Ref: C0632
Predicting
spreaders
spread
patterns
of
centrifugal
fertiliser
Bilal Hijazia, Jürgen Vangeytea, Simon Coola, Koen C. Mertensa, David Nuyttensa, Julien
Duboisb, Frédéric Cointaultb, Jan G. Pietersc
a
Institute for Agricultural and Fisheries Research (ILVO),Technology and Food Science Unit,
Agricultural engineering, Burg. van Gansberghelaan 115 bus 1, 9820 Merelbeke, Belgium
b
Institut national supérieur des sciences agronomiques, de l'alimentation et de
l'environnement (AgroSup), 26, Bd Dr Petitjean BP 87999, Dijon Cedex, 21079 France
c
Ghent University, Department of Biosystems Engineering, Faculty of Bioscience
Engineering, Coupure links 653, Gent, 9000
* [email protected]
Abstract
Nowadays farmers recognize the importance of a correct and precise fertiliser application:
non-uniform spread patterns cause extra pressure on the environment and might result in
economic losses for the farmer. In Europe most spreading is done by centrifugal fertilizer
spreaders but their spreading process is not easy to monitor and to control. To perform a
precise fertilization farmers need proper tools to determine and evaluate the spread patterns
at farm level. Therefore the Flemish Institute for Agricultural and Fisheries Research (ILVO)
and its partners are exploring and developing a fast and accurate technique for measuring
the spread pattern of conventional centrifugal spreaders. This method aims to be low cost
and applicable at the farm level. Also, the proposed solution has to be mobile to the extent
that the device can be built up on-site to test several machines. The device should enable
the adjustment of the spreader in such a way that a uniform spread pattern is obtained. At a
later stage, an onboard sensor could be envisaged. Three main approaches for evaluating
the spread pattern are currently available: the experimental collector tray method, the full
modelling approach like the Discrete Element Method (DEM), and the hybrid approach that
combines measurements and modelling. In this research the hybrid approach was applied:
the spread pattern was predicted with a ballistic flight model based on the measurement of
the horizontal outlet angle, the vertical outlet angle, the grain diameter, the grain density and
the initial velocity. In a first step, a 2-dimensional imaging technique was used with a small
field of view (0.33m x 0.25m) to measure the horizontal outlet angle and the speed of the
grains at different camera positions at the circumference of the disk. The vertical outlet angle
and the mass distribution were measured with a cylindrical collector. The grains flying under
the measurement unit were imaged using two different techniques: the high speed technique
and a newly developed multi-exposure imaging technique. For the high speed technique a
camera, type MotionXtra HG 100K (Roper Scientific, New Jersey, USA), was used. The
stroboscopic technique combined a specially designed LED stroboscope with a Nikon D 100
camera. Overall the stroboscopic technique and the high speed technique were capable of
measuring the outlet angle and the outlet speed. Small differences between the
measurements with both techniques existed, but ultimately in the aim is the determination of
the resulting spread pattern in the field. When comparing the simulated and the measured
spread pattern, relative errors amounted up to 30%. Therefore, in the next phase the twodimensional imaging technique was adapted with a much larger field of view (1m x 1m),
improved lighting system and motion estimation algorithms, resulting in lower relative errors
between the simulated and the measured cylindrical spread pattern. At the moment a 3D
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stereovision set-up (and related algorithms) to further improve the simulation of the spread
pattern is being developed.
Keywords: spread pattern, image techniques, cross correlation, ballistic flight,
fertiliser
1
Introduction
Nowadays farmers recognize the importance of a correct and precise fertiliser application:
non-uniform spread patterns cause extra pressure on the environment and might result in
economic losses for the farmer. (Hijazi et al., 2010). In Europe most spreading is done by
centrifugal fertilizer spreaders (Van Liedekerke et al., 2008). They combine large working
widths and easy maintenance with a relatively small investment cost. (Garcia-Ramos et al.,
2012). Pneumatic fertiliser spreaders are more expensive and pendulum spreaders have a
limited working width (Aphale et al., 2003). The centrifugal spreading process, however, is
not easy to monitor and to control(Vangeyte et al., 2004; Van Liedekerke et al., 2008).
To perform a precise fertilization, farmers need proper tools to determine and evaluate the
spread pattern at farm level. However, they do not have the machinery, agronomic support or
operator knowledge to verify the correct application rate and uniformity required to produce
healthy crops on the one hand and prevent environmental side effects and yield losses on
the other hand. (Hijazi et al., 2010).
Three approaches for evaluating spread patterns are currently available. (1) In a specially
designed spread hall or simply outside in the field the spread pattern can be measured by
collecting the grains with standardized trays placed perpendicular to the driving direction
(Tissot et al., 2002). The test is time consuming but it allows to evaluate the spreader in
practice at farm level (Vangeyte et al., 2007). Piron and Miclet (2006) developed a technique
that is cheaper and faster than the classical transversal method: the CEMIB or CEmagref
MIneral Bench (Piron et al., 2010). (2) The full modeling approach applies the Discrete
Element Method (DEM) to model the behaviour of the grains on the disk. Several
researchers showed that DEM simulations give promising results and perform qualitatively
well. More validation experiments and knowledge of the initial conditions of the particles are
necessary (Van Liedekerke et al., 2009). (3) The third approach combines measurements
with ballistic models to determine the spread pattern. This hybrid approach measures
specific parameters of the spreading process and uses these measurements as input for a
ballistic model to determine the spread pattern. These measurements involve the
measurement of the initial speed, direction and sometimes also the diameter of the grains.
Until now a real closed loop control system with appropriate sensors to accurately predict
and control the fertilizer distribution as applied in the field is not yet available (Cool et al.,
2014). Therefore, together with its partners Ghent University, University of Bourgogne, and
Agrosup Dijon, the Flemish Institute for Agricultural and Fisheries Research (ILVO) has been
developing and improving a fast and accurate technique for measuring the spread patterns of
conventional centrifugal spreaders. This paper summarizes and presents the methods and
results of ongoing research on predicting spread patterns of centrifugal fertiliser spreaders.
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2
Materials and methods
The developed method is based on the hybrid approach as described above. Since the final
solution for the farmers has to be fast, low cost and robust, an image based measuring
technique with appropriate algorithms was developed in several steps.
2.1
Two dimensional method using a small field of view
First a two dimensional imaging technique with a small field of view (0.33m x 0.25m) was
used to measure the horizontal outlet angle and the speed of the grains at different camera
positions around the circumference of the disk. Therefore, a modular measurement device
(Fig. 2.1) was developed that consisted of two main parts: a test spreader and a
measurement unit. The test spreader consisted of a hopper and an electric motor equipped
with a frequency modulator to set the rotation speed of the spread disk. The disk was
mounted with two long vanes (0.281 m). The spread pattern of this combination was
measured in a spread hall. As the arm with the measurement unit pivots about the center of
the test device, the measurement unit traces a path around the circumference of the disk.
The grains flying under the measurement unit were imaged using two different techniques: a
high speed and a stroboscopic imaging technique. For the high speed technique a camera,
type MotionXtra HG 100K (Roper Scientific, New Jersey, USA) was used. The stroboscopic
technique combined a specially designed LED stroboscope with a Nikon D 100 camera.
Recordings of the grain flow and calibration pictures were made at ten different positions
along the circumference of the disk.
Fig. 2.1 The modular device
To automatically recognise the grains as objects on the images taken by both techniques,
image analysis algorithms were developed in Matlab. The successive positions of the grains
in the image were determined so that the coordinates of the grains belonging to the same
trajectory were known. Then the distance and direction in the coordinate system of the image
were determined. To predict the trajectory and the landing position of the grains, the pixel
coordinates of the grains obtained from the image processing were transformed to real world
coordinates in a coordinate system attached to the disk center. This allowed to calculate the
horizontal outlet speed and the direction of the grains. The vertical outlet angle and the mass
distribution were then measured with a cylindrical collector.
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2.2
Two dimensional method with a large field of view and improved lighting
system
In the next phase the above approach was further improved by combining the idea of the
stroboscopic lighting technique with the method described in Cointault et al. (2003). In that
set-up a large field of view (1m x 1m) was used to be able to picture the whole circumference
of the disk. The applied motion estimation method combined a theoretical model of the grain
distribution and the Markov Random Fields method, based on the determination of optical
flow characteristics on the images (Cointault & Vangeyte, 2005).
To provide sufficient lighting a new stroboscopic system was developed (FR 13/57039). An
assembly of power-LEDs was arranged in an optimal configuration to provide adequate
illumination.
Fig. 2.2 Set up developed by Cointault et al., (2003) at Agrosup, Dijon.
With this new set-up a new motion estimation method had to be developed to estimate the
motion of the grains. Several motion estimation methods can be used. Displacements made
by fertilizer grains in pixels/image are generally larger than the displacements estimated with
classical motion estimation methods. To account for the noise and luminosity variation in our
image a two steps normalized cross-correlation algorithm using the local mean value and the
local variance value (Nillius and Eklundh, 2002) was implemented. The first step calculates
one total displacement vector for each fertilizer throw, while the second one refines this
vector to estimate the local motion for each pixel. Since the new stroboscopic device is still
under development, the motion estimation method was first tested on multi-exposure images
obtained with the set-up of Cointault et al. (2003). Then a simulator was developed to create
images with known grain velocities and trajectories. These simulated images were used to
evaluate the Markov Random Fields and cross-correlation techniques.
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3.1
Results and Discussion
Two dimensional method using a small field of view
Overall the stroboscopic technique and the high speed technique were capable of picturing
the grains at the different positions around the circumference of the disk. The high speed
images were of much better quality than the images taken with the stroboscopic system due
to the poor lighting conditions. Consequently, image enhancement was necessary to extract
information from the stroboscopic image while the high speed images did not need image
enhancement and noise removal. They were used to be compared with the images from the
stroboscopic technique. In Fig. 3.1 the images of the two different approaches are shown.
Fig. 3.1 High speed technique image (left) and stroboscopic technique image (right)
In Fig. 3.2 the 665 measurements for the horizontal outlet speed are illustrated. The mean
speed was 25.10 m/s ± 1.95 for the stroboscopic technique and 25.60 m/s ± 1.65 for the high
speed technique. The mean horizontal outlet angle measured with the stroboscopic
technique was 130.65° ± 3.90. The high speed technique measured 130.96° ± 4.07.
Fig. 3.2 All measurements of the horizontal outlet speed [mm/s] plotted as a function of the outlet
position [°].The blue triangles represent the high speed results, while the red crosses are the
stroboscopic measurements.
Small differences between the measurements of speed and direction with both techniques
existed, but ultimately we were interested in the resulting spread pattern in the field.
Therefore the measured horizontal speed and direction of the grains were combined with the
vertical outlet angle and entered in a ballistic model. When comparing this simulated spread
pattern with the spread pattern measured in a spread hall, relative errors between measured
and simulated spread pattern (Olieslagers, 1997) amounted up to 30%.
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3.2
Two dimensional method with a large field of view and improved lighting
system
With 6 times 8 3W power-LEDs positioned on the corners of a hexagon, the average
illuminance of the new stroboscopic system was 600 lx at a perpendicular distance of 1 m.
This is not sufficient to deliver the 1000 lx necessary at 1 m height. Therefore it is necessary
to further increase the number of LEDs. At the moment a new version of the system is being
further developed.
Fig 3.3 shows an image of one fertiliser throw at two instances and the results of the two step
cross correlation method. When the results are qualitatively evaluated the new method is
found to perform better than the Markov Random Fields method (Cointault et al., 2003).
Fig. 3.3. Fertilizer throws at different time instances (left) and results of two-step cross correlation
technique (right)
Since no reference method to determine the actual velocities is available, until now validation
has been performed by comparing the results with visual evaluation on actual images. The
cross correlation method was also evaluated using simulated images. The cross-correlation
method determined precisely the velocities with an average error of 0.1 pixel or less, and
90% of the grain velocities with a rate of error less than 0.4 pixel. The technique only needs
two successive images and the result does not depend on the distance between the grains.
The semi-local motion estimation technique is very well suited for non-uniform motion and
can provide sub-pixel accuracy.
4
Conclusions and Future work
The final goal of our research is to develop a fast and low cost method to measure and
evaluate spread patterns of fertiliser spreaders at farm level. Two steps towards this
development are explained in this paper.
First a two dimensional method to measure the directions and the speeds of fertilizer grains
was developed for a small field of view. It was shown that a stroboscopic lighting system
combined with a low-cost camera was a useful technique. However, to work in practice, a
larger field of view that allows to analyse the full circumference of the disk is necessary.
Therefore the stroboscopic system was further developed to be able to illuminate a larger
scene. A first prototype was developed but needs further improvements with more and more
powerful LEDs.
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At the same time new motion estimation algorithms were developed. By testing these
algorithms with simulated images we have shown their potential for our application.
However, in the future important challenges remain to be tackled. First of all, the proposed
solutions are not able to measure the vertical outlet angle. Moreover, the deviation caused by
the perspective projection is not taken into account. Our method is restricted to the
calibration of spreaders equipped with flat disks that mostly eject the grains in the horizontal
plane. Also, a simplified two-dimensional ballistic model is applied and only the gravitational
and drag force are considered.
At the moment research is conducted to solve these issues. A 3D-stereovision set-up is
operational. The 3D matching algorithm and the 3D motion estimation are under
development and an improved ballistic flight model was recently developed (Cool et al.,
2014). Until now all testing was performed indoor under controlled light conditions. The
possibilities of the proposed techniques for an outdoor application are not yet evaluated.
5
Acknowledgements
The authors would like to thank Richard Martin from AgroSup Dijon and the technical team
from ILVO for their help in developing the stroboscope system, designing the different setups and performing the measurements.
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FR 13/57039 DISPOSITIF D’ECLAIRAGE Eclairage stroboscopique haute cadence avec
large plage d’uniformité lumineuse à base de LEDs de puissance. (Hijazi B., Cointault F.,
Dubois J., Vangeyte J., and Clerc C.)
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