Directory UMM :Data Elmu:jurnal:A:Agricultural Water Management:Vol45.Issue2.Jul2000:
Agricultural Water Management 45 (2000) 159±167
Digital image processing for determining drop
sizes from irrigation spray nozzles
K.P. Sudheera,*, R.K. Pandab
a
Scientist `B', National Institute of Hydrology, Deltaic Regional Centre, Siddhartha Nagar,
Kakinada 533 003, India
b
Assistant Professor, Department of Agricultural Engineering, Indian Institute of Technology,
Kharagpur 721 302, India
Accepted 21 September 1999
Abstract
All the existing methods for measuring drop sizes produced by a sprinkler nozzle are either
cumbersome, expensive or time consuming. Moreover, none could quantitatively express the
relationship between drop size distribution and sprinkler head parameters viz. operating pressure
and nozzle size. In the present study digital image processing technique has been applied to
determine the drop size distribution from an irrigation spray nozzle. Image processing is the
technique of automating and integrating a wide range of processes used for the human vision
perception. The present study revealed that image processing technique can be successfully
implemented for drop size measurement accurately. Being a novel technique, the method has some
limitations for adaptation. These limitations can be very well contained through further research.
# 2000 Elsevier Science B.V. All rights reserved.
Keywords: Sprinkler irrigation; Drop size; Image processing
1. Introduction
For any sprinkler irrigation system, there is no direct evaluation procedure available to
assess the system performance analytically. The evaluation is generally done with the
support of field experiments. The main reason for this is that the droplet sizes produced
by any sprinkler nozzle have a significant effect on the uniformity of application. These
*
Corresponding author. Tel.: 91-884-372254; fax: 91-884-350054.
E-mail address: [email protected] (K.P. Sudheer)
0378-3774/00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 3 7 7 4 ( 9 9 ) 0 0 0 7 9 - 7
160
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
drop size characteristics are functions of nozzle size and operating pressure. Solomon et
al. (1985) indicated that although a few researchers have published measured data on the
drop size distribution, none could quantitatively express the relationship between nozzle
size, operating pressure and the drop size distribution. One of the reasons for this may be
non availability of a technique to measure the drop size accurately.
A sprinkler of a given nozzle size and trajectory angle, operating at a constant water
pressure would produce a particular range of drop sizes. The dispersing action of the jet
which leads to drop formation is due to the turbulence and air friction drag (Kohl, 1974).
The drop size from spray nozzles is an important factor affecting the formation of seals
on soil surfaces that restricts infiltration. Because small drops possess less energy, when
they impact the soil surface, seal that limit infiltration form more slowly than larger
drops. For these reasons, it is sometimes possible to reduce runoff and erosion by
converting from sprinklers that emit large drops to ones with smaller drops. Drop size is
especially important when sprinklers operate in winds. Distribution patterns from
sprinklers that emit smaller drops are more subject to wind distortion and lower
application uniformity. In addition, increased losses due to wind drift usually occur with
small droplet sprinklers (James, 1988).
Measurement of drop sizes, produced by a sprinkler nozzle, is also important in
quantitatively expressing the relationship between the drop size distribution and the
sprinkler head parameters viz. operating pressure and nozzle size. Such a relation can
further be helpful in developing an analytical model to evaluate the system performance.
Measurement of drop sizes can further be used to evaluate the impact of the water drops
on the soil. Direct and accurate measurement of the drop size is possible, but the
equipment required for such a procedure is highly sophisticated and expensive.
1.1. Existing techniques
The stain method (Seginer, 1963), based on the assumption that a drop falling upon a
uniform absorbent surface produces a stain whose diameter is proportional to the
diameter of the drop, has been in use since the early 1940s. The distribution of drop sizes
is determined by comparing the size of the stain with those produced by drops of a known
diameter. Hall (1970), by experimenting with many absorbent surfaces, cautioned the
potential users of this technique stating that care must be taken to ensure that drops fall
over at a sufficient distance to attain their terminal velocities before striking the absorbent
surface.
The photographic method (Hoffman, 1977) has the advantage of being a direct
measurement technique to determine the size of individual drops. But the equipment
required to make the measurement is too cumbersome for field use. Besides, visual
interpretation techniques have certain disadvantages. They require extensive training and
are labour intensive (Lillisand and Keifer, 1987). In addition spectral characteristics are
not always fully evaluated in visual interpretation efforts. This is because of the limited
ability of the eye to discern total values on an image and the difficulty for an interpreter to
simultaneously analyze numerous spectral pattern.
Kohl and DeBoer (1983) described `the flour method' as exposing flour in bags to
rainfall and the drop size determination through calibration charts. The flour is exposed to
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
161
rainfall and the water drops form a spherical shape with flour on the surface. This is then
dried in oven and the diameter of the dried pellet is measured. Calibration charts prepared
from water drops of known diameter are used for determining the drop sizes from the
rainfall. Laws found that the calibration curve varied slightly from one bag of flour to the
next bag of the same brand of flour. They also stated that the calibration curves for
smaller diameter drops were difficult to obtain.
The momentum method, that includes pressure transducers and piezo electric sensors,
has been successfully used to measure rain drop size. However, they have most
commonly been used to measure rainfall energy since they aggregate the effects of
multiple drops striking a finite surface area.
Tate (1961) developed the immersion technique by collecting water drops in a low
density, immiscible liquid (oil). The oil envelops the drops preventing both evaporation
from the drop as well as any condensation on the drop. Owing to the higher force of
surface tension water drops form a spherical shape, diameter of which can be measured
by using a measuring microscope or any similar equipment.
All these techniques are either cumbersome, or require expensive and sophisticated
equipment. Keeping in view the drawbacks of existing techniques, a study was conducted
to determine the feasibility of using digital image processing for measuring the drop sizes
from a sprinkler nozzle.
1.2. Image processing technique
Image processing is the enterprise of automating and integrating a wide range of
processes and representation used for vision perception. Computer vision research often
deals with relatively domain independent considerations. The results are useful in a wide
range of contexts. Usually such work is demonstrated for one or more application areas
(Deluitche et al., 1990; Liao et al., 1990). In general, an image processing technique
consists of three steps viz. data acquisition, processing and interpretation.
1.3. Data acquisition
The first step in the vision process is image formation. Images may arise from a variety
of techniques. For example, most television based systems convert reflected light
intensity into an electronic signal which is then digitized on requirement. In the present
study, image formation has been done by taking photographs of the sprinkler droplets and
digitizing it using a scanner.
1.4. Processing and interpretation
The imaging process converts useful physical information into a gray level array. The
intensity of each pixel corresponds to the average brightness measured electronically over
a neighborhood around each pixel. Each pixel is assigned a positive integer that results
from quantising the original electrical signal from the scanner into positive value using
analog to digital conversion. In this respect, imaging process is a collection of degenerate
transformations. However, the information is not irrecoverably lost because there is much
162
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
spatial redundancy. Neighboring pixels in the image have the same or nearly same
physical parameters. A collection of techniques which is called `processing', exploits the
redundancy to undo the degeneracies in the imaging process. Once the digitized image is
processed, the information can be interpreted, based on the objective, using several
techniques.
2. Methodology
Photographs of sprinkler droplets, in flight, were taken using a high resolution, high
speed camera and digitized using a scanner. The photograph obtained from a SLR
camera, fixed on a stand, has been converted into a digital image using a CCD (charge
couple device) camera connected to a MVP/AT computer system. Thus the scene is
converted to a two dimensional array of grey values. A sample digitised image is
presented in Fig. 1.
Segmentation is done on the digitised image to partition an image into regions which
correspond to the object of interest. Thresholding is the most popular segmentation
method. It is important in a digital image processing to select an adequate threshold of
gray level for extracting objects from its background. In ideal case, the gray level
histogram has a deep and sharp valley between two peaks representing objects and
background respectively, so that the threshold can be selected at the bottom of the valley.
Fig. 1. Sample digitised image of water drops on ¯y.
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
163
Segmentation leads to a binary image consisting of white objects (water drops) in a black
background. In the next step, one may measure the dropsize and count by extracting the
region and computing its parameters.
Several techniques are available for developing the regions out of the segmented
image. In the present study `pixel aggregation technique' has been used, being a simple
one. The approach starts with a set of seed points and thereafter, regions are grown by
appending to each seed point those neighboring pixels having similar grey values.
Once all the regions (water drops) in the image are developed, the area of each region
is found by computing the number of pixels in the region. The number of pixels can be
converted into actual area using the resolution and scale of the actual image. The
perimeter of the regions are also computed in a similar way by counting the number of
pixels in the object region with at least one neighbour pixel in the background region.
2.1. Shape identi®cation
It is important to determine the shape of the region to confirm if it belongs to the
category of the object of interest. The compactness ratio was used as an index to identify
the shape of region in the present study. Digital images, being a two-dimensional descrete
matrix of pixel grey values, can not reproduce the Eucledian geometry of the nature.
Thus, a circle will be a concentrated collection of square boxes (pixels) in the digital
image and will not have the ideal compactness value of 1/4p. Moreover, as the shape of
water drops moving through air at high speed need not be exact circle and will be
distorted, the compactness value will be around 1/4p. Thus, a tolerance range became
necessary. If the image is highly noisy and shapes are distorted, a larger tolerance limit
may be advisable. In our experiment, scenes were of moderate complexity and we chose
the tolerance limit as 1/12p. Though this parameter may influence the final size/shape
distribution, selection of threshold should be made based on the complexity of the scene
and presence of distortion in the scene, which can be observed by examining the grey
level histogram.
The compactness ratio is defined as,
compactness
area
perimeter2
(1)
In Eq. (1), the area corresponds to the number of pixels falling in the object region (water
drop) and perimeter corresponds to the number of pixels in the object region with an
adjacent neighbouring pixel in the background.
2.2. Drop size determination
Droplet sizes are computed, assuming that the region is circular, from the value of its
area as,
r
4A
(2)
d
p
in which, d±diameter of the drop; A±area of the region.
164
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
3. Results and discussions
The digitized image for the sprinkler nozzle discharge was prepared in a similar
procedure discussed above. Thresholding depends on the characteristics of the grey level
histogram (Fig. 2). This histogram depicts the number of pixels with each grey value in
the range 0±255. When the gray level histogram of the image was studied, it was
observed that there is very little variation between the gray level of all pixels
(corresponding to the object as well as background). Hence, it was difficult to separate
the object from its background by searching a valley. This difficulty in delineation of
object and background led to division of the image into different grids (windows),
thresholding individual grid separately, and then integration of all the segmented grids for
further analysis. Since the threshold selection based on the valley in the histogram was
not feasible, a method proposed by Otsu (1979) was used for each grid. The method is
characterized by its non-parametric and unsupervised nature of threshold selection. The
method utilizes the zeroth and first order cumulative moments of the gray levels and an
optimal threshold is selected automatically and suitably. It avoids the consideration of a
local property such as valley, but considers the integration of the histogram.
The area of each region was determined using region grow algorithm which works
based on the principle of pixel aggregation. The criterion for the identification of the
region was fixed as a gray level value of 255 (perfect white). Since gray level value of all
objects (water drops) were made to 255 during thresholding, any region identified should
fall in the category of water drops. Confirmation of the identified object, if it falls in the
category of interest, was done by computing the compactness ratio of each region using
Eq. (1).
Fig. 2. Greylevel histogram of digitised image.
165
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
Table 1
Output of image processing technique
Area (mm2)
Perimeter (mm)
Compactness
Diameter (mm)
0.73533
1.41066
4.23202
4.93732
7.05337
7.75871
8.46400
9.16936
9.87472
11.28534
11.99072
13.40099
16.21685
3.09880
4.52345
7.22370
8.40800
9.99180
10.34000
10.32400
11.55120
12.01529
12.81280
13.49200
14.31460
14.30197
0.07658
0.06894
0.08110
0.06984
0.07065
0.07257
0.07941
0.06872
0.06840
0.06874
0.06587
0.06540
0.07928
0.96760
1.34019
2.32129
2.50727
2.99677
3.14304
3.28279
3.41684
3.54583
3.79064
3.90731
4.13070
4.54400
In the process of identification of shape, a tolerance limit of 1/12p to the computed
compactness was selected. All those regions whose compactness ratio fell within this
range was considered as a water drop.
The results of the image processing technique for droplet size determination for a
sprinkler of 5 mm diameter nozzle at an operating pressure of 14 m of water is presented
Fig. 3. Comparative plot of results from the image processing technique and pellet method.
166
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
in Table 1. This table depicts the area, perimeter alongwith the computed compactness of
the dropsizes corresponding to each of the region obtained.
The resulted distribution of droplets from the image processing technique has been
compared with the measured data. The pellet method was used to measure the drop size
in laboratory for comparative study. The comparative plot of drop size distribution
resulted from image processing technique and pellet method for a sprinkler nozzle of
5 mm diameter operating at a pressure of 14 m is presented in Fig. 3. It can be observed
from the plot that the smaller dimension drops (below 0.90 mm) were not computed by
the presented technique. Further examination of the results (Fig. 3) revealed that the drops
having diameter of about 0.90 mm have increased a great deal in number when compared
to actual data. This may be due to the fact that the resolution (the range of resolution
depends on the hardware configuration of the computer) taken for the digital image was
only 256 256 pixels and each pixel corresponds to an actual size of 0.839 0.839 mm.
This size of pixel is large when compared to the size of smaller dimension drops. Any
region having an area below that of a pixel was considered having an area equal to that of
a pixel and the computations were performed accordingly. Hence it can be concluded that
for computer vision technique to be used for determining smaller drop sizes, the
resolution of pixel should be taken in such a way that the least possible dimension of the
drop falls below it. In other words, higher the resolution of the digital image, greater is
the accuracy of the technique.
4. Summary and conclusion
The feasibility of image processing technique for determination of the drop sizes from
an irrigation spray nozzle was studied. The photographs of the droplets on fly were taken
using an ordinary camera and analyzed using the digital image processing technique. The
technique performed reasonably well in determining the drop size distribution of water
spray from irrigation nozzles. Being a novel investigation, the method has some
limitations for adoption, which can be rectified through further research. This technique
may be much useful for researchers to ascertain a quantitative relationship between the
drop size distribution, operating pressure of sprinkler and its nozzle diameter.
References
Deluitche, M.J., Tang, S., Thompson, J.F., 1990. Prune defected detection by linescan imaging. Transactions of
the ASAE 15 (2), 950±960.
Hoffman, F.W., 1977. Applications of droplet photography. Calfran Industries, Spring®eld, MA.
James, L.G., 1988. Principle of Farm Irrigation System Design. Wiley, New York, 543 pp.
Kohl, R.A., 1974. Drop size distribution from medium sized agricultural sprayers. Transactions of the ASAE 8
(2), 186±190.
Kohl, R.A., DeBoer, D.W., 1983. Drop size distributions for a low pressure spray type agricultural sprinkler.
ASAE paper no. 83±2019, ASAE, St. Joseph, MI, 49085, p.16.
Liao, K., Cavaliert, R.P., Pitte, M.J., 1990. Handroff dimensional analysis and digital image based quality
inspection. Transactions of the ASAE 33 (1), 298±303.
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
167
Lillisand, T.M., Keifer, R.W., 1987. Remote Sensing And Image Interpretation. Wiley, Canada, pp. 20±24.
Otsu, N., 1979. A threshold selection method from grey level histograms. IEEE Transactions On Systems, Man
And Cybernetics, SMC 9 (1), 62±66.
Seginer, I., 1963. Water distribution from medium pressure sprinklers. J. Irrig. Drainage Eng. ASCE 89 (IR2),
13±29.
Solomon, K.H., Kineaid, D.C., Bezdek, J.C., 1985. Drop size distribution for irrigation spray nozzles.
Transactions of the ASAE 28 (16), 1966±1974.
Digital image processing for determining drop
sizes from irrigation spray nozzles
K.P. Sudheera,*, R.K. Pandab
a
Scientist `B', National Institute of Hydrology, Deltaic Regional Centre, Siddhartha Nagar,
Kakinada 533 003, India
b
Assistant Professor, Department of Agricultural Engineering, Indian Institute of Technology,
Kharagpur 721 302, India
Accepted 21 September 1999
Abstract
All the existing methods for measuring drop sizes produced by a sprinkler nozzle are either
cumbersome, expensive or time consuming. Moreover, none could quantitatively express the
relationship between drop size distribution and sprinkler head parameters viz. operating pressure
and nozzle size. In the present study digital image processing technique has been applied to
determine the drop size distribution from an irrigation spray nozzle. Image processing is the
technique of automating and integrating a wide range of processes used for the human vision
perception. The present study revealed that image processing technique can be successfully
implemented for drop size measurement accurately. Being a novel technique, the method has some
limitations for adaptation. These limitations can be very well contained through further research.
# 2000 Elsevier Science B.V. All rights reserved.
Keywords: Sprinkler irrigation; Drop size; Image processing
1. Introduction
For any sprinkler irrigation system, there is no direct evaluation procedure available to
assess the system performance analytically. The evaluation is generally done with the
support of field experiments. The main reason for this is that the droplet sizes produced
by any sprinkler nozzle have a significant effect on the uniformity of application. These
*
Corresponding author. Tel.: 91-884-372254; fax: 91-884-350054.
E-mail address: [email protected] (K.P. Sudheer)
0378-3774/00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 3 7 7 4 ( 9 9 ) 0 0 0 7 9 - 7
160
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
drop size characteristics are functions of nozzle size and operating pressure. Solomon et
al. (1985) indicated that although a few researchers have published measured data on the
drop size distribution, none could quantitatively express the relationship between nozzle
size, operating pressure and the drop size distribution. One of the reasons for this may be
non availability of a technique to measure the drop size accurately.
A sprinkler of a given nozzle size and trajectory angle, operating at a constant water
pressure would produce a particular range of drop sizes. The dispersing action of the jet
which leads to drop formation is due to the turbulence and air friction drag (Kohl, 1974).
The drop size from spray nozzles is an important factor affecting the formation of seals
on soil surfaces that restricts infiltration. Because small drops possess less energy, when
they impact the soil surface, seal that limit infiltration form more slowly than larger
drops. For these reasons, it is sometimes possible to reduce runoff and erosion by
converting from sprinklers that emit large drops to ones with smaller drops. Drop size is
especially important when sprinklers operate in winds. Distribution patterns from
sprinklers that emit smaller drops are more subject to wind distortion and lower
application uniformity. In addition, increased losses due to wind drift usually occur with
small droplet sprinklers (James, 1988).
Measurement of drop sizes, produced by a sprinkler nozzle, is also important in
quantitatively expressing the relationship between the drop size distribution and the
sprinkler head parameters viz. operating pressure and nozzle size. Such a relation can
further be helpful in developing an analytical model to evaluate the system performance.
Measurement of drop sizes can further be used to evaluate the impact of the water drops
on the soil. Direct and accurate measurement of the drop size is possible, but the
equipment required for such a procedure is highly sophisticated and expensive.
1.1. Existing techniques
The stain method (Seginer, 1963), based on the assumption that a drop falling upon a
uniform absorbent surface produces a stain whose diameter is proportional to the
diameter of the drop, has been in use since the early 1940s. The distribution of drop sizes
is determined by comparing the size of the stain with those produced by drops of a known
diameter. Hall (1970), by experimenting with many absorbent surfaces, cautioned the
potential users of this technique stating that care must be taken to ensure that drops fall
over at a sufficient distance to attain their terminal velocities before striking the absorbent
surface.
The photographic method (Hoffman, 1977) has the advantage of being a direct
measurement technique to determine the size of individual drops. But the equipment
required to make the measurement is too cumbersome for field use. Besides, visual
interpretation techniques have certain disadvantages. They require extensive training and
are labour intensive (Lillisand and Keifer, 1987). In addition spectral characteristics are
not always fully evaluated in visual interpretation efforts. This is because of the limited
ability of the eye to discern total values on an image and the difficulty for an interpreter to
simultaneously analyze numerous spectral pattern.
Kohl and DeBoer (1983) described `the flour method' as exposing flour in bags to
rainfall and the drop size determination through calibration charts. The flour is exposed to
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
161
rainfall and the water drops form a spherical shape with flour on the surface. This is then
dried in oven and the diameter of the dried pellet is measured. Calibration charts prepared
from water drops of known diameter are used for determining the drop sizes from the
rainfall. Laws found that the calibration curve varied slightly from one bag of flour to the
next bag of the same brand of flour. They also stated that the calibration curves for
smaller diameter drops were difficult to obtain.
The momentum method, that includes pressure transducers and piezo electric sensors,
has been successfully used to measure rain drop size. However, they have most
commonly been used to measure rainfall energy since they aggregate the effects of
multiple drops striking a finite surface area.
Tate (1961) developed the immersion technique by collecting water drops in a low
density, immiscible liquid (oil). The oil envelops the drops preventing both evaporation
from the drop as well as any condensation on the drop. Owing to the higher force of
surface tension water drops form a spherical shape, diameter of which can be measured
by using a measuring microscope or any similar equipment.
All these techniques are either cumbersome, or require expensive and sophisticated
equipment. Keeping in view the drawbacks of existing techniques, a study was conducted
to determine the feasibility of using digital image processing for measuring the drop sizes
from a sprinkler nozzle.
1.2. Image processing technique
Image processing is the enterprise of automating and integrating a wide range of
processes and representation used for vision perception. Computer vision research often
deals with relatively domain independent considerations. The results are useful in a wide
range of contexts. Usually such work is demonstrated for one or more application areas
(Deluitche et al., 1990; Liao et al., 1990). In general, an image processing technique
consists of three steps viz. data acquisition, processing and interpretation.
1.3. Data acquisition
The first step in the vision process is image formation. Images may arise from a variety
of techniques. For example, most television based systems convert reflected light
intensity into an electronic signal which is then digitized on requirement. In the present
study, image formation has been done by taking photographs of the sprinkler droplets and
digitizing it using a scanner.
1.4. Processing and interpretation
The imaging process converts useful physical information into a gray level array. The
intensity of each pixel corresponds to the average brightness measured electronically over
a neighborhood around each pixel. Each pixel is assigned a positive integer that results
from quantising the original electrical signal from the scanner into positive value using
analog to digital conversion. In this respect, imaging process is a collection of degenerate
transformations. However, the information is not irrecoverably lost because there is much
162
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
spatial redundancy. Neighboring pixels in the image have the same or nearly same
physical parameters. A collection of techniques which is called `processing', exploits the
redundancy to undo the degeneracies in the imaging process. Once the digitized image is
processed, the information can be interpreted, based on the objective, using several
techniques.
2. Methodology
Photographs of sprinkler droplets, in flight, were taken using a high resolution, high
speed camera and digitized using a scanner. The photograph obtained from a SLR
camera, fixed on a stand, has been converted into a digital image using a CCD (charge
couple device) camera connected to a MVP/AT computer system. Thus the scene is
converted to a two dimensional array of grey values. A sample digitised image is
presented in Fig. 1.
Segmentation is done on the digitised image to partition an image into regions which
correspond to the object of interest. Thresholding is the most popular segmentation
method. It is important in a digital image processing to select an adequate threshold of
gray level for extracting objects from its background. In ideal case, the gray level
histogram has a deep and sharp valley between two peaks representing objects and
background respectively, so that the threshold can be selected at the bottom of the valley.
Fig. 1. Sample digitised image of water drops on ¯y.
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
163
Segmentation leads to a binary image consisting of white objects (water drops) in a black
background. In the next step, one may measure the dropsize and count by extracting the
region and computing its parameters.
Several techniques are available for developing the regions out of the segmented
image. In the present study `pixel aggregation technique' has been used, being a simple
one. The approach starts with a set of seed points and thereafter, regions are grown by
appending to each seed point those neighboring pixels having similar grey values.
Once all the regions (water drops) in the image are developed, the area of each region
is found by computing the number of pixels in the region. The number of pixels can be
converted into actual area using the resolution and scale of the actual image. The
perimeter of the regions are also computed in a similar way by counting the number of
pixels in the object region with at least one neighbour pixel in the background region.
2.1. Shape identi®cation
It is important to determine the shape of the region to confirm if it belongs to the
category of the object of interest. The compactness ratio was used as an index to identify
the shape of region in the present study. Digital images, being a two-dimensional descrete
matrix of pixel grey values, can not reproduce the Eucledian geometry of the nature.
Thus, a circle will be a concentrated collection of square boxes (pixels) in the digital
image and will not have the ideal compactness value of 1/4p. Moreover, as the shape of
water drops moving through air at high speed need not be exact circle and will be
distorted, the compactness value will be around 1/4p. Thus, a tolerance range became
necessary. If the image is highly noisy and shapes are distorted, a larger tolerance limit
may be advisable. In our experiment, scenes were of moderate complexity and we chose
the tolerance limit as 1/12p. Though this parameter may influence the final size/shape
distribution, selection of threshold should be made based on the complexity of the scene
and presence of distortion in the scene, which can be observed by examining the grey
level histogram.
The compactness ratio is defined as,
compactness
area
perimeter2
(1)
In Eq. (1), the area corresponds to the number of pixels falling in the object region (water
drop) and perimeter corresponds to the number of pixels in the object region with an
adjacent neighbouring pixel in the background.
2.2. Drop size determination
Droplet sizes are computed, assuming that the region is circular, from the value of its
area as,
r
4A
(2)
d
p
in which, d±diameter of the drop; A±area of the region.
164
K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
3. Results and discussions
The digitized image for the sprinkler nozzle discharge was prepared in a similar
procedure discussed above. Thresholding depends on the characteristics of the grey level
histogram (Fig. 2). This histogram depicts the number of pixels with each grey value in
the range 0±255. When the gray level histogram of the image was studied, it was
observed that there is very little variation between the gray level of all pixels
(corresponding to the object as well as background). Hence, it was difficult to separate
the object from its background by searching a valley. This difficulty in delineation of
object and background led to division of the image into different grids (windows),
thresholding individual grid separately, and then integration of all the segmented grids for
further analysis. Since the threshold selection based on the valley in the histogram was
not feasible, a method proposed by Otsu (1979) was used for each grid. The method is
characterized by its non-parametric and unsupervised nature of threshold selection. The
method utilizes the zeroth and first order cumulative moments of the gray levels and an
optimal threshold is selected automatically and suitably. It avoids the consideration of a
local property such as valley, but considers the integration of the histogram.
The area of each region was determined using region grow algorithm which works
based on the principle of pixel aggregation. The criterion for the identification of the
region was fixed as a gray level value of 255 (perfect white). Since gray level value of all
objects (water drops) were made to 255 during thresholding, any region identified should
fall in the category of water drops. Confirmation of the identified object, if it falls in the
category of interest, was done by computing the compactness ratio of each region using
Eq. (1).
Fig. 2. Greylevel histogram of digitised image.
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Table 1
Output of image processing technique
Area (mm2)
Perimeter (mm)
Compactness
Diameter (mm)
0.73533
1.41066
4.23202
4.93732
7.05337
7.75871
8.46400
9.16936
9.87472
11.28534
11.99072
13.40099
16.21685
3.09880
4.52345
7.22370
8.40800
9.99180
10.34000
10.32400
11.55120
12.01529
12.81280
13.49200
14.31460
14.30197
0.07658
0.06894
0.08110
0.06984
0.07065
0.07257
0.07941
0.06872
0.06840
0.06874
0.06587
0.06540
0.07928
0.96760
1.34019
2.32129
2.50727
2.99677
3.14304
3.28279
3.41684
3.54583
3.79064
3.90731
4.13070
4.54400
In the process of identification of shape, a tolerance limit of 1/12p to the computed
compactness was selected. All those regions whose compactness ratio fell within this
range was considered as a water drop.
The results of the image processing technique for droplet size determination for a
sprinkler of 5 mm diameter nozzle at an operating pressure of 14 m of water is presented
Fig. 3. Comparative plot of results from the image processing technique and pellet method.
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K.P. Sudheer, R.K. Panda / Agricultural Water Management 45 (2000) 159±167
in Table 1. This table depicts the area, perimeter alongwith the computed compactness of
the dropsizes corresponding to each of the region obtained.
The resulted distribution of droplets from the image processing technique has been
compared with the measured data. The pellet method was used to measure the drop size
in laboratory for comparative study. The comparative plot of drop size distribution
resulted from image processing technique and pellet method for a sprinkler nozzle of
5 mm diameter operating at a pressure of 14 m is presented in Fig. 3. It can be observed
from the plot that the smaller dimension drops (below 0.90 mm) were not computed by
the presented technique. Further examination of the results (Fig. 3) revealed that the drops
having diameter of about 0.90 mm have increased a great deal in number when compared
to actual data. This may be due to the fact that the resolution (the range of resolution
depends on the hardware configuration of the computer) taken for the digital image was
only 256 256 pixels and each pixel corresponds to an actual size of 0.839 0.839 mm.
This size of pixel is large when compared to the size of smaller dimension drops. Any
region having an area below that of a pixel was considered having an area equal to that of
a pixel and the computations were performed accordingly. Hence it can be concluded that
for computer vision technique to be used for determining smaller drop sizes, the
resolution of pixel should be taken in such a way that the least possible dimension of the
drop falls below it. In other words, higher the resolution of the digital image, greater is
the accuracy of the technique.
4. Summary and conclusion
The feasibility of image processing technique for determination of the drop sizes from
an irrigation spray nozzle was studied. The photographs of the droplets on fly were taken
using an ordinary camera and analyzed using the digital image processing technique. The
technique performed reasonably well in determining the drop size distribution of water
spray from irrigation nozzles. Being a novel investigation, the method has some
limitations for adoption, which can be rectified through further research. This technique
may be much useful for researchers to ascertain a quantitative relationship between the
drop size distribution, operating pressure of sprinkler and its nozzle diameter.
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