Precision Agriculture
UAS Swarm Technology
Raymond J. DeMarco
III
Embry Riddle
Aeronautical University Worldwide
Abstract
Unmanned system technology continues
to grow in leaps and bounds. The
decreased cost of sensor packages for visual cameras, infrared vision, and
other types of computer vision along with miniaturization have created greater
capabilities for unmanned vehicles. Unmanned
aerial, ground, and maritime surface vehicles can intelligently work together
by the command and control of human operators.
A high level of autonomy is required for Unmanned Aircraft Systems
(UASs) to execute and collect various data for precision agriculture. A swarm UAS can safely and efficiently capture
data that is significant for farmer analysis of animal grazing and crop
cultivation. Swarm technology is needed
to increase the total coverage, quickness, and efficiency of aerial collect
data. For the same mission, a swarm of UAVs versus a single UAV is more robust,
flexible, and can cover a greater area. This paper discusses the need for
unmanned swarm technology in agricultural applications and describes the need
for the enhancement of precision agriculture.
This paper describes the sensor integration, human interface, and UAS
architecture required for navigating, capturing, and processing data for analysis. The advantages of the unmanned system over its
manned counterpart will explain the appropriateness of swarm technology.
Discussed here are the needs, benefits, limitations, and constraints of
unmanned sensor selection, processing, and system architecture for precision
agriculture UAS swarm technology.
Precision
Agriculture UAS Swarm Technology
At
this day and age, an Unmanned Aerial Vehicle (UAV) has evolved far from its
beginnings as a hobbyist activity. Its fundamental
purpose in the year 2017 is to gather important information. A mining expedition in an untouched Yukon
terrain will require visual 3D information about the topography and decide
where to take mineral samples for a potential location to begin mining. A UAV can gather information about deadly
radiation by remote sensing in an area where humans cannot enter such as a
nuclear disaster that occurred at Fukushima, Japan. When it is required to obtain a detailed
image of a specified area, whether it is by infrared or HD video, an UAV can
provide the aerial sensors to create a 3D map, or orthogram for analysis.
The possibility to deploy a cooperative swarm of UAVs
programmed to gather crop and field information as an alternative to a single
UAV will provide faster and more efficient data for analysis; thus, giving
farmers a wealth of information very quickly to make decisions on crop
production.
The
potential speed and coverage that is supplied in an aerial sensor network of a
UAV swarm working together is far superior to a single UAV working alone. This
paper discusses the possibility of a cooperative UAV swarm in an aerial sensor
network; multiple UAVs consisting of actor and master nodes that are deployed
at the operator’s command to follow a predetermined path and gather the
requested data through various sensors and processing software. Data is
collected and stored as a historical set to observe patterns as well as current
field conditions.
Problem Statement
The
agricultural industry can benefit greatly from the use of cooperative UAVs in an
aerial sensor network that will cover a greater area of data collection,
provide faster, more accurate and up to date crop information. Cutting edge technology such as UAV camera
size, capability and weight is becoming more affordable for farmers; unmanned
systems are low cost, high-tech, and are delivered fully assembled (McNeil,
2017). UAS have also been used in a variety of 3D-virtualization and aerial
service applications such as disaster response and search and rescue; the time
has come for this technology to flourish in precision agriculture industry.
Significance of the Problem
With thousands of acres of farmland, crop monitoring is one
of farming’s greatest challenges.
Unmanned Aircraft Systems (UAS) equipped with specialized sensors for
agriculture present a major advantage to farmers for soil and field analysis,
crop spraying, crop monitoring, irrigation and health assessment. Factors such as water quality and quantity,
climate change, glyphosate-resistant weeds, and soil quantity to name a few are
farmers’ increasingly complex concerns (McKinnon, 2016). Agriculture UAS can produce precise 3-D images
for soil analysis useful in planning seed planting patters and data for
irrigation and nitrogen level management.
UAVs have been around for decades, but what is exciting in the UAS
industry is the cost and size of the sensor equipment has decreased
substantially.
Modern technology has upgraded the banner of precision
agriculture. GPS equipped machinery such
as tractors and UAVs navigate through field zones at the control of the
farmer. Unmanned aerial vehicles
equipped with multispectral cameras sense and filter various light from
frequencies within and beyond the visible light range; image data is collected
and processed to create a 3D orthogram displaying critical information about
crops such as ripeness and temperature that give knowledge to the farmer about
crop health and livestock movement.
Figure 1:
Field Images. A: NGRDI, C:RGB, D:
WDI (Hoffman, 2016)
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The United Nations (UN) Food and Agriculture Organization
predicts in the next 40 years food production will need to increase by 70
percent. Other challenges include
climate change, droughts, floods, decreases in resources and political shifts
(Swan, 2012). Noelle Swan, writing for
seedstock.com has identified 5 major challenges to the future of agriculture
and food security.
Resource
Depletion
Since North American agriculture has become more
industrialized, it relies heavily on fossil fuels and topsoil resources. John
Gerber, food and farming professor at the University of Massachusetts says, “we
have an industrial agricultural system that’s totally dependent on the
assumption that cheap fossil fuels will last forever (Swan, 2012).” The U.S. Geological Society has found the
amount of ground water irrigated has tripled since the 1950s. With some resiliency to heavy rainfall and
periods of draught, this is an added challenge to the industry. The EPA states that the leading cause of
water pollution is from agricultural runoff.
Farmers need more efficient use of nitrogen and other additives. Topsoil supply is becoming a greater concern;
it is effected by “poor agricultural practices such as overcultivation,
overgrazing and overuse of water” says Leo Horrigan at the Johns Hopkins School
of Public Health (Swan, 2012).
Land
Management
Continually
planting the same crop in one location will drain soil of nutritional
value. Rotating and interspersing cover
crops prevents weed propagation and helps to with pest control since this
method attracts predator insects. The
cover crops add organic material, increase water holding, and replenish the
soil (Swam, 2012). Regarding livestock
management, overgrazing creates dry soil, destroys grasses, promotes weed
growth and leads to desertification.
Good practice of land and cattle management holds potential for better
food production.
Food
Waste
The
UN states that roughly one-third of the world’s food is wasted doing
production, handling, processing or distribution. Supermarkets and restaurants purchase an
excess amount of produce that drives up prices and increases risk of spoilage
since consumer expectations expect produce to be without blemishes, and have
the usual shape, size and color (Swan, 2012).
In the scope of this paper only
production is discussed but these are factors that place a burden on the
agriculture industry.
Demographic
Changes
There
is an increasing disconnect between people and farming communities. 80% of United States citizens live in urban
and suburban communities. Increasing
populations of metropolitan areas create a barrier between farming communities
and consumers. Some areas known as “food deserts” have no fresh food for
sale. The USDA reports that 20 million
Americans reside in these supposed food deserts. As urban areas grow farmers
have pressure to sell their land since they cannot afford to not sell it. This is an effect on urban-fringe farming.
Political
Shift
The North American Free Trade
Agreement (NAFTA) has burdened agriculture in North America and blamed for the
loss of 2 million farm jobs in Mexico due to the flood of U.S corn imports and
other agribusiness subsidies. The U.S.
has had major shortages of immigrant workers due to tightened immigration
policies while Americans have little desire or connection to farm labor.
What the agriculture industry is lacking is complete
knowledge, control, and efficiency of farm production from the technology that
is available. Unmanned systems are
already having an impact in precision agriculture but the billion-dollar
industry has more to reap. Unmanned
tractors perform autonomously through set waypoints. When cows are ready for milking, they step
onto a platform where robotic arms wash the teats, extract the milk, and
disinfect the suction cups.
Advancements in agriculture processes and machinery are
allowing dull, dirty, and dangerous tasks to become fewer. A fixed wing or multi-rotor UAV is set to
replace manned flights over crop fields.
It is possible that in the very
near future farmers will be able to send out a swarm of UAVs to autonomously
gather the information, and have it downloaded and processed for analysis. The remainder of this paper suggests modern
unmanned system technology should be fitted to the farmers needs so they can
maximize production, increase efficiency, and reduce resource requirement and
waste.
Alternative Actions
The use of UAVs in North America is governed by the
FAA. UAS operators who want to fly
outside the requirements of the Special Rule for Model Aircraft 14 CFR part 107
will need to request a waiver or Certificate of Authorization (COA) (Request a
Waiver/Airspace, 2017). Eventually UAVs
claimed their place on the map for precision agriculture. Today they are deployed to observe livestock
grazing and crops for various types of data through different spectrums of
light as mentioned previously. The most
common use of UAVs is for spot-checking to quickly gather data on perimeters or
a certain field area. A single UAV is
quick to deploy and an acceptable way to gather information from the air but
for multiple large fields it cannot accurately or timely map and capture
field-level data with the speed or precision a cooperative swarm of Micro
UAVs. There is even a UAV that can spray
nutrients or pesticide. The quickness
and accuracy of deployed UAS swarm to gather precision agriculture data will
far surpass the limited capacity of an unaccompanied UAV.
Basic system architecture states that the first step is
identifying the stakeholder needs. Utilizing
a UAV swarm in contrast to a single manned aircraft or UAV is operator
friendly, fast to deploy and acquires information for a differential analysis. The
second step is to identify how to maximize performance at the mission level by
maximizing the probability of accomplishing all tasks effectively. Complex algorithms, autonomous UAVs, and a
Human-Swarm Interface (HSI) will be required for farmers to command and monitor
operations remotely. System architecture
should tell us ‘what the system is’ or the tangible elements that the system is
composed of (Qin, 2014). It should also
tell us ‘what the system does’ to provide value to the stakeholders (Qin,
2014).
Concept
of Swarm Agriculture
Figure 2:
Master - actor swarm structure (Say, 2016)
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Why Swarm? The deployment of Micro UAVs (MUAVs) working
cooperatively with a master UAV from a technical perspective provide benefits
such as enhanced sensing and detection capabilities, operation in complex
environments, higher spatial coverage, high mobility and economy of time, and
the ability to carry out long time monotonous missions autonomously (Rohde,
2011). The enhanced sensing and detection
capability provides a more accurate 3D map data for analysis than a single UAS. Terrain and crop information will be
significantly more accurate with multiple cameras and angles. Total coverage and efficiency of exploration
is increased, visiting more cell grids but avoiding revisits of already
explored areas (Rohde, 2011).
Sensor
Integration
Remote
sensing in agriculture relies on the integration of visual and multispectral
cameras mounted on an UAS. It should be
noted that UAS platforms collect “remotely sensed temperatures with higher
spatial and temporal resolution than those collected by satellites and manned
aircraft (Hoffman, 2016).” Thermal and
Red-Green-Blue (RGB) cameras can collect the necessary data to “determine the
greenness and composite land surface temperatures (LST)” as well as the
spectral Vegetation Index (VI) (Hoffman, 2016).
Maps
The
French-based manufacturer Parrot has produced a lightweight multi-spectral
camera called the Sequoia at a price of $3500 (McNeil, 2017). It has a 16 megapixel (MP) RGB sensor and
four 1.2 MP sensors that collect near-infrared, red-edge, red, and green data
(McNeil, 2017). It weighs less than 4
ounces and contains internal storage plus SD card slot.
Plant
stomata are pores on the epidermis of plant organs that help facilitate gas
exchange. Stomata are the openings for
which gas is exchanged in photosynthesis and respiration (Franks, 2007). Water vapor leaving the plant through the
stomata is a process called transpiration.
Regarding crop irrigation, the stomata will be closed to reduce water
loss through transpiration when water supply is insufficient. This leads to stored plant energy and thus
higher canopy temperatures (Guilioni, 2008).
Canopy temperature is positively related to crop water stress and
negatively associated to soil moisture and transpiration. Calculating the crop water stress helps
farmers calculate irrigation needs. UAV
imagery will provide the accurate crop water stress maps at different stages of
crop growth. As discussed earlier, WDI
maps determine accurate water stress values and provide interpretation of
ripeness and crops that will not need large volumes of water (Hoffman, 2016)
Aerial
Sensor Network
Autonomous wirelessly connected
UAVs are a viable sensor platform for precision agriculture. Agent based
mobility algorithms make possible the fusion of measurements and data from
different sensors more timely and accurately than any single UAV, satellite or
manned aircraft. Not yet brought into
the agriculture industry, aerial sensor networks have gained significance in
systematic environmental monitoring and are applicable in a variety of areas
for 3D virtualization (Daniel, 2011).
Air to Air links. Radio Frequency (RF) relaying is carried
out through Air-to-Air (A2A) links in an aerial sensor network. Relaying RF transmission of telemetry and
sensor data enables real time calculation of crop field conditions. Relaying within the network also compensates
for losses of air-to-ground links. An
aerial mesh network transmits the telemetry and payload data and allows the UAS
to swarm and cover a large area. “Each
node adapts its motion in order to keep the mesh network alive and prevent
interruptions (Daniel, 2011).”
Figure 3: 3 different scenarios of sector divisions
(Say, 2016)
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Human-Swarm
Interface and Processing Software
Teleoperating
individual UAVs within a swarm protocol for this application would be extremely
difficult and unnecessary. The
Human-Swarm Interface (HSI) is ideal when a wide area is to be monitored with
autonomous aerial vehicles. A human
pilot will take control of the swarm of robots, autonomy will reduce the effort
required of the operator by setting waypoints or selecting the field area for
data to be collected. In a Single
Operator Multi UAV (SOMU) context robots are required to have a high level of
autonomy to decrease the workload of the human operator (Recchiuto, 2016). The HSI controls robot behavior by user
commands. A decentralized protocol
exists where UAVs keep their position in formation while avoiding obstacles and
inter-UAV collision while following inputs.
DroneDeploy. A 3D
orthogram image is produced from the data gathering swarm. A San Francisco based company called
DroneDeploy has created an app that uses cloud based servers to process sensor
data while the UAS is still in flight.
DroneDeploy has multiple cloud servers that receive and process sensor
data as quickly as possible; as the images are being taken, they are uploaded
to the cloud through Wi-Fi or cellular LTE service (Rees, 2015). Therefore, data is uploaded for processing
during flight and the orthogram map is available immediately for analysis.
DroneDeploy currently is contracted with DJI but can process
data for any type of aircraft if it has GPS Exchangeable Image File (EXIF)
metadata (Rees, 2015). GPS enables
geotagging while EXIF provides date and time, shutter speed, exposure
compensation, and white-balance depending on the camera type (Pic2map, 2017).
Having instantaneous data is especially advantageous for
agriculture. Speed is important to farmers so they can make the necessary
assessments and conclusions from crop information. The major uses of DroneDeploy for agriculture
are to keep track of livestock and grazing patterns, detect crop parasites and
fungi, plan drainage and irrigation, and assess crop damage (DroneDeploy,
2017).
Limitations and Recommendations
Limitations currently are mostly
due lack of swarm technology that is not military based. The cost of multispectral cameras is expensive. Not every UAV in a swarm may be outfitted
with multispectral but they can be staggered with actor UAVs for different
roles. The concept of a HSI is
relatively new and will require constant upgrades. Fixed-wing UAVs will be able to achieve
similar data gathering results as Vertical Takeoff and Landing (VTOL) copters
but hovering in one area will require constant circling where a quadcopter may
hover with ease. VTOL aircraft also
provide user friendly operation by ease of launch and recovery. Fixed-wing UAVs will fly for a longer duration
and will cover more area but launch and recovery will be a manual process.
Development of a swarm algorithms specifically for data
collection for precision agriculture or UASs will be required. A human-machine interface or HSI should be
developed with farmers’ recommendations for layout of controls and displays for
feedback. Processing software to analyze
crop field data needs to be user friend and designed to allow farmers to create
a historical library to compare previous data sets. It should be customized to agriculture so
that different levels of greenness reflect ripeness, areas requiring greater or
lesser irrigation are highlighted, et cetera.
Areas requiring attention should be highlighted and put in queue to be
evaluated.
Conclusion
In
this paper, I have discussed the perceived need for an UAS for the augmentation
of precision agriculture by providing more and faster information for
farmers. This concept of a swarm UAS is
faster, more accurate, and covers a greater area for data analysis than a
satellite, and manned or single unmanned aircraft. The requirement specification for UAS swarm
is included in the system architecture, sensor selection, HSI and processing
software. The aerial sensor network is
structured in a master/actor protocol collecting multispectral data, using RF
for communication and data link between UAVs.
The swarm operates autonomously, receiving GPS waypoints to collect 3D
map data. Data is downloaded for
analysis. Farmers can make important
decisions on crop health, irrigation requirements, and monitor cattle grazing. Currently, a solo operating UAS can perform
some of these tasks in smaller fields in selected area for spot
monitoring. The use of swarm UAS will
greatly improve agriculture efficiency in the future.
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