ASABE_paper

An ASABE Meeting Presentation
Paper Number: 152181748
Autonomous System for Pest Bird Control in Specialty
Crops using Unmanned Aerial Vehicles
Yiannis Ampatzidis, Joshua Ward and Omar Samara
Department of Physics and Engineering, California State University Bakersfield, 93311 Bakersfield,
CA, USA, e-mail: yampatzidis@csub.edu
Written for presentation at the
2015 ASABE Annual International Meeting
Sponsored by ASABE
New Orleans, Louisiana
July 26 – 29, 2015
(The ASABE disclaimer is in a table which will print at the bottom of this page.)
Abstract.
Pest birds have long been a significant source of crop loss for specialty crops growers. Traditional methods of
bird deterrence like netting are effective on small farms, but require far too much material and man power for
large scale applications. Audio and visual systems only provide short term deterrence. Avicides and pesticides
have too many environmental impacts and too much liability associated with it to be cost effective. Herein, a low
cost, autonomous bird control system using unmanned aerial vehicles (UAVs) is presented. This overall system
consists of: (i) bird detection using wireless ground sensor network (WGSN) and wearable devices (including
mobile phones, tablets); (ii) swarm of automated UAV-based systems for bird control; and (iii) a smart cloud-
based decision system. The UAV-based bird control system utilizes: (a) multirotor hexacopter to present a visual
threat; (b) combined with speakers producing a unique audio signature; (c) armed with a sprayer (fogger) to
irritate the birds and encourage them to leave the crop; and (d) a “smart control system-SCS”. It combines a
visual, audio, and chemical threat to pest birds, essentially becoming its own predatory species for long term
pest bird deterrence. When the WGNS detects a flock of birds in a field’s zone, the decision system creates a
“mission” and deploys the UAV-based system to pest, irritate and encourage the birds to leave the crop. In this
paper, a prototype UAV-based system was developed and evaluated in the field to prove the hypothesis. The
“bird detection event” was simulated.
Keywords. Specialty Crops, Unmanned Aerial Vehicles (UAVs), Methyl Anthranilate (MA), Bird Control, Ground
Sensor Network (GSN), Cloud Based Software (CBS), Arduino, Xbee Wireless Module, Integrated Pest
Management, Precision Agriculture, Bird Control
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2015 ASABE Annual International Meeting Paper Page 1
Introduction
Pest birds have long been a significant source of crop loss for specialty crops growers, especially during the
critical weeks leading up to harvest. For example, losses to pest birds in Washington crops result in an annual
loss of over $80,000,000 (Anderson et al., 2013); losses to pest birds in California blueberry crops result in an
annual loss of over $2,500,000. Hence, fruit loss to birds is a very costly problem (Ribot et al., 2011). Specialty
crops lose millions of dollars of produce annually to pests (Anderson et al., 2013) such as crows, ravens,
magpies, and starlings. Raven species in particular pose a threat not only to specialty crops, but other crops,
young livestock, and numerous endangered species due to their omnivorous diet, size, and relative ingenuity in
securing food. Controlling pest birds in a manner that is effective, humane, and profitable has been an agricultural
struggle long before modern science. Traditional pest control methods generally consist of scarecrow type
devices such as aluminum foil to produce visual deterrents, or propane cannons and loud speakers that produce
sounds startling or threatening to the birds (Himelrick, 1985). While these methods may be initially effective, over
the long term birds adapt to these systems as they begin to recognize there is no actual threat. Other methods
which are more effective include popular techniques like hunting birds with dogs or shotguns, falconry, avicides,
and netting. While these methods are significantly more effective long term at bird control, they have their own
major limitations. Some of the avicides are illegal, inhumane, and cause significant environmental damage.
Hunting pest birds is effective, but may not always be viewed as humane and may difficult to enact long term on
a scale large enough to serve as a constant deterrent, although it should be noted shotguns tend to leave an
impression in the birds that associates loud noises with death, and increases efficacy of sound based deterrence.
There is also potential for human error for a hunter to accidentally kill an endangered species like a Hawaiian or
Mariana Crow. Netting acts as an effective barrier, but is extremely labor and time intensive to set up, and fails
should a hole or tear develop in the vast expanse of net. Falconry is the best practice for controlling pest bird
populations as birds of prey are natural predators to pest birds and is humane. However, it is not always possible
to host predatory bird populations on site, and hiring falconers may not always be an option for various reasons.
In general, traditional methods of bird deterrence of like netting are effective on small farms, but require far too
much material and man power for large scale applications. Audio and visual systems only provide short term
deterrence. Avicides and other avian pesticides like strychnine and methiocarb may be either illegal, or have too
many environmental impacts and too much liability associated with it to be cost effective. Attempting to lure
natural predatory birds can be effective, but may not always work and additionally the farm loses its ability to
utilize other pesticides that are needed to control other pest populations. Growers believe that most traditional
techniques for bird damage management are ineffective, or only slightly effective. Additionally, the majority of
growers reported that bird damage have a significant impact on profits (Anderson etc., 2013). Some crops are
more attractive to birds, and some crops, like blueberries, can be protecting by traditional methods (e.g. netting)
more easily than apples and cherries (but it is a costly, labor intensive and time consuming method). Unmanned
aerial vehicles (UAVs) have been used for controlling pest bird population in various crop (e.g. vineyards). Most
of these UAV-based bird control systems are remote-controlled predator replica scatters (e.g. birdXPeller
product) with an audio device (Grimm et al., 2012); not so effective or low-cost systems.
Herein, we present a low cost, autonomous, UAV-based (multirotor hexacopter) system to deter pest birds. This
system combines a visual, audio, and chemical threat to pest birds, essentially becoming its own predatory
species for long-term pest bird deterrence. This system combines a visual, audio, and chemical threat.
Materials and Method
This research is aimed at developing an optimized integrated pest management system for pest bird control by
mimicking nature’s ability to not only produce a signal of a threat to warn pest birds of eminent danger, but to
also enforce its threat through a fully autonomous system that is effective, humane, profitable, and modular for
other types of pest control. The overall goal of this project is to develop an automated system for: (i) bird detection
using wireless ground sensor network (WGSN) and wearable devices (including mobile phones, tablets); (ii)
swarm of automated UAV-based systems for bird control; and (iii) a smart cloud-based decision system. When
the ground sensors detect a flock of birds in the field, this information (including the coordinates of the area where
the birds found, local weather data etc.) is transmitted, through the cloud-based system (wirelessly and in real
time), to the UAV-based system to pest the birds (Fig. 1).
The wireless ground sensor network (WGSN) collects information regarding bird location through sensors such
as lasers, microphones or motion detectors, and weather data including but not limited to wind, humidity, rain,
and temperature. That information is then transmitted to the Cloud-based software to be processed and
determine whether or not to deploy an unmanned aerial vehicle (UAV), and if so, which UAV to deploy and on
what path (Fig. 1).

Figure 1. Overall goal of this project: When the wireless ground sensor network (WGSN) detects a flock of birds (e.g. in Zone 2),
transmits the bird location and local weather data (e.g., wind, rain etc.) to the could-based decision software, which creates the
mission for the UAV-based system(s).
Materials
We have developed an affordable and autonomous data collection system using a UAV (multirotor hexacopter)
that can perform predefined operations. The system has been designed to launch, complete its mission
waypoints, and land completely under autonomous control. When it returns “home”, the collected data are
transmitted wirelessly to a web-database over a WiFi connection (Ward et. al, 2014; Ramirez et. al, 2014).
In this paper, we present a bird control system that utilizes a multirotor hexacopter – UAV. This autonomous
UAV-based system consists of: (i) a multirotor hexacopter (UAV) that can fly at speeds of 9 miles/hour to present
a visual threat; (ii) combined with speakers producing a unique audio signature (Fig. 2a); (iii) armed with a
chemical fogger producing a repelling chemical (e.g. methyl anthranilate) to irritate the birds and encourage them
to leave the crop (Fig. 2b); and (iv) a “smart control system-SCS” to control all the sensors/devices attached to
the vehicle-frame.
a) b)
Figure 2. Prototype 1 (to prove the concept), a) Front view of multi-rotor platform armed with (i) Pixhawk flight controller (ii)
Arduino microprocessor (iii) audio system; b) Rear view of multi-rotor platform with the (iv) spraying system (chemical fogger).
Each UAV-based system was designed to be autonomous and smart: it is able to communicate with ground
sensor networks, reschedule and navigate to desired destination in real-time (dynamic control system). Two

prototype units were developed utilizing open-source hardware and software. For example, the “Pixhawk
Autopilot” microcontroller (open-source hardware) was used to turn the hexacopter to a fully-autonomous system.
The “Mission Planner” (open-source software) was used to create the mission/path (e.g. Fig. 3). Open-source
hardware (micro-electronics) and software (embedded C) was used for the “smart control system-SCS” to
connect with the sensors (e.g. GPS, barometer) and control the speakers and the sprayer. The SCS is connected
to “Pixhawk Autopilot” microcontroller, having access to the data from various sensors and equipment including
the GPS with compass module, the telemetry system, accelerometers, cameras etc. (Fig. 4). Based on the data
from the “Pixhawk Autopilot” (GPS data) the SCS controls the sprayer and the speakers. Additionally, a radio
control transmitter and receiver (remote control) was used providing manual control of the UAV and the SCS.
One chemical that irritates the birds is the Methyl Anthranilate -MA (naturally occurring in many fruits, but
particularly Concord Grapes). MA is considered safe with no long-term environmental damage. It does not affect
non-target species like bees, causes no harm to the birds or humans. It does not affect quality of produce, and
has been proven to reduce losses in blueberry crops between 65% and 99% (EPA, 2011; Askham, 1992). MA is
a chemical shown to be highly effective in some instances (Askham, 1992; Mason et al. 1991; Mason, 1993;
Engman, 2002) while its efficacy is in doubt in others (Avery, 1996; Avery, 1992). The studies suggesting efficacy
were done either in a closed environment (Mason et al., 1991; Mason, 1993), or at a higher concentration
(Askham, 1992) while the studies doubting efficacy suggests then current methods were insufficient and new
technology may bring MA to the table as an effective repellant. Part of the reason MA has not been widely
implemented is due to the cost and the speed at which it degrades naturally. It becomes largely ineffective within
3 days of application, and at $100/gallon, it’s not very cost effective to constantly apply it. This UAV-based system
applies small amounts only (target-based application) when it’s in the vicinity of pest birds, thereby ensuring the
birds are affected by this pesticide while using only a small amount of it.
Figure 3. Example of the UAV mission, using open source software (Mission Planner).
Figure 4. The “smart control system-SCS” (arduino mega) is connected to “Pixhawk Autopilot” microcontroller, having access to
the data from various sensors and controls (triggers) the speakers and the sprayer (based on the GPS and barometer data).

Method
The proposed method is: when a flock of birds is detected, the UAV-based system(s), armed with loud speakers
and a smart sprayer (e.g. methyl anthranilate-MA fogger), is deployed to their location. The system presents a
visual threat with a unique audio signal, hazes the birds with MA to irritate them, thus using multiple strategies to
drive birds from the field. This effectively makes the UAV a new predator.
The application of this system should result in long-term bird deterrence due to a new threat to pest bird, and
avoiding environmental damage as the pest birds are not harmed. MA is reported by the EPA to pose no
significant environmental threats, and non-pest species are not affected in the process (Environmental Protection
Agency, 2011). In this project we simulate the WGSN, which transmits the “bird detection information” to a non-
Cloud decision software (laptop). The decision software creates the mission/path (randomly) and deploys the
UAV-based bird control system to pest the flock of birds (Fig. 5). The “simulated WGSN” system consists of a:
(i) arduino mega microcontroller; (ii) GPS; (iii) radio communication system (Xbee); and (iv) a digital button. When
the button is pressed, the “bird detection information” is transmitted to the decision software. Then the decision
software creates a “random” mission (e.g. Fig. 6) for the UAV-based system. Finally, the UAV launches to pest
the birds; when it reaches the zone, where the flock of birds was detected, it triggers the sprayer and the
speakers. When the mission is finished, it returns home. This is a target-based application (precision farming
application).
Figure 5. The simulated event (bird detection) is transmitted to the Decision Software, which creates the “mission” and deploys
the UAV to pest the flock of birds.
Figure 6. Three different UAV “missions/paths” to cover the zone where the flock of birds was detected.

Results and Discussion
The UAV system was evaluated in a commercial orchard field. When flock of birds was detected (simulated event
using a microcontroller, GPS, radio communication and a button) in Zone 15 (Fig. 7), the decision system creates
a random “mission” and deploys the UAV to pest the birds. Figure 7 presents two different “paths/missions” to
cover the zone 15. When the UAV reaches zone 2, the SCS trigger the speaker and sprayer. This is a target-
based application; the speaker and sprayer are “active” only when the UAV is inside the zone 15, saving
chemicals and power. The system was evaluated in various orchards; no errors were detected. Every time the
button was pressed (bird detection simulated event) the decision software developed a path and deployed the
UAV to pest the birds. The objective of this experiment was to evaluated the automated system and not to pest
a flock of birds. A colored water was used for the sprayer. The UAV launched and when it reached the zone,
activated the sprayer and speaker with success. This system is fully autonomous.
a) b)
Figure 7. Flock of birds was detected in Zone 15. The decision system creates a mission and the UAV navigates to its desired
destination (zone 15) to pest the flock of birds: a) mission 1 – vertical; b) mission 2 – horizontal.
Conclusion
In this paper, a target-based system to pest birds is presented. The UAV-based system is a fully autonomous
system; it communicates with a ground-based wireless sensor network, launces and completes its mission under
autonomous control. A bird detection system detects the flock of birds in different field zones, and a decision
system creates the UAV mission (path). In this project, the bird detection system was simulated. The performance
of the UAV-based bird control system was evaluated in the field. Some limitations of this system are: (i) UAV
flight time and payload; (ii) sprayer capacity; (iii) agrochemical efficacy; and (iv) bird detection system accuracy.
Future Research
Various types of UAVs will be utilized including multirotor copters and planes. A bird detection system will be
developed using low-cost cameras, lasers, microphones, motion sensors and wearable devices (including mobile
phones and tablets). The proposed system will be evaluated with regards to further development and
implementation. We will study the impact (damage) of multiple bird species to various crops. In-field experiments
will be conducted to measure the bird damage to various crops using: (i) traditional bird-management methods
(collect samples from bins during harvest and historical fruit damage data from commercial packing shed); (ii)

the proposed system (mainly through observation); (iii) without control. The effect of several audio signatures
and chemicals (concentration, dose etc.) will be evaluated on the different pest bird species. Finally, an algorithm
will be developed to optimize the field logistics (which UAV goes where) in real time (dynamic control).
References
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Boulanger, J.R., Steensma, K.M.M., & Shwiff, S.A. (2013). Bird damage to select fruit crops: The cost of damage
and the benefits of control in five states. Elsevier, Amsterdam, The Netherlands.
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grapes. Proceedings of the Fifteenth Vertebrate Pest Conference 1992.
Avery, M. L., et al. (1996). "Field evaluation of methyl anthranilate for deterring birds eating blueberries. The
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repellent feed additives. The Journal of wildlife management (1991): 182-187.
Mason, J. R., Clark, L., & Miller T.P. (1993). Evaluation of a pelleted bait containing methyl anthranilate as a bird
repellent. Pesticide science 39.4 (1993): 299-304.
Ramirez, A., Ward, J., Ampatzidis, Y., & Jafarzadeh S. (2014). UAV-based Wireless Sensor Network in Orchards.
Southern California Conferences for Undergraduate Research, CSU Fullerton, 22 November, Fullerton CA, USA.
Ribot, R.F.H., Berg, M.L., Buchanan, K.L., & Bennett, A.T.D. (2011). Fruitful use of bio-acoustic alarm stimuli as
a deterrent for Crimson Roseallas (Playcercus elegans). Emu 111 (4), 360-367.
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Fullerton, 22 November, Fullerton CA, USA.

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