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Close-range air-assisted precision spot-spraying for
robotic applications: aerodynamics and spray coverage
analysis
Authors
Aleš Malneršič, University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, SI 1000
Ljubljana, Slovenia, ales.malnersic@fs.uni-lj.si
Matevž Dular, University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, SI 1000
Ljubljana, Slovenia, matevz.dular@fs.uni-lj.si
Brane Širok, University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, SI 1000 Ljubljana,
Slovenia, brane.sirok@fs.uni-lj.si
Roberto Oberti, University of Milan, Department of Agricultural and Environmental Sciences, Celoria 2,
20133 Milano, Italy, roberto.oberti@unimi.it
Marko Hočevar, University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, SI 1000
Ljubljana, Slovenia, marko.hocevar@fs.uni-lj.si
Abstract
Orchards and grapevines are currently sprayed overall. Most bush and tree crop sprayers
use airflow assistance which generates movements in canopy exposing both sides of the
leaves to the spray. Also, large coherent vortices are formed further contributing to
improved spray coverage. A new close-range air-assisted spot-spraying method for the
selective treatments of disease foci is evaluated here which is promising for reduction of
pesticides. Targets structures are expected to have typical diameters around 150 mm and
the size of the unit matches this. In contrast to conventional methods, this size of unit
prevents the generation of large scale coherent turbulent structures in the airflow that
could provide spray coverage of both sides of the target leaves. Therefore, to enhance the
beneficial effects of local turbulence, and to induce leaf movement whilst retaining the
small size of the spray unit, a rotating screen to generate airflow pulses with discrete
peaks in velocity was added and tested. Experiments on the close-range spraying of
young grapevine plants using the rotating airflow screen were performed. A high-speed
camera, image analysis system and water sensitive papers were used for analysis of the
spraying. Natural frequencies of individual leaves showed sharp fluctuations at discrete
frequencies and single leaf fluctuations of root mean square velocity corresponded well to
the pulsating airflow. Spraying was evaluated as percentage spray coverage and number
of droplet impacts. Spray coverage of front side of leaves (facing the sprayer) was good,
but coverage of back of the leaves was limited.
Keywords
precision spraying; agricultural robotics; airflow; plant motion; movement of leaves;
spray deposition
Nomenclature
Abbreviations
CROPS Clever Robots for Crops
RMS Root Mean Square
SEEF Spraying End Effector
WSP Water Sensitive Paper
Symbols
Umean mean airflow velocity / (m s-1)
URMS root mean square airflow velocity / (m s-1)
Tu turbulence level / %
Ui airflow velocity / (m s-1)
N number of measurements
C(i,j) correlation at position (i,j)
f(x,y) image at position (x,y)
w(x,y) sub image at position (x,y)
M width of image f / pt
N height of image f / pt
K width of sub image w / pt
L height of sub image w / pt
R normalised correlation coefficient
w
average value of grey level of all points in the sub image w
f
average value of grey level of all points in the image f
1. Introduction
Application of agrochemicals is at present the method most used to protect plants from
diseases, pests and weeds (Oerke et al., 2003). To do this, pesticide formulations are
diluted in water and distributed over the vegetation in form of sprays. To protect plants
from diseases and pests, agrochemicals are sprayed uniformly to ensure coverage of
susceptible targets at the appropriate time in the season. In orchards, grapevines and
greenhouses susceptible targets (fruits, bunches, new sprouts, younger leaves, etc.) can be
located anywhere in the vegetation, consequently current spraying techniques aims to
cover all parts of plants, front and behind, top and bottom, as well as within the canopy.
As a result, high volume air-flow has been used to assist the transport and deposition of
pesticide droplets the innermost parts of the canopy. Coarse spray can runoff from leaf
surface or fail to deposit before reaching the target, whilst buoyancy can cause fine spray
to drift away from target, with uncontrolled diffusion to soil and air. When treating plants
with sparse canopy, a portion of the spray can travel through the foliage without being
impacted. Thus a certain amount of pesticide can go off-target, with significant negative
effects on production costs, impact on the environment and the quality of the produce
(Otto et al., 2013; Jong et al., 2008; Cunha J.P. et al., 2012).
Current robotic technologies can be applied to crop protection (Mulla, 2013) enabling the
possibility of precise and selective targeting of the spray (Khot et al., 2012; Zaman et
al.,2011; Esau et al., 2014). This represents one of the most promising options for
reducing the amount of used pesticide, whilst maintaining crop-protection efficiency.
The concept of precise application of pesticides also involves the possibility of real-time
adjustments of spraying application to the local needs of the target (plant, or part of the
plant) on which the treatment is being applied (West et al., 2003; Andújar et al., 2012).
Hence, there is a need to develop and introduce techniques and systems for disease
detection and pesticide distribution (Dekeyser et al., 2013) which are able to optimise the
spot-application of pesticides according to the specific characteristics of the target, such
as disease susceptibility, or the presence of infection symptoms.
In the broad field of agricultural robotics, research work is focused on the development
and validation of intelligent and selective agricultural robots with crops-care capabilities
by integrated use of cutting-edge robotics and further advancing of sensing technologies
(Bontsema et al., 2014). Among these robots, a novel robotic sprayer may have a modular
architecture, enabling flexible, adaptive and coordinated operation of multiple spraying
units, giving to the machine the unprecedented capabilities of continuously adapting
pesticide spraying pattern to the crop-canopy characteristics (as volume and foliage
density), as well as selectively spot spraying only selected targets (as disease foci or fruits
to be protected) (Oberti et al., 2014). Rapid optical detection of disease is essential for
precision spraying (West et al., 2003).
Here a new technique of spraying is introduced. For close range precision spraying small
patches of disease are required to be treated during their early development. For this a
close range precision application a spraying end effector (SEEF) is required. In the
following an SEEF design will be presented and measurements of the properties of the
airflow around the plant will be investigated. The compatibility of close range precision
spraying with emerging robotic technology as part of the development of precision
agriculture will be investigated.
1.2. Flow aerodynamics around plants and leaves
In the context of spraying tree and bush crops, airflow from an air-assisted sprayer carries
pesticide spray towards its target and provides pressure to the surface of leaves and
branches. The main goal is to establish flow conditions in canopies, required for good
pesticide application (Endalew et al., 2010).
Large coherent structures can form under such flow conditions, enabling good
penetration of the spray and interaction with the plant. These structures manifest
themselves as airflow with constantly changing velocity and direction. To some extent,
large coherent structures are responsible for flux of pesticide droplets to the backs of
leaves (Sánchez-Hermosilla et al., 2012) but plant movement also increases the
probability of spray droplets impacting these areas (Pujol et al., 2013).
To be effective against early discrete disease foci, and not causing excessive pesticide
consumption, the size of the spray plume should be approximately of the same size as the
disease foci. For close-range precision spot spraying of an infected grapevine leaves the
size of coherent structures should be around 150 mm in diameter (Ash, 2000). However,
compared to conventional spraying, this size is too small to effectively disperse spray
around plant leaves and deposit it on rear surfaces. Also, plant motion is limited by the
size of leaves; at best small branches and individual leaves may be exposed to the SEEF
airflow. Displacement and rotation of leaves, sprayed with SEEF, is likely to be more
limited in compared with conventional spraying.
2. Close-range spraying end effector (SEEF)
The SEEF was designed such that pesticide could be locally applied to the position of the
disease foci. The SEEF consisted of the following components: an airflow generator
(axial fan), an airflow nozzle, a pesticide nozzle, a pesticide pump, electrical connections
for power supply and control signals, a pesticide connection and a chassis. A schematic
diagram of the SEEF is shown in Fig. 1.
2.1. SEEF design
The SEEF was designed to enable connection to robotic agricultural systems, such that
developed under CROPS EU project (Bontsema et al., 2014). The SEEF was of
lightweight construction since it was intended for installation on robotic arms.
A voltage regulator was used for regulation of rotational speed of axial fan. Air flow
velocity at target distance from 0.3 m to 1.2 m was adjustable from 5 m s-1 to 15 m s-1.
Fig. 1. Close range spraying end effector. 1. Axial fan, 2. Flow straightener, 3. Airflow
diffuser, 4. Pesticide nozzle, 5. Pesticide valve, 6. SEEF case with voltage regulator and
pesticide nozzle switch
Airflow was generated by a 119 mm 90W axial fan (4118 N/2H7P, Ebmpapst,
Mulfingen, Germany). The tangential component of the airflow was transformed to an
axial direction with flow straightener with inlet angle 19.5 °. The diameter of the
aerodynamic diffuser varied from 116 mm to 80 mm, an angle of 9.5 ° and length 120
mm.
Pesticide was supplied to the unit by a diaphragm pump (SHURFLO 8000, PENTAIR,
Worsley, UK) which was located with the pesticide reservoir in a control box outside the
SEEF. The pesticide nozzle used was a full cone type with 30 ° flow angle (Type S 0.5
30 °, STEINEN, Parsippany, NJ, USA). The normal operating pressure of the circuit was
360 kPa. The pesticide volume flow rate at selected pressure was 1.0 l min-1 which was
turned on / off and regulated by an electromagnetic valve.
2.2. Airflow structure
The airflow at the exit from aerodynamic nozzle of SEEF should contain only small
tangential and radial velocity components since both components cause expansion of the
airflow with the increasing distance from the SEEF. Unwanted expansion of the airflow
prevents spot spraying of diseased plant parts occurring particularly for targets located far
away from the SEEF and deep within the canopy. To reduce airflow expansion, the SEEF
was equipped with flow straightener and a conical aerodynamic nozzle as described in
section 2. However, the use of a flow straightener and conical aerodynamic nozzle reduce
turbulence in the airflow and prevents the formation of large coherent structures. Lack of
large coherent structures decreases probability of spray depositing on the back of leaves.
In general, large coherent structures feature low frequencies, while small coherent
structures feature high frequencies. Large coherent structures, with their low frequency
can coincide with the natural frequencies of branches and leaves and may produce
increased plant movement causing the back of leaves to be exposed to spray.
As a possible remedy to the expected limited deposition on the back of leaves, pulsations
in the airflow were deliberately introduced via a rotating screen. The rotating screen
operated as a device that alternately stopped and allowed the airflow to pass through.
Such arrangement was considered able to produce high airflow pulsations without
significantly expanding the spray plume. Measurements of the airflow from the SEEF
using the rotating screen are presented in the next section.
3. Measurement and analysis methods
Three measurement techniques were used to evaluate the operation of the SEEF: (1)
aerodynamics measurements using hot-wire anemometry, (2) spray coverage and
determination of the number of spray impacts using by water sensitive papers and (3)
measurement the motion of plants and leaves using high speed imaging and analysis.
They are described more in detail in the following subsections.
Pulsations of the airflow were achieved using a rotating circular airflow screen with four
unevenly distributed openings. The diameter of each opening was 80 mm and the airflow
screen was rotated by an electric motor, driven by a variable frequency drive.
Fig. 2. Measurement setup for SEEF airflow properties measurements with hot-wire
anemometry, left: Rear view, right: Right side view; (1) SEEF, (2) electric motor with
rotating airflow screen, (3) electric motor variable drive, (4) personal computer with
multifunctional data acquisition board, (5) signal conditioner, (6) constant temperature
anemometer and (7) hot wire sensor.
Fig. 3. Measurement setup for plant and leaves motion analysis, left: Rear view, right:
Right side view; (1) SEEF, (2) electric motor with rotating airflow screen, (3) electric
motor variable drive, (4) personal computer, (5) pesticide pump, (6) plant, (7) high speed
camera with lens and (8) water sensitive papers.
3.1. SEEF aerodynamic measurements
To establish aerodynamic properties of the SEEF, instantaneous velocity measurements
were performed using hot-wire anemometry. The experimental setup for measurement of
SEEF airflow properties is shown in Fig. 2. A hot wire anemometer (Mini CTA, Dantec,
Denmark) with straight single wire sensor (55P11, Dantec, Skovlunde, Denmark) was
used. The sensor wire diameter was 5 μm and length was 1.25 mm. Signal conditioning
was performed using a 10 kHz Bessel filtering with an AC powered chassis (SCXI 1000,
National Instruments Corp., Austin, TX, USA), an 8-channel isolation amplifier (SCXI-
1120, National Instruments Corp.) and a BNC connection box (SCXI-1305, National
Instruments Corp.). Data acquisition unit used a 16 bit A/D measurement board (NI 6212,
National Instruments Corp.). Sampling frequency was 20 kHz and sampling interval was
10 s. Calibration and measurement were carried out according to procedure of Bruun
(1995) and Jørgensen (2005). For measurement of flow temperature, a 4 wire Pt100 type
A temperature sensor and a data acquisition unit (Agilent 34970A, Agilent technologies,
Santa Clara, CA, USA) were used.
During measurements of aerodynamic flow properties SEEF spray nozzle was not
operated. The SEEF was located at the same height and axis as hot wire anemometer
probe and the probe was mounted with its hot-wire perpendicular to the direction of the
main flow. The distance between SEEF nozzle and the hot-wire anemometer probe varied
according to different operation points as shown in Table 1, being 0.7 m, 0.5 m and 0.3
m. Mean airflow velocity Umean, root mean square (RMS) airflow velocity URMS and
turbulence level Tu were calculated from the measurements according to guidelines in
(Jørgensen, 2002). The RMS velocity was used as a measure for leaf velocity
fluctuations, since the average velocity of leaves is zero, because leaves are attached to
the plant.
Mean airflow velocity:
Eq. 1
Airflow velocity root mean square (RMS) (Jørgensen, 2002):
Eq. 2
Turbulence level:
Eq. 3
Where Ui is airflow velocity and N is number of measurements.
3.2. Spray coverage and number of impacts
Within the grapevine (Vitis vinifera L.), two positions on both sides of the leaves were
selected for an analysis of spray coverage and deposition as shown in Fig. 4. To analyse
spray deposit Water Sensitive Papers (WSP, QUANTIFOIL Instruments GmbH, Jena,
Germany) of size 75 mm x 26 mm were attached to the plants. The number of spray
droplet spots and the percentage of coverage were evaluated for each WSP. Spray deposit
and coverage of the droplets from different sprayings was quantified using WSP analysis.
For each analysis, WSP were placed at equivalent places (Porras-Soriano et al., 2005) and
were attached to the selected leaves on both surfaces at fixed positions. They were
collected approximately 10 min after they were completely dry. Spot-spraying time was
set to 1 s, corresponding to a realistic application time and spraying was carried out using
mains water. Measurements were performed indoors in a laboratory. Air temperature
during measurements was 22 °C and relative humidity was around 50 %.
After each experiment, the plant was allowed to dry completely and the WSPs were
carefully detached and stored in sealed labelled plastic bags for subsequent imaging.
Colour digital images of WSPs were acquired using a digital scanner at 1200 dpi,
resulting in a nominal resolution of 21 µm pixel-1.
The obtained RGB images were processed by custom software written in Matlab
(R2013a, the MathWorks Inc., Natick, MA, USA) to obtain quantitative descriptors of
spray deposit on the target. To this aim, the green channel of the WSP image was
extracted (B, Fig.4) obtaining a sharply bimodal distribution of grey levels,
corresponding to background pixels (very high grey levels) and drops trace pixels (very
low grey levels) respectively.
Given the constant settings of the digital scanner, a constant threshold value (170 on 255)
was applied to segment droplet pixels from their background in all the WSPs collected in
the experiments (C, Fig.4).
Fig. 4. From the RGB image (A) the green channel of the WSP image was extracted (B) a
sharp bimodal distribution of pixels grey levels was obtained which corresponded to the
sensitive paper background and adsorbed droplets respectively. Given the fixed settings
of the digital scanner, a constant threshold value (170 from 255) was applied to segment
droplets pixels (C) and filtered with a morphological operation to remove noise pixels
(D).
The obtained binary image was then filtered with a morphological opening operator (D,
Fig.4) to remove noise pixels from background and from the border of drops regions. The
resulting nominal resolution of images was then degraded to 42 × 42 µm2
The pixel regions retained after the processing (D, Fig.4) were then assumed to
correspond to spray deposit on the WSP. By determining their quantity, dimensions, and
spatial distribution, it was possible to characterise the spray deposit on the target.
For example the red squared frame in Fig. 4.A resulted to be covered by spray drops on
the 3.7% of its area, with an average number of impacts of 62 drops cm-2. The drops
population had a volume median diameter of 241 µm.
3.3. Plant and leaves motion analysis by image analysis
The camera used for image acquisition of plant motion was FASTEC HISPEC 4 (Fastec
Imaging Corp., San Diego, CA, USA). The camera operated at a resolution 800×858
pixel at 150 frames s-1 and was positioned above and slightly behind the SEEF. The
number of acquired images in each series was 5000. A Nikkor 50 mm f/1.2 lens (Nikon,
Tokyo, Japan) was used with the camera. A dark background was used for easier
separation of the plant from the background. The plant was illuminated from two sides
with 4 led lights CREE XM-L T5 (CREE Inc., Durham, NC, USA) in a row that were 25
mm apart, placed on each side at distance 0.8 m between them and 0.5 m from the plant.
LED lights were powered by a DC current source to ensure the continuous illumination.
During visualisation, leaves were spatially variable illuminated or screened by
neighbouring leaves. Simple general or local intensity thresholding was inadequate to
separate plant from the background. A combination of edge detection and morphology
operations (Fig. 5) was therefore selected. Custom software program for image analysis
A
B C D
A
B C D
was used, which was written in LabVIEW (National Instruments Corp.) using the Vision
Development Module library.
Edge detection was performed using Sobel filtering method followed by outlying particle
removal using erosion algorithm (Fig. 5C). For erosion, all objects in image were kept
that were resistant to the specified number of erosions. Kept objects were rendered to the
shape and size the same as before erosion algorithm. Best results were achieved using
two consecutive erosions. After particle removal algorithm, holes were filled as shown in
Fig. 5D.
A B C D E
Fig. 5. Sample analysis of plant leaves velocity. (A) first image of the image pair. (B)
second image of the image pair, (C) first image after edge detection, (D) first image with
closed holes, (E) velocity vectors overlaid over the first image of the image pair.
A normalised cross correlation method was used for matching of patterns for
displacement evaluation on series of images. Leaves are objects that change shape,
rotation and to some extent also size, when they move in the spray plume both closer and
further away from the camera.
A sub image w(x,y) of size K × L, is located inside image f(x,y) of size M x N. Here K ≤
M and L ≤ N (Fig. 6). Correlation among w(x,y) and f(x,y) in location (i, j) is given by the
equation
Eq. 4
where i = 0,1...M-1
j = 0,1...N-1,
The summation is in the region, where images w and f overlap. Sub image w moves over
entire image and C is calculated according to Eq. 4 for every point in the image. The
highest value of function C in the image denotes place, where sub image w corresponds
the most to the image f.
Fig. 6. Principle of cross correlation displacement measurement.
The correlation method is susceptible to changes in image grey level (illumination) in the
image f and sub image w. Therefore the normalised correlation coefficient R was used
according to the equation
Eq. 5
Here
w
is average value of grey level of all points in sub image w and
f
is average value
of f in the image. Value of R is in the interval from -1 and 1 and is independent of change
of illumination of f and w.
Displacement estimation was performed in a selected matrix of locations. A template of
selected size was extracted from the first image for every selected location. The template
size 45×45 pixels was used. Among the locations in the second image, the one with the
highest similarity was considered where the distance from the template was smaller than
a selected value. The lowest allowed similarity limit was selected to reject locations with
poor matching. Subpixel accuracy and shift invariant search were performed, meaning
that in a selected position of the search the template was not rotated.
From displacements and time difference, when two consecutive images were recorded,
velocities in x and y direction were calculated (Fig. 5 E).
3.4. Operational set-points for spray coverage and number of impacts
measurements
Several operating conditions were explored during the experiments for measurements of
spray coverage, number of impacts and plant and leaves motion with high speed imaging
as shown in Table 1. Spraying and imaging was done simultaneously. In operational set-
point 1 spraying was carried out twice on different plants and in operational set-point 2
spraying was carried out three times.
Operational
set-point
Repetitions
Distance
[m]
Air velocity
at the location
of target [ms-1]
Pulsation
frequency
[Hz]
Spray
1
2
0.3
6.1
1.2
yes
2
3
0.5
7.5
2
yes
3
1
0.7
7.5
2
no
Table 1. Selection of operational-set points.
4. Results and discussion
The results of aerodynamic operation of SEEF are presented in section 4.1, the results of
the spraying with SEEF in section 4.2 and the results of measurements of plant
movement are presented in section 4.3.
4.1. Results of aerodynamic measurements
Figure 7 shows a decrease of axial velocity with increasing distance from the SEEF.
Velocity is presented for the two settings of the fan rotational speed, one producing
airflow with an average velocity of 10 m s-1 and the other 7.5 m s-1 both measured at a
distance of 0.3 m from the SEEF.
Fig. 7. Relationship between distance from SEEF and airflow axial velocity.
Measurements of aerodynamic properties of SEEF were performed in the absence of the
plant according to experimental setup shown in Fig. 2. Results are shown as turbulence
levels and spectra of velocity fluctuations. Figure 8 shows power spectrum of velocity
fluctuations for all selected operational-set points. Experimental results show that number
of discrete peaks in velocity fluctuations decreases with distance of the measurement
location from SEEF, number of discrete peaks in velocity fluctuations being the highest
for operational-set points 1 (Fig. 8.A, distance 0.3 m), and the lowest in operational-set
point 3 (Fig. 8C, distance 0.7 m).
Selection of rotating screen with four unevenly distributed openings resulted in
generation of the airflow with several independent frequency peaks of velocity
fluctuations. In the operational-set point 1, four highest peaks of velocity fluctuations are
at frequencies 1.2 Hz, 2.4 Hz, 3.5 Hz and 4.7 Hz. In the operational-set point 2 one high
peak of velocity fluctuations is at frequency 2 Hz.
The airflow from the SEEF without the rotating screen contains only limited flow
fluctuations. Since the generation of large velocity fluctuations and associated coherent
structures occurs at the location of the rotating screen, then with increasing distance from
0
2
4
6
8
10
12
0 0.2 0.4 0.6 0.8 1 1.2 1.4
velocity [ms-1]
distance from SEEF [m]
7,5ms⁻¹@0,3m
10ms⁻¹@0,3m
the SEEF the discrete structures decay and a more coherent airflow occurs. At greater
distances from SEEF (operational-set point 3), discrete peaks of velocity fluctuations are
not discernible.
A
B
C
Fig. 8. Airflow velocity power spectrum at location of the target at distances (A) 0.3 m,
(B) 0.5 m and (C) 0.7 m from the SEEF.
Turbulence levels are shown in Table 2. Turbulence levels in the case of operational-set
point 1 (distance 0.3 m) were 66%, while in the case of operational-set point 2 (distance
0.5 m) turbulence levels were 52% and in operational-set point 3 (distance 0.7 m)
turbulence level was 67%.
4.2. Results of spray coverage and number of impacts measurements
0.0
0.2
0.4
0.6
0.8
0 5 10 15
velocity amplitude [ms-1]
frequency [Hz]
Distance from SEEF 0.3 m
0.0
0.2
0.4
0.6
0.8
0 5 10 15
velocity amplitude [ms-1]
frequency [Hz]
Distance from SEEF 0.5 m
0.0
0.2
0.4
0.6
0.8
0 5 10 15
velocity amplitude [ms-1]
frequency [Hz]
Distance from SEEF 0.7 m
The selection of operational set-points to achieve good spray coverage and the maximum
number of impacts (drops cm-2) is shown in subsection 3.4. Figure 9 show an effective
spray diameter around 150 mm at distance 0.3 m and air velocity 7.5 m s-1. Results of
coverage and droplet density are shown in Table 2. Droplet density on front side of the
leaves was good. At the operational-set point 1 and repetition 1, spray coverage on right
leaf was excessive (94.4 %) and the spray droplets were not distinct. The Syngenta
(2002) sets lower limit for droplet density and in addition an interval of acceptable
droplet densities. According to Syngenta (2002) and Cunha (2012), the minimum droplet
density in the target area should be no less than 20 to 30 drops cm-2 for insecticide or pre-
emergence herbicide applications, 30 to 40 drops cm-2 for contact post-emergence
herbicides, and 50 to 70 drops cm-2 for fungicide applications. For spray droplet volume
mean diameter 200 µm Syngenta (2002) sets the range of acceptable droplet densities
from 129 drops cm-2 to 258 drops cm-2.
In our case the droplet density on the back of the leaves was <40 drops cm-2 except at the
operational-set point 1 and repetition 2, where on one back side of the leaf the droplet
density reached 118 drops cm-2 whilst on the other it was 61 drops cm-2. This is around or
less than minimum droplet density in the target area (Syngenta, 2002) and less than
interval of acceptable droplet densities (Syngenta, 2002). In our case the spray droplet
volume mean diameter was 167 µm with standard deviation 40µm
Limited movement of the plant leaves, combined with small turbulent structures in the
airflow, essentially prohibited spray being deposited on both sides of leaves. As a result,
when spraying by SEEF, only front sides of the leaves were sprayed.
Fig. 9. Effective spray diameter at distance 0.3 m and air velocity 7.5 m s-1 shown by
water sensitive paper.
4.3. Results of plant and leaves motion analysis by image analysis
Introduction to image analysis procedure used was provided in subsection 3.3. Average
leaf velocity for the entire plant was calculated for each operation point as an average of
all measured velocities on all images. Similarly leaf RMS velocity was calculated as a
RMS of all measured velocities on all images.
Average RMS velocity vectors are overlaid on representing image for each operation
point (Fig. 8). Velocity vectors are shown in red colour. Intensity of the colour represents
percent of successfully calculated velocities at certain point. The origin of the coordinate
system was in the top left corner of the domain. Since the RMS values of velocity
fluctuations were always positive and velocity vectors were calculated as a RMS value of
individual velocities of the complete sequence the vectors always points down and to the
right. Using this visualisation method only velocities in x and y directions can be
measured so velocity information about depth (z direction) is not available.
Fig. 10. Sample Average RMS velocity vectors. The location of water sensitive papers
can also be seen.
Average leaf velocity is close to zero, because leaves fluctuate around their equilibrium
position. Leaf RMS velocity plots feature two distinctive intervals of velocities that
correspond to two distances between SEEF and the plant. In the case of operational-set
point 1 (distance 0.3 m) the RMS displacement was larger than for operational-set point 2
(distance 0.5 cm).
Leaf RMS velocity of fluctuations corresponded well with airflow turbulence levels for
all operational-set points. In the case of operational-set point 2 (turbulence level 52%)
leaf RMS velocity was from 0.157 m s-1 to 0.193 m s-1. In the case of operational-set
point 1 (turbulence level 66 %) leaf RMS velocity was 0.243 m s-1 and 0.346 m s-1.
Figure 11 shows frequency of fluctuations of leaves for operational-set point 3, repetition
1, left leaf, operational-set point 2, repetition 2, left leaf and operational-set point 1,
repetition 2, left leaf. The frequency of fluctuations of leaves had narrow peaks of
fluctuations around discrete frequencies. The highest peaks of fluctuations are in the
region from 2 to 8 Hz. This value corresponds to the frequency interval of fluctuations of
airflow from SEEF, as shown in Fig. 8.
Velocity was only measured in positions where the plant was present. However, plants
were not present at every location for the complete sequence. Such situations occur at the
edges of plant. In these positions the RMS velocity is shown in white for the hypothetical
case of only one successfully calculated velocity in the sequence. Positions, where plant
was present in all image pairs in the sequence, and velocity was successfully calculated,
are shown in dark red. All other positions have colours between white and red, depending
on the proportion of successfully calculated velocities. The many positions where
velocity was successfully calculated only in few cases were on the edges of plants. Here
RMS velocity is usually very high, as can be seen in Fig. 10.
5. Discussion
Our understanding of precision spraying with the SEEF is the following. The requirement
for precision spraying requires a controlled airflow without noteworthy flow fluctuations
(in the form of large coherent structures like vortices); otherwise, the airflow fluctuations
(high flow turbulence levels) disturb the airflow and prevent precise delivery of the spray.
Due to the limited size of the sprayed area the focus is on the movement of single leaves
instead of branches. At the location of a targeted leaf, the airflow from a SEEF acts on
the leaf with a constant force which moves the leaf from its equilibrium position to an
extreme point, causing bending. Many spray droplet impacts occur on the front of the
leaf with very few occurring at the back of the leaf (see Table 2). This situation changes
only slightly when airflow pulsations are introduced. The airflow acts on the leaf with an
intermittent force, but the force direction and size remain essentially the same. In a very
simplified view, leaf is excited by intermittent force and thus fluctuates from its
equilibrium point to both maxima. However, as results in Table 2 show, spray reaching
the back of the leaf side is still limited. We believe that the very limited spray reaching
the back of the leaves with the SEEF is due to the two factors: (1) the absence of large
coherent structures within the spraying airflow carrying spray around plant leaves and (2)
the small size of the SEEF not providing large coherent flow structures. The situation is,
however, different with the large conventional orchard and vineyard sprayers currently on
the market which are able to transport spray around leaves and provide large velocity
fluctuation in leaves.
Operati
onal -
set
point
Repeti
tion
average
leaf
velocity
[m s-1]
leaf
RMS
velocity
[m s-1]
turbu-
lence
level
[%]
spray
coverage
front side
[%]
spray
coverage
back side
[%]
droplet
density
front side
[drops cm-2]
droplet
density
back side
[drops cm-2]
left
right
left
right
left
right
left
right
1
1
0.029
0.346
66
48.8
94.4
0.2
0.2
138
n.a.
8
7
2
0.0052
0.243
31.5
13.9
9.5
2.1
133
177
118
61
2
1
0.0058
0.168
52
42.8
27.0
0.5
0.7
157
218
23
38
2
0.0073
0.193
50.7
39.6
0.7
0.2
130
190
39
10
3
0.0014
0.157
16.9
53.4
0.3
0.4
190
99
14
28
3
1
0.028
0.328
67
Table 2. Integral parameters of spraying. In the first column of the table, right front of the
leaf, the WSP was almost saturated and droplet images were not distinct.
Fig. 11. Frequency analysis of fluctuation of leaves. Frequency was calculated in selected
point. Darker line relates to the horizontal component of velocity and the lighter line to
vertical component of velocity.
5. Conclusions
The SEEF effector was evaluated for a close range precision spraying process in
vineyards. It was found to achieve air flow velocities up to 10 m s-1 at distance 0.3 m
within diameter around 150 mm. The SEEF was equipped with rotating airflow screen
which induced discrete frequency peaks of velocity fluctuations. Measurement of natural
frequencies of displacement of leaves in the airflow showed that leaves fluctuate with
discrete frequencies. Spraying of front side of leaves surfaces was good, while spraying
of back side surfaces should be further improved. Results point to an important drawback
of future robotic high precision spraying applications. Such future robotic high precision
0.0
1.0
2.0
3.0
4.0
5.0
0246810 12 14 16 18 20
velocity amplitude [ms-1]
frequency [Hz]
operational-set point 1, repetititon 2, left leaf
0.0
1.0
2.0
3.0
4.0
5.0
0246810 12 14 16 18 20
velocity amplitude [ms-1]
frequency [Hz]
operational-set point 2, repetition 2, left leaf
0.0
1.0
2.0
3.0
4.0
5.0
0246810 12 14 16 18 20
velocity amplitude [ms-1]
frequency [Hz]
operational-set point 3, repetition 1, left leaf
spraying applications will require positioning of spraying arms and the spraying of
disease foci in bush and tree crops from both sides of the plantation row.
6. Acknowledgement
This work was funded by the European Commission in the 7th Framework Programme
(CROPS GA no 246252).
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