Hostname: page-component-8448b6f56d-m8qmq Total loading time: 0 Render date: 2024-04-18T11:20:25.823Z Has data issue: false hasContentIssue false
  • English
  • Français

How remote sensing is offering complementing and diverging opportunities for precision agriculture users and researchers

Published online by Cambridge University Press:  01 June 2017

R. Jackson ,
R. C. Gaynor ,
A. Bentley ,
J. Hickey ,
I. Mackay  and
E. S. Ober
R. Jackson*
Affiliation:
NIAB, Huntingdon Road, Cambridge, CB3 0LEUK
R. C. Gaynor
Affiliation:
Roslin, University of Edinburgh, Easter Bush, EH25 9RG, UK
A. Bentley
Affiliation:
NIAB, Huntingdon Road, Cambridge, CB3 0LEUK
J. Hickey
Affiliation:
Roslin, University of Edinburgh, Easter Bush, EH25 9RG, UK
I. Mackay
Affiliation:
NIAB, Huntingdon Road, Cambridge, CB3 0LEUK
E. S. Ober
Affiliation:
NIAB, Huntingdon Road, Cambridge, CB3 0LEUK
*
†E-mail robert.jackson@niab.com
Get access

Abstract

Precision farming advances are providing opportunities in both production agriculture and agricultural research. For growers and agronomists, the benefits of identifying where crops are stressed, the location of weeds and estimating yields on a large scale are clear. Researchers, who have different needs, can benefit from a detailed focus on a specific characteristic, such as one disease (e.g. yellow rust). This paper will review how recent advances in technology are beginning to allow the development of specialised tools within research and agriculture and how current precision agriculture tools can be effective at measuring desirable traits.

Keywords

ResearchSensorsMultispectralHyperspectralThermal
Type
Satellite Applications
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 383 - 387
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andújar, D, Rueda-Ayala, V, Moreno, H, Rosell-Polo, JR, Escolá, A, Valero, C, Gerhards, R, Fernández-Quintanilla, C, Dorado, J and Griepentrog, H 2013. Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor. Sensors 13, 14662–14675.CrossRefGoogle ScholarPubMed
Casswell, L 2014. Soil mapping can cut costs and boost yields – Farmers Weekly. Farmers Weekley. Available at: http://www.fwi.co.uk/machinery/soil-mapping-can-cut-costs-and-boost-yields.htm [retrieved November 30, 2016].Google Scholar
Guijarro, M, Pajares, G, Riomoros, I, Herrera, PJ, Burgos-Artizzu, XP and Ribeiro, A 2011. Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture 75, 75–83.Google Scholar
Keyworth, S, Jarman, M and Medcalf, K 2009. Assessing the Extent and Severity of Erosion on the Upland Organic Soils of Scotland using Earth Observation: A GIFTSS Implementation Test: Final Report, Scottish Government, St. Andrew’s House, Regent Road, Edinburgh, UK.Google Scholar
Li, L, Zhang, Q and Huang, D 2014. A review of imaging techniques for plant phenotyping. Sensors 14, 20078–20111.Google Scholar
Mackay, I, Ober, E and Hickey, J 2015. GplusE: beyond genomic selection. Food and energy security 4, 25–35.CrossRefGoogle ScholarPubMed
Mackay, I, Bansept-Basler, P, Barber, T, Bentley, A, Cockram, J, Gosman, N, Greenland, A, Horsnell, R, Howells, R, O’Sullivan, D, Rose, G and Howell, P 2014. An eight-parent multiparent advanced generation inter-cross population for winter-sown wheat. creation, properties, and validation G3 (4), 1603–1610.Google Scholar
Mahlein, AK 2016. Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. Plant Disease 100, 241–251.Google Scholar
Marshall, M, Thenkabail, P, Biggs, T and Post, K 2016. Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation). Agricultural and Forest Meteorology 218–219, 122–134.Google Scholar
NASA 2016. Airborne Visible/Infrared Imaging Spectrometer. Available at: http://aviris.jpl.nasa.gov/ [retrieved December 1, 2016].Google Scholar
Pluto-Kossakowska, J, Osińska-Skotak, K, Fijałkowska, A and Chmiel, J 2013. Use of remote sensing in control of good agricultural and environmental conditions on agricultural farms. Ecological Questions 17, 75.Google Scholar
Richards, J 1999. Remote Sensing Digital Image Analysis – An Introduction J Richards, ed., Springer.Google Scholar
Rose, DC, Sutherland, WJ, Parker, C, Lobley, M, Winter, M, Morris, C, Twining, S, Ffoulkes, C, Amano, T and Dicks, LV 2016. Decision support tools for agriculture: Towards effective design and delivery. Agricultural Systems 149, 165–174.CrossRefGoogle Scholar
Sahoo, RN, Ray, SS and Manjunath, KR 2015. Hyperspectral remote sensing of agriculture. Current Science 108, 848–859.Google Scholar
Schimmelpfennig, D and Ebel, R 2016. Sequential Adoption and Cost Savings from Precision Agriculture. Journal of Agricultural and Resource Economics 41, 97–115.Google Scholar
Torres-Sánchez, J, López-Granados, F and Peña, JM 2015. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture 114, 43–52.CrossRefGoogle Scholar
USGS 2016. What are the band designations for the Landsat satellites? Available at: http://landsat.usgs.gov/band_designations_landsat_satellites.php [retrieved December 1, 2016].Google Scholar
Zhang, L and Grift, TE 2012. A LIDAR-based crop height measurement system for Miscanthus giganteus. Computers and Electronics in Agriculture 85, 70–76.Google Scholar