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In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
WIRELESS SENSOR NETWORKS FOR IN-SITU IMAGE VALIDATION 
FOR WATER AND NUTRIENT MANAGEMENT 
R. Devadas 3 ’*, S.D. Jones 3 , G. J. Fitzgerald 5 ,1. McCauley 0 , B.A. Matthews' 1 , E.M. Perry 5 , M. Watt c , J.G. Ferwerda 6 , A.Z. Kouzani 1 
3 School of Mathematical and Geospatial Sciences, Royal Melbourne Institute of Technology University, Melbourne, 
VIC 3001-(rakhesh.devadas, simon.jones)@rmit.edu.au 
5 Grains Innovation Park, DPI Victoria, Horsham, VIC 3401, Australia^ Glenn.Fitzgerald, eileen.perry) 
@dpi.vic.gov.au 
c Future Farming Systems Research, DPI Victoria, Attwood 3049, Australia. (Ian.McCauley, 
Michelle.Watt)@dpi. vic.gov.au 
d Knowledge Information and Technology Branch, DPI Victoria, Queenscliff, Victoria 3225, Australia- 
Brett.Matthews@dpi.vic.gov.au 
e Faculty of Engineering Technology, the University of Twente, Netherlands- J.G.Ferwerda@ctw.utwente.nl 
f School of Engineering, Deakin University, Geelong, Victoria 3217, Australia- abbas.kouzani@deakin.edu.au 
Commission VII 
KEY WORDS: Remote sensing, wireless sensor network, precision agriculture, nitrogen, hyper spectral, validation 
ABSTRACT: 
Water and Nitrogen (N) are critical inputs for crop production. Remote sensing data collected from multiple scales, including 
ground-based, aerial, and satellite, can be used for the formulation of an efficient and cost effective algorithm for the detection of N 
and water stress. Formulation and validation of such techniques require continuous acquisition of ground based spectral data over 
the canopy enabling field measurements to coincide exactly with aerial and satellite observations. In this context, a wireless sensor in 
situ network was developed and this paper describes the results of the first phase of the experiment along with the details of sensor 
development and instrumentation set up. The sensor network was established based on different spatial sampling strategies and each 
sensor collected spectral data in seven narrow wavebands (470, 550, 670, 700, 720, 750, 790 nm) critical for monitoring crop 
growth. Spectral measurements recorded at required intervals (up to 30 seconds) were relayed through a multi-hop wireless network 
to a base computer at the field site. These data were then accessed by the remote sensing centre computing system through broad 
band internet. Comparison of the data from the WSN and an industry standard ground based hyperspectral radiometer indicated that 
there were no significant differences in the spectral measurements for all the wavebands except for 790nm. Combining sensor and 
wireless technologies provides a robust means of aerial and satellite data calibration and an enhanced understanding of issues of 
variations in the scale for the effective water and nutrient management in wheat. 
1. INTRODUCTION 
Water and Nitrogen (N) are critical inputs for wheat production. 
Judicial application of these inputs is essential for 
environmentally sustainable and profitable agricultural 
production. Standard practice is to apply N fertilizers at a 
uniform rate based on the field level average available soil N or 
target grain yield (Zillmann et al. 2006). Optimizing the N 
availability is crucial as N is a vital component for vegetative 
growth, chlorophyll formation (Gooding and Davies 1997) and 
grain development in wheat (Wright Jr. 2003). On the other 
hand, excessive availability of N can heighten the risks of frost 
damage, foliar disease (Olesen et al. 2003) and can also delay 
crop maturation (Gooding and Davies 1997). If excessive rates 
of N are applied, which are not balanced by stored soil water 
and/or in-crop rainfall, then this can result in moisture stress 
which in turn results in premature ripening of the crop; referred 
to as ‘haying-off (Herwaarden et al. 1998). Surplus N 
application also leads to potential off-farm movement of 
nitrogen into surface and ground water and can have strong 
effects on the structure and function of both terrestrial and 
marine ecosystems like eutrophication (Smith et al. 1999). 
Application of the optimal rates of N based on spatial variability 
of soil conditions at high spatial resolutions could lead to cost 
effective and environmentally sustainable crop production 
(LaRuffa et al. 2001). Remote sensing techniques are powerful 
Corresponding author. 
tools for monitoring spatial variations in crop growth 
characteristics non-destructively. These techniques are based on 
the spectral reflectance characteristics of the plant canopy which 
in turn is dependent on the spatial distribution/orientation of 
plant leaves and supporting structures, the nature of pigments 
contained within the individual leaves and internal leaf structure 
(eg mesophyll arrangements) (Chappelle et al. 1992; Myers 
1983). 
Nutrient status detection using remote sensing is a relatively 
new concept, made possible by the development of high spatial 
and spectral resolution sensors. Over the past few years a 
number of studies have shown the potential to use remote 
sensing for the detection of nitrogen status of grains 
(Haboudane et al. 2002a; Lilienthal et al. 2000; Strachan et al. 
2002). These studies have however been unable to resolve the 
problem of the interacting causes of plant-growth limitation, 
such as water-shortage and nutrient limitation. A few studies 
have shown that combining optical narrow band imaging with 
thermal imaging may provide a solution to this problem 
(Fitzgerald et al. 2006b; Tilling et al. 2007). These studies 
demonstrated the utility of hyperspectral and narrow-band 
multispectral remote sensing techniques, utilizing the canopy 
reflectance characteristics in wavebands 445, 670, 705, 720, 
750 and 790 nm, for the detection of spatial variation in the N 
status of the crop. Indices such as the Canopy Chlorophyll 
Content Index (CCCI) (Barnes et al. 2000; Rodriguez et al.