to precision farming. In: G. Wang and Q. Weng, editors, Remote sensing of natural resources. Chap 18. CRC Press, Boca Raton, FL. 2013. pp.333–352.
Mulla D J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst. Eng., 2013; 114(4): 358–371.
Pinter Jr P J, Hatfield J L, Schepers J S, Barnes E M, Moran M S, Daughtry C S T, et al. Remote sensing for crop management. Photogrammetric Eng. & Remote Sensing, 2003; 69(6): 647‐664.
Huang Y, Thomson S J. Remote sensing for cotton farming. In: Cotton, 2nd edition, Eds. D. D. Fang and R.G. Percy. American Society of Agronomy, Inc., Crop Science Society of America, and Soil Society of America, Inc. Madison, WI, USA, Agronomy Monograph, 2015.57: 439–464.
Yang C, Everitt J H, Bradford J N, Escobar D E. Mapping grain sorghum growth and yield variations using airborne multispectral digital imagery. Transactions of the ASAE, 2000; 43(6): 1927–1938.
Dabrowska-Zielinska K, Moran M S, Maas S J, Pinter Jr P J, Kimball B A, Mitchell T A, Clarke T A, Qi J. Demonstration of a remote sensing/modeling approach for irrigation scheduling and crop growth forecasting. J. Water and Land Devel, 2001; 5: 69–87.
Thomson S J, Hanks J E, Sassenrath-Cole G F. Continuous georeferencing for video-based remote sensing on agricultural aircraft. Transactions of the ASAE, 2002; 45(4): 1177–1189.
Thomson S J, Zimba P V, Bryson C T, Alarcon-Calderon V J. Potential for remote sensing from agricultural aircraft using digital video. Applied Engineering in Agriculture, 2005; 21(3): 531–537.
Thomson S J, Sullivan D G. Crop status monitoring using multispectral and thermal imaging systems for accessible aerial platforms. ASABE paper no. 061179; 2006.
Fletcher R S, Everitt J H. A six-camera digital video imaging system sensitive to visible, red edge, near-infrared, and mid-infrared wavelengths. Geocarto Intl., 2007; 22(2): 75–86.
Zhang H, Lan Y, Lacey R E, Hoffmann W C, Huang Y. Analysis of vegetation indices derived from aerial multispectral and ground hyperspectral data. Int. J. Agric. Biol. Eng., 2009; 2(3): 33–40.
Huang Y, Sui R, Thomson S J, Fisher D K. Estimation of cotton yield with varied irrigation and nitrogen treatments using aerial multipspectral imagery. Int J Agric & Biol Eng, 2013; 6(2): 37–41.
Piekarczyk J. Application of remote sensing in agriculture. Geoinformatica Polonica, 2014; 13(1): 69–75.
Huang Y, Lee M A, Thomson S J, Reddy K N. Ground-based hyperspectral remote sensing for weed management in crop production. Int J Agric & Biol Eng, 2016; 9(2): 98–109.
Thomson S J, Plamondon-Ouellet C M, Defauw S L, Huang Y, Fisher D K, English P J. Potential and challenges in use of thermal imaging for humid region irrigation system management. Journal of Agricultural Science, 2012; 4(4): 103–116.
Zhang J, Yang C, Song H, Hoffmann W C, Zhang D, Zhang G. Evaluation of an airborne remote sensing platform consisting of two consumer-grade cameras for crop identification. Remote Sensing, 2016; 8(3): 257.
Huang Y, Thomson S J, Lan Y, Maas S J. Multispectral imaging systems for airborne remote sensing to support agricultural production management. Int J Agric & Biol Eng, 2010; 3(1): 50–62.
Samseemoung G, Soni P, Jayasuriya H P W, Salokhe V M. Application of low altitude remote sensing (LARS) platform for monitoring crop growth and weed infestation in a soybean plantation. Precision Agric, 2012; 13(6): 611–627.
Zhang C, Walters D, Kovacs J M. Applications of low altitude remote sensing in agriculture upon farmers' requests - a case study in Northeastern Ontario, Canada. PLoS ONE. 2014; 9(11): e112894. doi: 10.1371/journal.pone.0112894.
Huang Y, Thomson S J, Hoffmann W C, Lan Y, Fritz B K. Development and prospect of unmanned aerial vehicles for agricultural production management. Int J Agric & Biol Eng, 2013; 6(3): 1–10.
Thomson S J, DeFauw S L, English P J, Hanks J E, Fisher D K, Foster P N, et al. Thermal characterization and spatial analysis of water stress in cotton (Gossypium hirsutum) and phytochemical composition related to water stress in soybean (Glycine max). Proceedings of the 9th International Conference on Precision Agriculture, Denver, CO. 2008; CD-ROM paper abstract_221.pdf.
Rouse J W, Haas R H, Schell J A, Deering D W. Monitoring vegetation systems in the great plain with ERTS. In: Proc. 3rd ERTS Symposium, NASA SP-351, Vol. 1, NASA, Washington, DC. 1973; pp. 309–317.
Cyr L, Bonn F, Pesant A. Vegetation indices derived from remote sensing for an estimation of soil protection against water erosion. Ecol. Model. 1995; 79(s1-3): 277–285.
Jackson R D, Huete A R. Interpreting vegetation indices. Prev. Vet. Med. 1991; 11(3-4): 185–200.
Gitelson A A, Kaufman Y J, Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ, 1996; 58(3): 289–298.
Warren G, Metternicht G. Agricultural applications of high-resolution digital multispectral imagery: evaluating within-field spatial variability of canola (Brassica napus) in Western Australia. Photogrammetric Engineering & Remote Sensing, 2005; 71(5): 595–602.
Copyright (c)