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Estimation of liquid deposition on corn plants sprayed from a drone

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Laboratory results of spraying maize plants using a multi-rotor drone are presented. The XR11001 flat fan sprayer from TeeJet was used for the tests. The liquid pressure in installation was 0.2 MPa. The height of corn plants was 1.6 m and 0.9 m above the soil surface. The drone was equipped with electric motors DJI 4114, kV-400 and propellers with dimensions 15 x 2.2". The influence of the air stream produced by the drone rotors and the plant height on the plants sprayed from the drone spray stream was investigated. The research showed the dependence of the distribution of the deposited liquid on individual parts of plants from plant height and air flow.
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1RYHPEHU 201, Brno, Czech Republic
Estimation of liquid deposition on corn plants sprayed from
a drone
Boguslawa Berner, Aleksandra Pachuta, Jerzy Chojnacki
Department of Authomatic, Mechanics & Construction
Koszalin University of Technology
Racławicka 1517, 75620 Koszalin
POLAND
boguslawa.berner@tu.koszalin.pl
Abstract: Laboratory results of spraying maize plants using a multi-rotor drone are presented. The
XR11001 flat fan sprayer from TeeJet was used for the tests. The liquid pressure in installation was
0.2 MPa. The height of corn plants was 1.6 m and 0.9 m above the soil surface. The drone was equipped
with electric motors DJI 4114, kV 400 and propellers with dimensions 15 x 2.2". The influence of the
air stream produced by the drone rotors and the plant height on the plants sprayed from the drone spray
stream was investigated. The research showed the dependence of the distribution of the deposited liquid
on individual parts of plants from plant height and air flow.
Key Words: UAV, spraying plants, corn, liquid deposition, drone
INTRODUCTION
Unmanned aerial vehicles (UAVs) are used in agriculture primarily as instruments to acquire
information about the rural environment (Pascuzzi et al. 2018), about the fertilization demand of plants
(Mazur and Chojnacki 2017), the level of weed infestation and even about existing plant parasites (Tetila
et al. 2017). Drones are currently able to perform some agrotechnical operations on crops. They can be
used to spray plants with plant protection products, both chemical and biological (Berner and Chojnacki
2017a), spread fertilizers, sow seeds and plant plants. The advantage of using drones in field works is
the lack of kneading soil and plants, the ability to work on hard-to-reach areas, especially due to the
diversification of the terrain or the wetness of the ground. The drones enable quick movement over field
crops, and enable treatments at different heights, directly above the plants (Faal et al. 2017).
Spraying drones can either perform autonomously, according to a previously planned flight route,
or the route of their flight and treatment site can be controlled by the drone operator by means of
a transmitter, in real time (Yallappa et al. 2017, Huang et al. 2009, Xue et al. 2016). The interest in using
drones in plant protection is also growing due to the innovativeness of this solution, which in the future
may contribute to full robotisation of field works (Mogili and Deepak 2018).
Drones airplanes resembling airplanes can be used for spraying plants, but the most common
application for field works has been drones with rotor construction, with rotating blades of propellers
keeping them in the air (Berner and Chojnacki 2017b, Wei-Cai et al. 2016, Zhou and He 2016, Giles
and Billing 2015). Research on the use of unmanned aerial vehicles in plant protection focuses mainly
on the assessment of the quality of treatments performed with their help. To assess the treatments with
drones can be used probes fixed on plants, spread over several levels of plants. They allow the
assessment of the degree of liquid penetration in the plant canopy. On the basis of traces of droplets on
the probes one can estimate their size and uniformity of distribution, and even the capacity of the
deposited liquid (Wei-Cai et al. 2016, Berner and Chojnacki 2017b, Zhou and He 2016). The quality of
the distribution of liquid plants can depend on the amount of spraying and the shape of plants as well as
the accuracy of the drone moving over the plants (Tang et al. 2018, Berner and Chojnacki 2017b). In
addition to the use of probes attached to the plants, a biological assessment is also carried out (Qin et al.
2018).
Spraying plants with drones can become common in the future, provided that the effectiveness of
treatments performed with them will not be worse, and even better than treatments carried out with the
use of ground equipment.
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1RYHPEHU 201, Brno, Czech Republic
MATERIAL AND METHODS
The aim of the study was to assess the impact of the air stream produced by drone rotors and plant
heights, on the distribution of sprayed plants from a drone.
The tests were carried out in the laboratory hall. On the high mounted treadmill was a trolley,
moved by a pull rod pulled by an electric motor. The drone has been mounted to the trolley from below.
It was a S900 hexacopter, manufactured by DJI. The drone was equipped with electric motors DJI 4114,
kV 400 and propellers with dimensions 15 x 2.2". Under the rotor of the drone, on the jib attached to
the frame of the drone, at a distance of 0.4 m from the lower surface of the propellers, a flat stream
sprayFer XR11001 from TeeJet was installed. The liquid to the sprayer was supplied from an external
power source. The pressure of the liquid in the atomizer was constant and amounted to 0.2 MPa. The
nozzle has been positioned so that the fan of the spray jet was directed transversely to the direction of
travel of the cart with the drone. The travel speed of the trolley on the frame race was set to 1.2 m/s and
did not change during the tests. The rotational speed of all drone rotors was constant and amounted to
580 rad/s, which corresponded to the power of the drone, equal to 90 N. The rotation of the drone rotor
was controlled by means of an optical tachometer mounted on a drone and connected via a USB
connection to a computer.
Corn plants in phase 77 according to BBCH, bred in outdoor boxes were selected for research.
All plants were shortened to an equal level 1.6 m above the ground level in containers, which resulted
in a height of 1.8 m above the floor surface in the laboratory. In the second phase of research, the plants
were shortened, cutting the lower part of the plant's momentum, to 0.9 m above ground level. The boxes
were set up in the laboratory under the frame with the trolley. The spacing of plants in a row transverse
to the direction of travel of the trolley was 0.40 m and in a row parallel to the direction of movement of
the trolley it was equal 0.70 m. The plant setting is shown in the figure 1.
Figure 1 Stand setting
Legend: 1 tachometer, 2 nozzle, 3 samplers
On three levels of plants there are placed probes with dimensions 0.02 x 0.04 m made of
aluminium foil, 5 pieces on each level. Samplers were always glued on the same plant, in the same
places for the same height of the plant. Samplers were placed on the top layer of the plant, on the middle
layer and the lower layer 20 cm above the soil surface. In the case of tall plants, the central level was
0.9 m in the case of shortened plants 0.55 m above the soil level.
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1RYHPEHU 201, Brno, Czech Republic
The experiments were performed with rotating rotors and without rotating the drone's rotors,
repeating each measurement three times. The probes were located on the central plant positioned exactly
under the axis of the sprayer's symmetry. As the spray liquid, water stained with washable water
nigrozine was used at a concentration of 0.5%. After the plants dried, the sensors were peeled off the
plants and stored in sealed containers.
After completing the tests, nigrozine was washed from the probes using distilled water of
indicated capacity. The concentration of the dye, which was proportional to the mass of the deposited
spray liquid, was determined by means of a spectrophotometer.
RESULTS
The average results of the research of the deposited liquid on the probes are presented on figure
2 for tall plants and for low plants are on figure 3. They actually represent the concentrations of the
washed dye from the surface of the probes, which are proportional to the amount of liquid deposited on
them.
Figure 2 Liquid distribution on tall plants
Legend: level of samplers on plant:
A top, B middle, C lower, LSD
Least Significant Difference
Using the analysis of variance, the significance of the influence of plant height and air blowing
on the liquid deposition on selected plant levels was determined. Analysis of variance in which the
factors were:
level of samplers placement,
plant height,
rotors work.
Figure 3 Liquid distribution on low plants
Legend: level of samplers on plant:
A top, B middle, C lower,
LSD Least Significant Difference
It showed the significance of the impact of plant height and rotors work for liquid deposition on
corn plants with a significance level of less than 0.05. The value of the Least Significant Difference
0.00037
0.00030
0.00002
0.00043
0.00037
0.00010
0.00004
0.00000
0.00005
0.00010
0.00015
0.00020
0.00025
0.00030
0.00035
0.00040
0.00045
A B C LSD
without rotors r otation with rotors rotation
0.00037
0.00019 0.00015
0.00041
0.00028
0.00017
0.00004
0.00000
0.00005
0.00010
0.00015
0.00020
0.00025
0.00030
0.00035
0.00040
0.00045
A B C LSD
without rotors r otation with rotor s rotation
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1RYHPEHU 201, Brno, Czech Republic
(LSD) was also calculated. This value is shown on plots (see Figures 2 and 3). By comparing its value
to the results, it can be noticed that no significant influence of the air stream on changes in the capacity
of the deposited liquid on the samplers placed at the level C of low plants was found.
Based on the data obtained from the measurements, a plots of the percentage of distribution of
the liquid on individual plant levels were made according to the formula described in the formula 1 (see
Figures 4 and 5). =100
[%] (1)
Where:
Pi share of the liquid capacity deposited on the sampler on i this level in relation to the
liquid capacity deposited at all levels, %
vi liquid capacity deposited on the sampler at i this level.
Figure 4 Percentage of distribution of the liquid on individual samplers levels (tall plants)
Legend: level of samplers on plant:
A top, B middle, C lower
Plots of the percentage distribution of liquid at individual plant levels show the relative relations between
the plant height and the uniformity of liquid deposition on them.
Figure 5 Percentage of distribution of the liquid on individual samplers levels (low plants)
Legend: level of samplers on plant: A
top, B middle, C lower
CONCLUSION
The test results indicated that there is a significant effect of plant height and air flow on the
distribution of liquid on corn plants. The influence of plant height is predictable and it is natural that
with a free falling stream of liquid less and less it reaches the lower parts of plants. In the case of the air
53.0
43.9
3.1
47.8
41.0
11.2
0.0
10.0
20.0
30.0
40.0
50.0
60.0
A B C
Pi, pe rcentag e of dist ribution, %
without rotors r otation with r otors rotation
52.3
26.9
20.8
48.1
32.4
19.6
0.0
10.0
20.0
30.0
40.0
50.0
60.0
A B C
Pi, pe rcentag e of dist ribution, %
without rotors rotation with r otors rotation
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1RYHPEHU 201, Brno, Czech Republic
stream operation, part of the liquid contained in the stream of drops was transferred to the lower parts
of plants. This can be seen from the plots, both in the amount of liquid deposited and the percentage of
the total deposited liquid. The air stream originating from drone rotors has positively influenced the
improvement of the uniformity of settling of the liquid on the plants. It was also noticed that the air
stream was part of the droplet stream outside the testing area in the laboratory.
ACKNOWLEDGEMENTS
The research was financially supported by the statutory resources of the Mechanical Faculty of the
Koszalin University of Technology.
REFERENCES
Berner, B., Chojnacki, J. 2017a. Influence of the air stream produced by the drone on the sedimentation
of the sprayed liquid that contains entomopathogenic nematodes. Journal of Research and Applications
in Agricultural Engineering, 3(62): 26295.
Berner, B., Chojnacki, J. 2017b. Use of drones in crop protection. In Proceedings of IX International
Scientific Symposium Farm Machinery and Processes Management in Sustainable Agriculture. Lublin
2017: 4651.
Faal, B.S. et al. 2017. An adaptive approach for UAVbased pesticide spraying in dynamic
environments. Computers and Electronics in Agriculture, (138): 210223.
Giles, D.K., Billing, R.C. 2015. Deployment and Performance of a UAV for Crop Spraying Chemical
Engineering Transactions, 44: 307312.
Huang, Y. et al. 2009. Development of a spray system for an unmanned aerial vehicle platform. Applied
Engineering in Agriculture, 25(6): 803809.
Mazur, P., Chojnacki, J. 2017. Comparison of two remote nitrogen uptake sensing methods to determine
needs of nitrogen application. Journal of Research and Applications in Agricultural Engineering, 62(2):
7679.
Mogili, U.M.R., Deepak, B.B.V.L. 2018. Review on Application of Drone Systems in Precision
Agriculture. Procedia Computer Science, 133: 502509.
Pascuzzi, S. et al. 2018. Unmanned aerial vehicle used for remote sensing on an apulian farm in southern
Italy. In Proceedings of 17th International Scientific Conference Engineering for Rural Development.
Jelgava, 2325. 05. 2018: 149154.
Qin, W. et al. 2018. Droplet deposition and efficiency of fungicides sprayed with small UAV against
wheat powdery mildew. International Journal of Agricultural and Biological Engineering, 11(2): 2732.
Tang, Y. et al. 2018. Effects of operation height and tree shape on droplet deposition in citrus trees using
an unmanned aerial vehicle. Computers and Electronics in Agriculture, 148: 17.
Tetila, E.C. et al. 2017. Identification of Soybean Foliar Diseases Using Unmanned Aerial Vehicle
Images. In Proceedings of IEEE Geoscience and Remote Sensing letters, 14(12): 21902194.
Wei-Cai, Q. et al. 2016. Droplet deposition and control effect of insecticides sprayed with an unmanned
aerial vehicle against plant hoppers. Crop Protection 85: 7988.
Xue, X. et al. 2016. Develop an unmanned aerial vehicle based automatic aerial spraying system.
Computers and Electronics in Agriculture, 128: 5866.
Yallappa, D. et al. 2017. Development and evaluation of drone mounted sprayer for pesticide
applications to crops. In Proceedings of IEEE Global Humanitarian Technology Conference (GHTC):
1–7.
Zhou, L.P., He, Y. 2016. Simulation and optimization of multi spray factors in UAV. In Proceedings of
ASABE Annual International Meeting, Orlando, Florida, July 1720: 1720.
407
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