Precision Agriculture

, Volume 13, Issue 3, pp 351–369 | Cite as

In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture

  • R. R. Pullanagari
  • I. J. YuleEmail author
  • M. P. Tuohy
  • M. J. Hedley
  • R. A. Dynes
  • W. M. King


A study was conducted to explore the potential use of a hand-held (proximal) hyperspectral sensor equipped with a canopy pasture probe to assess a number of pasture quality parameters: crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, dietary cation–anion difference (DCAD), lignin, lipid, metabolisable energy (ME) and organic matter digestibility (OMD) during the autumn season 2009. Partial least squares regression was used to develop a relationship between each of these pasture quality parameters and spectral reflectance acquired in the 500–2 400 nm range. Overall, satisfactory results were produced with high coefficients of determination (R 2), Nash–Sutcliffe efficiency (NSE) and ratio prediction to deviation (RPD). High accuracy (low root mean square error-RMSE values) for pasture quality parameters such as CP, ADF, NDF, ash, DCAD, lignin, ME and OMD was achieved; although lipid was poorly predicted. These results suggest that in situ canopy reflectance can be used to predict the pasture quality in a timely fashion so as to assist farmers in their decision making.


Hyperspectral proximal sensing Pasture quality Partial least squares regression (PLSR) 



The authors are grateful for the technical support received from: Michael Killick, Massey University, Palmerston North, New Zealand; Grant Rennie, AgResearch, Ruakura, Hamilton, New Zealand; Brian DeVantier, AgResearch Grasslands, Palmerston North, New Zealand; Staff from AgResearch, Lincoln, New Zealand and comments on the manuscript from Carolyn Hedley and Cathe Goulter.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • R. R. Pullanagari
    • 1
  • I. J. Yule
    • 1
    Email author
  • M. P. Tuohy
    • 1
  • M. J. Hedley
    • 2
  • R. A. Dynes
    • 3
  • W. M. King
    • 4
  1. 1.New Zealand Centre for Precision Agriculture (NZCPA), Institute of Natural ResourcesMassey UniversityPalmerston NorthNew Zealand
  2. 2.Institute of Natural ResourcesMassey UniversityPalmerston NorthNew Zealand
  3. 3.AgResearch, Lincoln Research CentreChristchurchNew Zealand
  4. 4.AgResearch, Ruakura Research CentreHamiltonNew Zealand

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