Directory UMM :Data Elmu:jurnal:S:Sensor Review:Vol19.Issue3.1999:
Research articles
Recognising leafy
plants with in-air sonar
Phillip John McKerrow and
Neil Lindsay Harper
The authors
Phillip John McKerrow and Neil Lindsay Harper are
both at the School of Information Technology and
Computer Science, University of Wollongong,
Wollongong, New South Wales, Australia.
Keywords
Sensors, Sonar, Ultrasonic
Abstract
Continuous Transmission Frequency Modulated (CTFM)
ultrasonic sensors can be used to recognise plants. The
echo from a plant is modelled as an acoustic density
profile. Classification based on features extracted from
the echo is more robust than classification based on the
echo. Potential applications for this sensing system
include landmark navigation and plant sensing for
selective spraying of agricultural chemicals.
Electronic access
The research register for this journal is available at
http://www2.mcb.co.uk/mcbrr/sr.asp
The current issue and full text archive of this journal is
available at
http://www.emerald-library.com
Sensor Review
Volume 19 . Number 3 . 1999 . pp. 202±206
# MCB University Press . ISSN 0260-2288
Introduction
An ideal range sensing system provides three
types of information: object location, object
motion, and object classification. In-air sonar
research and development has achieved considerable success with the first of these.
Object location can be measured in 3D
(Akbarally and Kleeman, 1995), indoor environments can be mapped (McKerrow,
1993) and mobile robots navigated (Stevens
et al., 1995).
Using in-air sonar to recognise and classify
objects is a new development. The energy in
the echo of a sonar chirp from an object is a
function of the surface area of the object. The
distribution of that energy with time is a
function of the relative ranges of the elemental
areas that make up the complex surface.
Using measurements of the distribution of the
energy in an echo, a sonar system can
calculate features proportional to the depth
and solid area of complex objects. With these
features it can recognise objects.
Leafy plants are naturally occurring objects
with complex geometric structure. When
blind people were trained to use a sonar
mobility aid (Kay, 1974) they reported that
they could tell the difference between different plants (Gisonni, 1966). When using a
sensor to recognise plants one problem is the
asymmetry of plants with rotation. The echo
received by the sensor varies depending on
the angle from which a plant is insonified.
However, we discovered that there is information in the echo which characterises the
structure of the plant and is relatively invariant with orientation.
A sensor that can recognise plants has many
potential applications. One is the detection of
landmarks for the outdoor navigation of
mobile robots. Another is the detection of
weeds in crop rows. Selective spraying of
herbicides can considerably reduce the
amount of poison introduced into the environment. A third is the detection of fruit
during harvesting.
The techniques for plant recognition can be
applied to the sensing of any set of objects
that can be separated using the measurement
of their geometric structure. A sensor that can
Received and Revised April 1999. This research
was supported by Thomson Marconi Sonar, and
the sensing system was purchased from Bay
Advanced Technologies in New Zealand.
202
Recognising leafy plants with in-air sonar
Sensor Review
Volume 19 . Number 3 . 1999 . 202±206
Phillip John McKerrow and Neil Lindsay Harper
recognise objects has many potential applications in industry. One is the recognition of
sub-assemblies in a manufacturing process.
Another is the measurement of the size of
gravel in crushing plants. A third is the
recognition of faces at airports.
Figure 2 CTFM Signals. fsweep = sweep frequency
range, fa = audio frequency, famax = maximum audio
frequency, ts = sweep time, td = time to echo at
maximum range, to = time period for FFT sampling, ta
time to echo
CTFM
Continuous Transmission Frequency Modulated (CTFM) Sonar was developed for in-air
applications by Leslie Kay (Gough et al.,
1984). A CTFM system (Figure 1) transmits
a frequency swept sine wave using a wide
bandwidth electrostatic transducer. The region of insonification is determined by the
geometry of the transducer. Ideally, the echo
is a time delayed copy of the transmitted
signal, but the shape of the envelope is
changed by the filtering effects of reflection
off objects and the frequency response of the
receiver.
After reception, the echo is demodulated
with the transmitted signal (Figure 2) to
produce fixed frequency tones for stationary
objects. The frequency of a tone is proportional to the range to the reflecting object.
The amplitude of the tone is proportional to
the energy reflected at that range. A Fast
Fourier Transform (FFT) is used to convert
the time domain tones into a frequency
spectrum.
The signal processing for the generation of
the sweep and the demodulation of the echo
can be done in analog electronics or in
software. Digital signal processing requires
analog anti-aliasing filters to remove digitisation noise from the transmitted signal. The
gains of the filter stages are adjusted to give a
fixed amplitude tone for an echo from a
reference object that is located on the axis of
the transmitted beam.
Figure 1 Block diagram of CTFM sensing system
The sweep time (ts in Figure 2) is determined
by the maximum range, the maximum demodulation frequency and the length of the
FFT sampling period. The audio tone for a
target is continuous from the time at which
the echo arrives until the end of the sweep.
Thus, the sweep consists of two time periods:
the time to the arrival of an echo from
maximum range (td) and the time to capture
the samples for the FFT (to). The range of the
sensor is selected by choosing the sweep time.
A typical set up is a 50kHz sweep (fsweep)
from 100kHz down to 50kHz and a sweep
time of ts = 102.4 msec. With these settings,
and a maximum demodulation frequency of
famax = 5kHz, the time to arrival of an echo
from maximum range is td = 10.24msec. The
velocity of sound in air is 331.6 + 0.6 *T
(where T is the temperature in ëC). Thus, the
maximum range at 20ëC is 1.75m. During the
rest of the sweep, the demodulated frequency
is constant enabling the signal to be sampled
for frequency calculation. When sampling at
12KHz, a sample period of 86 msec is
required to obtain the samples for a 1024
point FFT.
Echo data
In the time domain, the complexity of the
audio signal is proportional to the geometric
complexity of the target. In the frequency
domain (Figure 3), a spectral line occurs
for each range where energy is reflected. A
203
Recognising leafy plants with in-air sonar
Sensor Review
Volume 19 . Number 3 . 1999 . 202±206
Phillip John McKerrow and Neil Lindsay Harper
Figure 3 Echo spectra of (a) Eucalyptus maculata and (b) Polyscias
murrayi. Note: the plants were isolated from any background while being
sensed
Figure 4 Correlation of an asymmetric plant (Crinum Pedunculatum top)
and a symmetric plant (Pittosporum crassifolium bottom), with black for
correlation using features and grey for correlation using echo spectra. The
reference spectrum is at 256Ê
1024 point FFT produces positive amplitude
values for 512 * 9.77 Hz frequency bands,
each equivalent to 3.43mm of range. The
amplitude of the spectral line is proportional
to the intensity of the echo and hence to the
area of the reflecting surface normal to the
receiver.
The foliage of the first plant in Figure 3
is sparse and oriented to form a shell around
the plant. As a result, most of the energy
is reflected from the front and the back
of the plant. This is shown in the spectrum
by the large peeks at the start and end of
the echo. The second plant is more typical
with a large peek in the spectrum toward
the front of the plant, due to the denser
foliage.
Features
recognised with the sensor aimed within 5ë of
the orientation at which the reference spectrum was recorded.
Features extracted from the echo spectrum
vary less with rotation, and hence, are better
for classification. Features can be selected in a
number of ways. We chose to select features
on the basis of their ability to discriminate
between a set of plants and their correspondence to geometric features of the plant.
When the features can be mapped to plant
geometry we have a more robust sensing
system because it is based on a theoretical
understanding of the sensing process. The
result of research into features is an acoustic
density profile model.
Acoustic density profile
Plants can be recognised by comparing the
echo spectrum to a set of recorded spectra for
the plants of interest. However, the spectrum
varies with rotation. The variation depends on
the symmetry of the plant with rotation
(Figure 4).
Many plants are locally symmetric with
their echo spectra giving good correlation over
about 10ë of orientation. Some, like Crinum
Pedunculatum, have regions with good local
correlation separated by large steps due to the
leaves being oriented parallel to a few planes,
rather that being spread evenly around the
plant. Consequently, most plants can be
The aim of the acoustic density profile model
is to map signal features into acoustic features
and acoustic features into plant geometry. In
most cases, we have achieved a one to one
mapping from signal feature to acoustic
feature. But the mapping from acoustic
feature to plant geometry is often one to
many. As a consequence, the acoustic density
profile models the structure of the plant
foliage rather than the details of the plant,
such as leaf shape.
Each spectral line is a measure of the
acoustic energy reflected at that range. The
204
Recognising leafy plants with in-air sonar
Sensor Review
Volume 19 . Number 3 . 1999 . 202±206
Phillip John McKerrow and Neil Lindsay Harper
frequency is proportional to the range and
the amplitude to the acoustic area of the
reflecting surfaces at that range. A plant's
acoustic area is determined by leaf area, leaf
orientation, occlusion and multi-path reflection. The complete spectrum for a plant
is the acoustic density of the foliage as a
function of depth. The length of the
spectrum is the acoustic depth of the plant
which is less than or equal to the physical
depth.
The sum of the spectral lines (the area
under the curve) is the total acoustic energy
reflected by the plant and is a measure of the
total acoustic area of the plant. The variation
of the amplitudes of the spectral lines
(envelope of the spectrum) represents the
variation in acoustic density as the signal
penetrates into the plant. Layering of the
foliage results in repeated patterns in the
spectrum. For example, the front and back
surfaces of Eucalyptus maculata appear as
large spikes at the start and end of the
spectrum in Figure 3.
Threshold features are good for classification because they contain a lot of information
about the structure of the foliage, but they
have multiple mappings to geometry. Each
threshold feature is the count of peeks above a
threshold (one feature for each decade in the
range 10.90 per cent) in the normalised
spectrum. These features can be used to
classify plants according to the denseness of
their foliage. Blind users of Leslie Kay's
mobility aid claim that they can sense the size
of leaves. We are yet to discover a feature that
represents leaf size.
Classification
After examining many features we arrived at a
set of 19 that produce the best discrimination
between plants. These can be further reduced
to suit the group of plants in a specific sensing
situations. When designing a classifier for an
application, we can determine the features we
need by measuring the error rate of the
classifier when finding the linear separation of
the plants. The error rate is the ratio of the
number of classification errors to the number
of cases.
In our research we measured 100 plants.
A statistical classifier was run on all pairs of
plants to determine which plants are the
most similar. Pairwise classification also
calculates the average classification of a plant
against all of the 99 other plants using the
Sonar data. The result of pairwise classification for all 100 plants was an average
classification of 90.5 per cent with a standard
deviation of 9.6.
Most pairs produced good results and many
pairs correctly classify 100 per cent of the test
set. But, there are pairs of plants which are
difficult to separate. This indicates either that
their acoustic density profiles change a lot
through rotation or that the plants are similar.
The results of pairwise classification can be
used to determine which plants in a set will be
easy to recognise and which plants will be
difficult. As the number of plants increases
the discrimination ability of a classifier reduces. We use both statistical and neural
network classifiers.
A statistical classifier has the advantage over
a neural net classifier in that it provides a list
of features that are ordered in terms of their
discriminatory ability. This provides important information about the differences
between different combinations of plants. A
neural net classifier has the advantage that it is
a non-linear classifier and generally achieves a
better classification result.
Conclusion
Analysis of the acoustic density profiles of 100
different plants shows that they can be
separated using pattern classification when a
small sample is considered. As the number of
plants increases the classification percentage
generally decreases. The result of classifying a
single return as one of 100 plants is low.
When there is a large number of plants, there
is a high probability of plants with similar
acoustic density profiles.
Plants can be clustered either on the basis of
botanical classification or on the basis of
similar acoustic templates. The result is very
different clusters. Clustering on the basis of
acoustic templates gives similar clusters to
clustering on the basis of physical characteristics, due to the fact that the acoustic features
can be mapped to geometric features of the
plant. Some plants show much more variation
with rotation than others. Acoustic features
show less variation with rotation than the
acoustic density profiles.
In current research, we are applying this
sensor to landmark navigation of an
205
Recognising leafy plants with in-air sonar
Sensor Review
Volume 19 . Number 3 . 1999 . 202±206
Phillip John McKerrow and Neil Lindsay Harper
outdoor mobile robot. Preliminary results
indicate that we should be able to track the
edges of paths and use plants as natural
landmarks. Later this year, we will commence
research into agricultural applications of this
sensor. This research will focus on recognising weeds among vegetables, such as
cabbages, and detecting fruit on vines, such as
tomatoes.
References
Akbarally, H. and Kleeman, L. (1995), ``A sonar sensor for
accurate 3D target localisation and classification'',
Proceedings ICRA'95, IEEE, pp. 3003-8.
Gissoni, F. (1966), ``My cane is twenty feet long'', The
New Outlook for the Blind, February.
Gough, P.T., de Roos, A. and Cusdin, M.J. (1984),
``Continuous transmission F.M. sonar with one
octave bandwidth and no blind time'', IEE Proceedings, Vol. 131, Part F No 3, pp. 270-4.
Kay, L. (1974), ``A sonar aid to enhance spatial perception
of the blind: engineering design and evaluation'',
The Radio and Electronic Engineer, Vol. 44 No. 11,
pp. 605-27.
McKerrow, P.J. (1993), ``Echolocation ± from range to
outline segments'', Robotics and Autonomous
Systems, Elsevier, Vol. 11 No. 4, pp. 205-11.
Stevens, M., Stevens, A., and Durrant-Whyte, H. (1995),
``OxNav: reliable autonomous navigation'', Proceedings ICRA'95, IEEE, pp. 2607-12.
206
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Recognising leafy
plants with in-air sonar
Phillip John McKerrow and
Neil Lindsay Harper
The authors
Phillip John McKerrow and Neil Lindsay Harper are
both at the School of Information Technology and
Computer Science, University of Wollongong,
Wollongong, New South Wales, Australia.
Keywords
Sensors, Sonar, Ultrasonic
Abstract
Continuous Transmission Frequency Modulated (CTFM)
ultrasonic sensors can be used to recognise plants. The
echo from a plant is modelled as an acoustic density
profile. Classification based on features extracted from
the echo is more robust than classification based on the
echo. Potential applications for this sensing system
include landmark navigation and plant sensing for
selective spraying of agricultural chemicals.
Electronic access
The research register for this journal is available at
http://www2.mcb.co.uk/mcbrr/sr.asp
The current issue and full text archive of this journal is
available at
http://www.emerald-library.com
Sensor Review
Volume 19 . Number 3 . 1999 . pp. 202±206
# MCB University Press . ISSN 0260-2288
Introduction
An ideal range sensing system provides three
types of information: object location, object
motion, and object classification. In-air sonar
research and development has achieved considerable success with the first of these.
Object location can be measured in 3D
(Akbarally and Kleeman, 1995), indoor environments can be mapped (McKerrow,
1993) and mobile robots navigated (Stevens
et al., 1995).
Using in-air sonar to recognise and classify
objects is a new development. The energy in
the echo of a sonar chirp from an object is a
function of the surface area of the object. The
distribution of that energy with time is a
function of the relative ranges of the elemental
areas that make up the complex surface.
Using measurements of the distribution of the
energy in an echo, a sonar system can
calculate features proportional to the depth
and solid area of complex objects. With these
features it can recognise objects.
Leafy plants are naturally occurring objects
with complex geometric structure. When
blind people were trained to use a sonar
mobility aid (Kay, 1974) they reported that
they could tell the difference between different plants (Gisonni, 1966). When using a
sensor to recognise plants one problem is the
asymmetry of plants with rotation. The echo
received by the sensor varies depending on
the angle from which a plant is insonified.
However, we discovered that there is information in the echo which characterises the
structure of the plant and is relatively invariant with orientation.
A sensor that can recognise plants has many
potential applications. One is the detection of
landmarks for the outdoor navigation of
mobile robots. Another is the detection of
weeds in crop rows. Selective spraying of
herbicides can considerably reduce the
amount of poison introduced into the environment. A third is the detection of fruit
during harvesting.
The techniques for plant recognition can be
applied to the sensing of any set of objects
that can be separated using the measurement
of their geometric structure. A sensor that can
Received and Revised April 1999. This research
was supported by Thomson Marconi Sonar, and
the sensing system was purchased from Bay
Advanced Technologies in New Zealand.
202
Recognising leafy plants with in-air sonar
Sensor Review
Volume 19 . Number 3 . 1999 . 202±206
Phillip John McKerrow and Neil Lindsay Harper
recognise objects has many potential applications in industry. One is the recognition of
sub-assemblies in a manufacturing process.
Another is the measurement of the size of
gravel in crushing plants. A third is the
recognition of faces at airports.
Figure 2 CTFM Signals. fsweep = sweep frequency
range, fa = audio frequency, famax = maximum audio
frequency, ts = sweep time, td = time to echo at
maximum range, to = time period for FFT sampling, ta
time to echo
CTFM
Continuous Transmission Frequency Modulated (CTFM) Sonar was developed for in-air
applications by Leslie Kay (Gough et al.,
1984). A CTFM system (Figure 1) transmits
a frequency swept sine wave using a wide
bandwidth electrostatic transducer. The region of insonification is determined by the
geometry of the transducer. Ideally, the echo
is a time delayed copy of the transmitted
signal, but the shape of the envelope is
changed by the filtering effects of reflection
off objects and the frequency response of the
receiver.
After reception, the echo is demodulated
with the transmitted signal (Figure 2) to
produce fixed frequency tones for stationary
objects. The frequency of a tone is proportional to the range to the reflecting object.
The amplitude of the tone is proportional to
the energy reflected at that range. A Fast
Fourier Transform (FFT) is used to convert
the time domain tones into a frequency
spectrum.
The signal processing for the generation of
the sweep and the demodulation of the echo
can be done in analog electronics or in
software. Digital signal processing requires
analog anti-aliasing filters to remove digitisation noise from the transmitted signal. The
gains of the filter stages are adjusted to give a
fixed amplitude tone for an echo from a
reference object that is located on the axis of
the transmitted beam.
Figure 1 Block diagram of CTFM sensing system
The sweep time (ts in Figure 2) is determined
by the maximum range, the maximum demodulation frequency and the length of the
FFT sampling period. The audio tone for a
target is continuous from the time at which
the echo arrives until the end of the sweep.
Thus, the sweep consists of two time periods:
the time to the arrival of an echo from
maximum range (td) and the time to capture
the samples for the FFT (to). The range of the
sensor is selected by choosing the sweep time.
A typical set up is a 50kHz sweep (fsweep)
from 100kHz down to 50kHz and a sweep
time of ts = 102.4 msec. With these settings,
and a maximum demodulation frequency of
famax = 5kHz, the time to arrival of an echo
from maximum range is td = 10.24msec. The
velocity of sound in air is 331.6 + 0.6 *T
(where T is the temperature in ëC). Thus, the
maximum range at 20ëC is 1.75m. During the
rest of the sweep, the demodulated frequency
is constant enabling the signal to be sampled
for frequency calculation. When sampling at
12KHz, a sample period of 86 msec is
required to obtain the samples for a 1024
point FFT.
Echo data
In the time domain, the complexity of the
audio signal is proportional to the geometric
complexity of the target. In the frequency
domain (Figure 3), a spectral line occurs
for each range where energy is reflected. A
203
Recognising leafy plants with in-air sonar
Sensor Review
Volume 19 . Number 3 . 1999 . 202±206
Phillip John McKerrow and Neil Lindsay Harper
Figure 3 Echo spectra of (a) Eucalyptus maculata and (b) Polyscias
murrayi. Note: the plants were isolated from any background while being
sensed
Figure 4 Correlation of an asymmetric plant (Crinum Pedunculatum top)
and a symmetric plant (Pittosporum crassifolium bottom), with black for
correlation using features and grey for correlation using echo spectra. The
reference spectrum is at 256Ê
1024 point FFT produces positive amplitude
values for 512 * 9.77 Hz frequency bands,
each equivalent to 3.43mm of range. The
amplitude of the spectral line is proportional
to the intensity of the echo and hence to the
area of the reflecting surface normal to the
receiver.
The foliage of the first plant in Figure 3
is sparse and oriented to form a shell around
the plant. As a result, most of the energy
is reflected from the front and the back
of the plant. This is shown in the spectrum
by the large peeks at the start and end of
the echo. The second plant is more typical
with a large peek in the spectrum toward
the front of the plant, due to the denser
foliage.
Features
recognised with the sensor aimed within 5ë of
the orientation at which the reference spectrum was recorded.
Features extracted from the echo spectrum
vary less with rotation, and hence, are better
for classification. Features can be selected in a
number of ways. We chose to select features
on the basis of their ability to discriminate
between a set of plants and their correspondence to geometric features of the plant.
When the features can be mapped to plant
geometry we have a more robust sensing
system because it is based on a theoretical
understanding of the sensing process. The
result of research into features is an acoustic
density profile model.
Acoustic density profile
Plants can be recognised by comparing the
echo spectrum to a set of recorded spectra for
the plants of interest. However, the spectrum
varies with rotation. The variation depends on
the symmetry of the plant with rotation
(Figure 4).
Many plants are locally symmetric with
their echo spectra giving good correlation over
about 10ë of orientation. Some, like Crinum
Pedunculatum, have regions with good local
correlation separated by large steps due to the
leaves being oriented parallel to a few planes,
rather that being spread evenly around the
plant. Consequently, most plants can be
The aim of the acoustic density profile model
is to map signal features into acoustic features
and acoustic features into plant geometry. In
most cases, we have achieved a one to one
mapping from signal feature to acoustic
feature. But the mapping from acoustic
feature to plant geometry is often one to
many. As a consequence, the acoustic density
profile models the structure of the plant
foliage rather than the details of the plant,
such as leaf shape.
Each spectral line is a measure of the
acoustic energy reflected at that range. The
204
Recognising leafy plants with in-air sonar
Sensor Review
Volume 19 . Number 3 . 1999 . 202±206
Phillip John McKerrow and Neil Lindsay Harper
frequency is proportional to the range and
the amplitude to the acoustic area of the
reflecting surfaces at that range. A plant's
acoustic area is determined by leaf area, leaf
orientation, occlusion and multi-path reflection. The complete spectrum for a plant
is the acoustic density of the foliage as a
function of depth. The length of the
spectrum is the acoustic depth of the plant
which is less than or equal to the physical
depth.
The sum of the spectral lines (the area
under the curve) is the total acoustic energy
reflected by the plant and is a measure of the
total acoustic area of the plant. The variation
of the amplitudes of the spectral lines
(envelope of the spectrum) represents the
variation in acoustic density as the signal
penetrates into the plant. Layering of the
foliage results in repeated patterns in the
spectrum. For example, the front and back
surfaces of Eucalyptus maculata appear as
large spikes at the start and end of the
spectrum in Figure 3.
Threshold features are good for classification because they contain a lot of information
about the structure of the foliage, but they
have multiple mappings to geometry. Each
threshold feature is the count of peeks above a
threshold (one feature for each decade in the
range 10.90 per cent) in the normalised
spectrum. These features can be used to
classify plants according to the denseness of
their foliage. Blind users of Leslie Kay's
mobility aid claim that they can sense the size
of leaves. We are yet to discover a feature that
represents leaf size.
Classification
After examining many features we arrived at a
set of 19 that produce the best discrimination
between plants. These can be further reduced
to suit the group of plants in a specific sensing
situations. When designing a classifier for an
application, we can determine the features we
need by measuring the error rate of the
classifier when finding the linear separation of
the plants. The error rate is the ratio of the
number of classification errors to the number
of cases.
In our research we measured 100 plants.
A statistical classifier was run on all pairs of
plants to determine which plants are the
most similar. Pairwise classification also
calculates the average classification of a plant
against all of the 99 other plants using the
Sonar data. The result of pairwise classification for all 100 plants was an average
classification of 90.5 per cent with a standard
deviation of 9.6.
Most pairs produced good results and many
pairs correctly classify 100 per cent of the test
set. But, there are pairs of plants which are
difficult to separate. This indicates either that
their acoustic density profiles change a lot
through rotation or that the plants are similar.
The results of pairwise classification can be
used to determine which plants in a set will be
easy to recognise and which plants will be
difficult. As the number of plants increases
the discrimination ability of a classifier reduces. We use both statistical and neural
network classifiers.
A statistical classifier has the advantage over
a neural net classifier in that it provides a list
of features that are ordered in terms of their
discriminatory ability. This provides important information about the differences
between different combinations of plants. A
neural net classifier has the advantage that it is
a non-linear classifier and generally achieves a
better classification result.
Conclusion
Analysis of the acoustic density profiles of 100
different plants shows that they can be
separated using pattern classification when a
small sample is considered. As the number of
plants increases the classification percentage
generally decreases. The result of classifying a
single return as one of 100 plants is low.
When there is a large number of plants, there
is a high probability of plants with similar
acoustic density profiles.
Plants can be clustered either on the basis of
botanical classification or on the basis of
similar acoustic templates. The result is very
different clusters. Clustering on the basis of
acoustic templates gives similar clusters to
clustering on the basis of physical characteristics, due to the fact that the acoustic features
can be mapped to geometric features of the
plant. Some plants show much more variation
with rotation than others. Acoustic features
show less variation with rotation than the
acoustic density profiles.
In current research, we are applying this
sensor to landmark navigation of an
205
Recognising leafy plants with in-air sonar
Sensor Review
Volume 19 . Number 3 . 1999 . 202±206
Phillip John McKerrow and Neil Lindsay Harper
outdoor mobile robot. Preliminary results
indicate that we should be able to track the
edges of paths and use plants as natural
landmarks. Later this year, we will commence
research into agricultural applications of this
sensor. This research will focus on recognising weeds among vegetables, such as
cabbages, and detecting fruit on vines, such as
tomatoes.
References
Akbarally, H. and Kleeman, L. (1995), ``A sonar sensor for
accurate 3D target localisation and classification'',
Proceedings ICRA'95, IEEE, pp. 3003-8.
Gissoni, F. (1966), ``My cane is twenty feet long'', The
New Outlook for the Blind, February.
Gough, P.T., de Roos, A. and Cusdin, M.J. (1984),
``Continuous transmission F.M. sonar with one
octave bandwidth and no blind time'', IEE Proceedings, Vol. 131, Part F No 3, pp. 270-4.
Kay, L. (1974), ``A sonar aid to enhance spatial perception
of the blind: engineering design and evaluation'',
The Radio and Electronic Engineer, Vol. 44 No. 11,
pp. 605-27.
McKerrow, P.J. (1993), ``Echolocation ± from range to
outline segments'', Robotics and Autonomous
Systems, Elsevier, Vol. 11 No. 4, pp. 205-11.
Stevens, M., Stevens, A., and Durrant-Whyte, H. (1995),
``OxNav: reliable autonomous navigation'', Proceedings ICRA'95, IEEE, pp. 2607-12.
206
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