PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Ultrasonic Remote Sensing for Precision Agriculture
Stuart BRADLEY1; Mathew LEGG2
1

Inverse Acoustics Ltd, New Zealand2

Massey University, New Zealand

ABSTRACTPrecision Agriculture makes use of quantitative measurements as input to sophisticated farm management
software. For management of grazing animals, such as dairy cows, a key input is pasture biomass, so that
pasture is not over-grazed nor have too much or too little application of fertilizer. Aside from destructivelycutting, drying, and weighing the pasture, current measurement methods are slow and/or unreliable. We
describe a new ultrasonic sensor which is compact and low-power, and which senses pasture properties
remotely from a moving farm bike or UAV. The sensor comprises co-located log-spiral arrays of transmittersand receivers, giving high spatial resolution transverse to the propagation direction without excessive
component counts. A pulsed, radar-like linear-FM chirp and matched filter gives high along-axis spatialresolution. With CLEAN image deconvolution, mm resolution is obtained through the pasture layer. Thismethodology allows detailed profiles of pasture density to be obtained at rates of 100 profiles per second. We
present results retrieved via modelling of the complex acoustic scattering occurring in the pasture layer.
Keywords: sensor arrays, ultrasonic sensor, scattered sound, chirp pulse

1. INTRODUCTION
Modern farming is an intensive business in which optimization of resources through observation based farm management software is an important component of ‘precision agriculture’. For
management of grazing animals such as dairy cows, the quantity of economic interest is the biomass,or the mass of ‘dry matter’ (DM) per unit area of ground, which is the mass of pasture per unit area
when the pasture has been cut and dried. It is this dry matter which contains the food value for thelivestock. Current methods for estimating biomass include: cutting, drying, and weighing; measuring
the compressibility of pasture using a ‘rising plate meter’ (RP); measuring the depth of pasture usinga ‘CDaX’ or ultrasonic sensor; and using multi-spectral satellite image data. Cutting, although anabsolute measure, destroys the pasture. The biomass isுଵு‫ ܯܦ‬ൌ ‫׬‬଴ ߩ݄݀ ൌ ቀு ‫׬‬଴ ߩ݄݀ቁ ‫ ܪ‬ൌ ߩҧ ‫ܪ‬(1)where U is the bulk density and H is the depth of the pasture. The bulk density includes the empty
space between grass blades. Underlying the use of pasture height H as a measure of biomass DM isthe assumption ߩҧ is constant for a range of farm pasture conditions. In practice, it is found that thisis not true and the correlation between DM and H is not strong (1, 2).
The use of an ultrasonic sensor mounted on a motorized farm bike or UAV and remotely sensing
pasture properties is attractive because of potential low cost, low power, compactness, and the ability
to both sense the depth of the pasture as well as pasture density information within the pasture layer.
The backward scattered acoustic power, dP, from a depth dh isௗ௉ൌ ߙ௕௦ ݄݀(2)௉
where P is the power incident on the area at depth h, and D bs is the acoustic backscatter cross section
area per unit volume, as in, for example, Hodges (3). An ultrasonic ranging system can potentially1

inverse.acoustics@gmail.com

4977

sense depth h as well as D bs, a measure of pasture interception of sound, hence providing more
information than height alone. This is the essence of the new ultrasonic remote sensing tool for
precision agriculture (4).

2. SENSORS AND RESOLUTION
2.1 Choice of frequency
Ultrasound is used for several reasons. There is very little background noise at high frequencies
and short-wavelengths are reflected well from the thin grass blades. For example, Fricke et al. ( 5)
used 180 kHz. However, such frequencies are strongly scattered by the pasture and do not penetratewell through to the ground, so the instrument can only measure the distance from the sensor to thepasture tops. The depth of pasture must be then found from another measurement of distance fromsensor to the ground. This secondary measurement is typically based on the distance from sensor toground when the farm bike or other platform is at rest. This is a potential source of error, since theplatform will generally have a suspension system and the sensor-ground distance will vary.
In general, scattering cross-sections depend on the size parameter, ka, where k is the wavenumber
and a is a typical dimension of the scattering object. A typical half-width of a blade of rye grass is a
= 2 mm and for ka = 1, the frequency is 27 kHz. A common piezo-electric transducer operating
frequency is 40 kHz for operating in air, so the decision was made to use ultrasonic frequencies
between 20 kHz and 40 kHz.
2.2 Sensor array design and lateral resolution
The height above ground of the mounting on a farm bike is typically 800 mm and pasture can be
200 mm deep. The range to the top of the pasture is then typically R = 600 mm. Using the far-fieldapproximation, the diameter D of the sensor array should satisfy D 2 ≤ cR/fmax where c is the soundspeed and fmax is the maximum frequency transmitted. In practice we choose fmax = 35 kHz to avoid
the transmitting element resonance at 40 kHz, and also D = 60 mm to better satisfy the far-field
condition.
In order to keep transmitted and received beam side-lobe levels low, a sparse spiral array of sensors
is used. In figure 1, the transmitters are the square objects and the receiving microphones sense
through the small holes which lie on the white spiral lines. The power consumption is sufficiently lowthat the instrument can be run from a smart phone for 24 hours.

Figure 1 – The ultrasonic array.
The resulting lateral resolution is equivalent to a half-power footprint of around 140 mm at a rangeof R = 780 mm, or a circular area of 0.06 m 2 . This is finer than the 0.1 m 2 of the RP while providing
reasonable averaging over multiple grass blades.

4978

2.3 Chirp pulse design and vertical resolution
The typical depth of rye grass pasture is 100 – 200 mm. A typical calibration equation for theRising Plate Meter is DM (kg m-2 ) = 1.5x10 -3 h (mm) + 0.05 where h is the pasture depth. For a mean
DM = 0.2 kg m-2, a 10% error occurs if h is in error by 13 mm. This gives a rough guide of the requiredalong-range (vertical) resolution (rough, because the correlation between pasture depth and biomass
DM is not consistently strong). This is comparable to the wavelength at 35 kHz. The usual approachto obtaining high spatial resolution while maintaining high output power, is to use a coded pulse anda matched filter receiver (5). A commonly used coding is a linear FM chirp pulse where the frequency
is linearly swept from f0 to f0+B over a time T. This provides a very distinctive echo signal which canbe detected by correlation with the original transmitted signal. The result is a range resolution 'R =c/(2B), which depends only on bandwidth B and not on pulse duration T, hence allowing for longer
pulses and transmitting greater power. We use f0 = 20 kHz and B = 15 kHz, giving an along-path range
resolution of 11 mm.
2.4 CLEAN deconvolution
Since the shape of the matched filtered echo signal is well-known, the CLEAN deconvolution
method can be used to identify ‘point’ scattering elements. The basic method is to find the highest
peak intensity in the echo signal, multiply that by the matched filter sinc function, and subtract thatfrom the echo time series. This is repeated until only noise remains. Each peak intensity identified isthen considered to be the intensity from a point scatterer, resulting in a mm-scale along-path resolution.
The difficulty with CLEAN is interpretation, giving the complex scattering environment of typicalpasture (see figure 2). Grass blades extend obliquely through the scattering volume, with typically a
number of blades present at any time. For this reason, we currently have not been using CLEAN inregressions to obtain DM.

Figure 2 – The complex scattering matrix of typical pasture.
2.5 Calibration
The measured beam pattern for a target disk of radius a = 20 mm is shown in figure 3, together
with the Airy diffraction pattern for a 60 mm diameter circular aperture (the diameter of the speakerand microphone arrays). This is a plot of the normalized square of the output voltage, together withthe standard deviation from around 40 transmissions of a 20 -35 kHz chirp. The central beam patternclosely follows the Airy pattern for a tonal 35 kHz signal.
Measurements were made of echoes from a range of small disks, as shown in figure 4. Also shownis the echo from a 24.5 mm length trimmed from a 4.5 mm wide grass blade (equivalent area to acircular disk of radius 6 mm), and the theoretical backscatter from disks using the quoted sensitivities
from speaker and microphone specifications together with the array beam pattern. The agreement
between measurements and theory is very close.

4979

Figure 3 – The measured beam pattern (black) and experimental variation (green) compared withtheoretical disk diffraction at f0 (blue) and f0+B (magenta).

Figure 4 – Measured echo strength from small disks (blue dots) and a segment of a blade of grass(green dot). Also shown (red line) is the theoretical dependence, at the range of 780 mm.

3. FIELD RESULTS
Figure 5 shows some echo profiles with the ultrasonic pasture meter mounted at the front of a farm
bike moving at 10 km h -1 (2.8 m s -1). The background noise in ranges above the pasture (above thesolid green line), is low compared with the signals from within the pasture. Also, there is good
penetration to the ground (the solid red line). Echoes at ranges further than the direct distance to theground come from multiple reflections within the pasture layer. The pasture top is estimated by asimple signal threshold and the ground position from the highest signal peak. While both methods
work well much of the time, there are some failures. For pasture top, isolated high blades or stems
can give a false impression of the real depth of the bulk pasture, whereas sometimes a pasture echo
will masquerade as a ground echo. More complex algorithms have been developed, but are notdiscussed here.
Figure 6 shows a 20 m section of a farm bike traverse at 15 m s -1, comparing DM obtained by
cutting, drying and weighing, and the pasture depth estimated entirely from ultrasound. A simple linear
regression between DM and depth H over a range of platform speeds up to 20 km h -1 gives a coefficientof determination of 0.65. This is high compared with other pasture DM estimation methods.

4980

Figure 5 – A succession of echo profiles recorded (thin lines), together with estimated top ofpasture (solid green line) and ground location (solid red line).

Figure 6 – Comparison between measured DM and acoustically-estimated pasture depth along a
20 m horizontal traverse at 15 m s -1 .

4. CONCLUSIONSA compact and low-power remote sensing instrument has been designed to ultrasonically profile
through pasture from a moving platform. The design has ensured that blades of grass are sensed andalso that there is penetration to the ground. This allows the pasture layer to be identified independentlyof the instantaneous height of the sensor above ground. Information is obtained about the strength of
scattering from within the pasture layer (but not discussed in detail here). Judicious design of theantenna gives a lateral spatial resolution appropriate to averaging over representative areas of pasture,
while use of a linear FM chirp pulse gives along-path spatial resolution sufficient to determine pasture
depth accurately. The design is confirmed by detailed calibration.
Field measurements have been conducted at platform speeds up to 20 km h -1 (5.5 m s -1) whichshow good signal-to-noise performance. DM obtained by cutting, drying and weighing the pasture has
been compared with the pasture depth estimated from the ultrasonic echoes. Linear regression givesa coefficient of determination (often written R 2) greater than 0.65, which is higher than generallyachieved with other methods of DM estimation.

4981

ACKNOWLEDGEMENTS
This work is supported by a grant from the New Zealand Ministry of Business, Innovation andEmployment (MBIE), and by co-funding from Gallagher Group Ltd. The authors are grateful for thecollegial interactions with colleagues at Gallagher and from AgResearch.

REFERENCES
1. King WMcG, Rennie GM, Dalley DE, Dynes RA, Upsdell MP. Pasture Mass Estimation by the C-DAX
Pasture Meter: Regional Calibrations for New Zealand. Proc 4th Australasian Dairy Science Symposium2010. 31 August-2 September 2010; Lincoln University, Christchurch, New Zealand. p. 233-238.
2. Moeckel T, Safari H, Reddersen B, Fricke T, Wachendorf M. Fusion of ultrasonic and spectral sensor datafor improving the estimation of biomass in grasslands with heterogeneous sward structure. RemoteSensing 2017; 9; 14 pp.
3. Hodges RP. Underwater acoustics: Analysis, design and performance of sonar. Hoboken, New Jersey,
USA: John Wiley & Sons; 2011
4. Patent 740348. Vegetation Measurement Apparatus, Systems, and Methods. 2018.
5. Fricke T, Richter F, Wachendorf M. Assessment of forage mass from grassland swards by heightmeasurement using an ultrasonic sensor. Computers and Electronics in Agriculture. 2011; 79(2): 142–152.

4982