A6
Paper #6270
Disclaimer — This paper partially fulfills a writing requirement for first year (freshman) engineering students at the
University of Pittsburgh Swanson School of Engineering. This paper is a student, not a professional, paper. This paper
is based on publicly available information and may not provide complete analyses of all relevant data. If this paper is used
for any purpose other than these authors’ partial fulfillment of a writing requirement for first year (freshman)
engineering students at the University of Pittsburgh Swanson School of Engineering, the user does so at his or
her own risk.
HOW DRONES ARE REVOLUTIONIZING FARMING
Audrey Clarke, ajc174@pitt.edu, Mahboobin 4:00, Aditya Peri, asp74@pitt.edu, Lora 4:00
Revised Proposal — The United Nations Food and
Agricultural Organization (FAO) predicts that we will need
to increase our food production by as much as 70% by 2050
[1]. In addition, the FAO has found that climate change is
likely to reduce food productivity and production stability in
areas that already have food shortages [1]. Improving crop
yields and having the ability to adjust to a changing climate
will be vital to meeting global food demands and ultimately
preventing the collapse of society.
Even with significant advances in technology over many
years, farming still requires the manual investigation of
plants, weeds, and soil along with the experience of a
seasoned farmer to determine the corrective actions to be
taken to improve yield. Recent advances have led to the
development of soil testing and ground sensors making the
monitoring of crops more efficient but there is still a lot that
goes undetected. A bird’s eye view of the farm is needed to
determine patterns so they can be addressed on a wide scale.
Aerial sensors have been used on satellites and planes but
their limited resolution and expense of collecting data limits
their usefulness. This is all about to change with the advances
in drones.
Agricultural drones are unmanned aerial vehicles (UAV)
that can be flown over hundreds of acres in a single flight
while collecting data. Drones have many advantages over
satellites; the cost is significantly less, they can fly on
demand, have more powerful lenses, and most importantly
they interact with agricultural software applications that add
value by processing the data using the latest research models
and by displaying the results in an actionable format. In the
U.S., drone usage will increase as federal rules surrounding
unmanned drones are loosened and it’s predicted that
agricultural drones will make up 80% of the drone market in
the next few decades [2]. Agricultural drones are the key
behind the precision agriculture movement, which is the
practice of observing, measuring, and responding to crop
variation.
Drones use visual and multispectral sensors to measure
the visual and near infrared wavelengths reflected by
vegetation in order to create vegetation index maps which
are commonly referred to as normalized difference vegetation
index (NVDI) maps. NVDI mapping works on the principle
that healthy crops absorb most of the visible light that hits
University of Pittsburgh Swanson School of Engineering 1
1/29/2016
them, and reflects a large portion of the near-infrared light
while unhealthy or sparse crops reflect more visible light and
less near-infrared light [1]. This paper will discuss how the
data collected by drones can be processed using the NDVI
algorithm to help farmers precisely control the use of
fertilizer and water to minimize wastes while improving crop
yields.
REFERENCES
[1] B. Kennedy. (2014). “Can the world keep up with soaring
global
food
demand?”
CBS.
(online
article).
http://www.cbsnews.com/news/can-the-world-keep-up-withsoaring-global-food-demand/
[2] S. Businessli. (2015). “3 Ways Drones can help take
agriculture to new sustainability heights.” EDF. (Online
Article).
http://blogs.edf.org/growingreturns/2015/08/19/3ways-drones-can-help-take-agriculture-to-new-sustainabilityheights/
ANNOTATED BIBLIOGRAPHY
S. Businelli. (2015, August 19). “3 ways drones can help take
agriculture to a new sustainability heights.” EDF. (Online
Article).
http://blogs.edf.org/growingreturns/2015/08/19/3ways-drones-can-help-take-agriculture-to-new-sustainabilityheights/
This blog from the Environmental Defense Fund (EDF)
is written by an experienced Biological Basis of Behavior
major from University of Pennsylvania. This article describes
the advantages of using unmanned drones in agriculture and
how drones are able create satellite maps and vegetation
index images needed to detect crop health and to detect
drought areas. This blog will be used to outline the benefits of
drones and how maps are used to access crop health.
J. Crain, I. Monasterio, B. Raun. (2012). “Evaluation of a
Reduced Cost Active NDVI Sensor for Crop Nutrient
Management.”
Hindawi.
(Online
Article).
http://www.hindawi.com/journals/js/2012/582028/
This article, from a professional, peer-reviewed journal
specializing in sensors, describes how NDVI sensors can be
Audrey Clarke
Aditya Peri
used to increase fertilizer nitrogen efficiency. This article
provides a table with recommended changes in nitrogen
levels based on varying NDVI levels. This article can be used
in our paper to show how NDVI measurements actually
translate to actionable plans to be taken by the farmer.
will use this source to compare and contrast infrared images
from satellites to drone sensors.
R. Rohr. (2014, January 21). “Meet the New Drone That
Could Be a Farmer’s Best Friend.” Modern Farmer. (Online
Article). http://modernfarmer.com/2014/01/precision-hawk/
This article from the Modern Farmer, which is the
authoritative resource for today’s cutting-edge food producers
and consumers, explores Precision Hawk’s Lancaster drone.
This drone captures images of vegetation indexes by using its
multispectral sensors. Additionally, this drone is able to cover
a lot of ground and upload all data to its cloud systems. We
will use this drone’s features in our paper to show the
benefits of UAVs in the agricultural industry.
(2015). “Emerging Risk Report.” Lloyds. (online report)
http://www.lloyds.com/~/media/files/news%20and%20insigh
t/risk%20insight/2015/food%20system%20shock/food%20sy
stem%20shock_june%202015.pdf
This research article from Lloyd’s of London, a global
insurer, describes the impacts to society of a major disruption
to the global food supply. The report describes how climate
change and water scarcity can drastically lower crop yield
and trigger a shock in food prices and possible political
instability. Information from this article will help us show
the importance of minimizing water waste and reacting
quickly to climate changes to prevent crop loss.
(2014, June 9). “Variable Rate Nutrient Application: Should I
Consider it For My Farm?” Agriculture and Agri-Food
Canada. (Online Article). http://www.agr.gc.ca/eng/scienceand-innovation/agricultural-practices/soil-and-land/soilnutrients/variable-rate-nutrient-application-should-i-considerit-for-my-farm-/?id=1368026127650
This article was written by the Agriculture and Agri-Food
Canada (AAFC), a Canadian government program which
supports sustainable farming. The article describes how aerial
mapping can create soil management zones which are areas
within a field that need different levels of nutrient
additions. We can use this information in our paper to show
how NDVI numbers correspond to soil sampling and to
specific recommendations for water and fertilizer
adjustments.
(2014, June 10). “Misconceptions about UAV-collected
NDVI imagery and the Agribotix experience in ground
trothing these images for agriculture.” Agribotix. (Online
Article). http://agribotix.com/blog/2014/6/10/misconceptionsabout-uav-collected-ndvi-imagery-and-the-agribotixexperience-in-ground-truthing-these-images-for-agriculture
This blog is written by Agribotix, an agricultural
intelligence company, and focuses on NDVI imaging. The
article describes the background on the development of
NDVI and contains detailed diagrams of NDVI imaging. It
also describes how healthy, unhealthy, and dead vegetation
reflects NIR and visible light. Information from this blog will
support the focus of our paper on how the NDVI algorithms
effectively detect crop health and improve yields.
J. Wihbey. “Agricultural drones may change the way we
farm.”
The
Boston
Globe.
(Online
Article).
https://www.bostonglobe.com/ideas/2015/08/22/agriculturaldrones-change-wayfarm/WTpOWMV9j4C7kchvbmPr4J/story.html
This article from the Boston Globe, a newspaper,
discusses how drones are the future of the farming industry
and how federal rules on the use on UAV’s have been
recently loosened for the farming community. It also
discusses several universities that have been researching
farming techniques as they relate to the big data being
collected by drones. We will use the information from this
article to show how farming research and data analysis can
improve farming efficiency.
(2015). “Precision Farming Toolkit.” Arable Farming. (PDF).
http://www.cffertilisers.co.uk/media/1911/precision-farmingtoolkit-final.pdf
This precision farming guide was published jointly by CF
Fertilizers, Claas, and Agrii. These companies specialize in
fertilizer, agricultural machinery, and agronomy services
respectively. This guide describes how soil management
through yield mapping can lead to intelligent soil sampling
and how soil factors into irrigation and fertilizer control.
Information from this article will be used to explain why
different soil types need specialized care.
(2010). “Reflected Near-Infrared Waves”. NASA Science.
(Online
Article).
http://missionscience.nasa.gov/ems/08_nearinfraredwaves.ht
ml
This technical article from NASA explains the concept of
Infrared Radiation being reflected and absorbed to show
vegetation health and soil conditions. It goes into depth about
how to identify healthy vegetation and to identify spectral
signatures. Additionally, it provides information on accessing
information on soil and vegetation from space satellites. We
2