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On using the precise sensor

Published online by Cambridge University Press:  01 June 2017

E. Pérez-Fernández*
Affiliation:
The James Hutton Institute, Craigiebuckler, Aberdeen, AB158QH, Scotland
M. J. Aitkenhead
Affiliation:
The James Hutton Institute, Craigiebuckler, Aberdeen, AB158QH, Scotland
C. A. Shand
Affiliation:
The James Hutton Institute, Craigiebuckler, Aberdeen, AB158QH, Scotland
A. H. J. Robertson
Affiliation:
The James Hutton Institute, Craigiebuckler, Aberdeen, AB158QH, Scotland
*
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Abstract

In precision agriculture, the selection and use of appropriate sensors determine the type and quality of information that will feed decision-support models. A wide variety of sensors, spectral ranges, data collection and processing approaches are used, sometimes leading to confusion. Whether in transmission or reflectance mode, multispectral or hyperspectral, laboratory or field-based or even satellite-borne, in order to achieve meaningful and accurate measurements it is essential to have a clear understanding of which part of the electromagnetic spectrum the sensors relate to and how the corresponding radiation interacts with the substrate (e.g. soils, crops, livestock products). Sensors in the visible range (390-700 nm) use colour to identify certain properties of the substrate (e.g. chlorophyll and pigments in crops, organic matter contents in soil) and can be used to detect and quantify colour changes that could, in turn, be correlated with changes in those properties. Alternatively, radiation in the near (NIR, 750-2500 nm) and mid infrared (MIR, 2500-25 000 nm) interacts with the molecular bonds that constitute organic and inorganic matter and, therefore, sensors with detectors in these ranges provide different but interrelated information on the chemical composition of the substrate. Shorter wavelength radiation in the form of X-Rays (0.1-10 nm) induces fluorescence in the substrate and XRF sensors provide elemental atomic information that is highly applicable to the study of soils, sediments and fluids. At the James Hutton Institute, we have expertise in the use of all these types of sensors and are developing practical applications based on a thorough understanding of the processes involved. In this paper we provide an overview of the capabilities and applications of the different sensors used in precision agriculture, not only with a theoretical understanding, but also with an awareness of the practicalities involved.

Type
Crop Sensors and Sensing
Copyright
© The Animal Consortium 2017 

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References

Aitkenhead, MJ, Donnelly, D, Sutherland, L, Miller, DG, Coull, MC and Black, HIJ 2015. Predicting Scottish topsoil organic matter content from colour and environmental factors. European Journal of Soil Science 66, 112120.CrossRefGoogle Scholar
Aitkenhead, MJ, Donnelly, D, Coull, M and Black, H 2013. E-SMART: Environmental Sensing for Monitoring and Advising in Real-Time. IFIP Advances in Information and Communication Technology 413, 129142.Google Scholar
Aitkenhead, MJ, Coull, MC, Towers, W, Hudson, G and Black, HIJ 2012. Predicting soil chemical composition and other soil parameters from field observations using a neural network. Computers and Electronics in Agriculture 82, 108116.CrossRefGoogle Scholar
Barron, V and Torrent, J 1986. Use of the Kubelka-Munk theory to study the influence of iron oxides on soil colour. Journal of Soil Science 37 (4), 499510.Google Scholar
Bueno Guerra, MB, de Almeida, E, Carvalho, GGA, Souza, PF, Nunes, LC and Krug, FJ 2014. Comparison of analytical performance of benchtop and handheld energy dispersive X-ray fluorescence systems for the direct analysis of plant materials. Journal Analytical Atomic Spectrometry 29, 16671674.CrossRefGoogle Scholar
Galvão, LS and Vitorello, I 1998. Role of organic matter in obliterating the effects of iron on spectral reflectance and colour of Brazilian tropical soils. International Journal of Remote Sensing 19 (10), 19691979.Google Scholar
Klockenkamper, R and von Bohlen, A 2015. Total-Relection X-Ray Fluorescence Analysis and Related Methods. Wiley, Hoboken, NJ, USA.Google Scholar
Nocita, M, Stevens, A, Wesemael, B, van, Aitkenhead, M, Bachmann, M, Barthes, B et. al., 2015. Soil spectroscopy: an alternative to wet chemistry for soil monitoring. Advances in Agronomy 132, 139159.Google Scholar
Pérez-Fernández, E and Robertson, AHJ 2016. Global and local calibrations to predict chemical and physical properties of a national spatial dataset of Scottish soils from their near infrared spectra. Journal of Near Infrared Spectroscopy 24 (3), 305316.Google Scholar
Reidinger, S, Ramsey, MH and Harltey, SE 2012. Rapid and accurate analyses of silicon and phosphorus in plants using a portable X-ray fluorescence spectrometer. The New Phytologist 195 (3), 699706.Google Scholar
Robertson, AHJ, Shand, C and Pérez-Fernández, E 2016. The application of Fourier transform infrared, near infrared and X-ray fluorescence spectroscopy to soil analysis. Spectroscopy Europe 28 (4), 913.Google Scholar
Tsumuki, H, Kanehisa, K and Kawada, K 1989. Leaf surface wax as a possible resistance factor of barley to cereal aphids. Applied Entomology and Zoology 24, 295301.Google Scholar
Wold, S 1995. Chemometrics; what do we mean with it, and what do we want from it? Chemometrics and Intelligent Laboratory Systems 30, 109115.Google Scholar