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AgriDrone - A Ubicomp Prototype for Precision Farming

Med MatovuIT University of Copenhagen

medm@itu.dk

Viktoras SvolkaIT University of Copenhagen

vsvo@itu.dk

Paul HenckelIT University of Copenhagen

pkrh@itu.dk

ABSTRACTPrecision agriculture aims at providing ICT tools that allowfarmers to micromanage and make detailed plans for the workto be done. Solutions have existed since the 1980s but havenot found widespread adoption. With the rapid developmentof low-cost open-source drones and advances in camera tech-nology the last couple of years, we propose a system formonitoring crop development by taking pictures and planningagricultural activities using drones that can be controlled be-yond visual range using a standard web interface. The systemwas evaluated in terms of its perceived usability and a com-parative measure on the time it takes to cover an area by footvs using drone captures. We found usability of our system tobe high and calculated the coverage time of the drone to be 3times faster than manual effort. This will allow the farmers toschedule activities in a more informed way, saving time firstand resources.

Author Keywordsdrones, aerial photography, precision agriculture, precisionfarming, crop specific site survey, UAV, decision supportsystem

INTRODUCTIONUsing drones or unmanned aerial vehicles (UAVs) in agricul-ture initially started back in the early 1980s for crop dust-ing[?]. Since then the UAVs have proliferated in applicationsof aerial photography to imaging of crop fields to assist withcrop production management [6, 1]. The agriculture indus-try has grown and is seeking advanced ways to control andmonitor crops, thus the term Precision Agriculture/Farming(PA/PF) has emerged as a farming management strategy. PAwas initially used as synonym for automatic steering systemsin the early 1990s. Current PA applications collects and pro-cess data from multiple sources for improving the understand-ing and management of soil and landscape resources in orderto handle crops in a more efficient way.

Even though solutions for PA exists today, widespread adop-tion has not been catching on. There can be numerous reasonsfor this including that PA is not equally suited for all types ofcrop [2], but also that the systems available are too complexor more advanced than needs to be for the utility of the farm-ers in their day to day work.

We propose a low cost, low entry barrier system for mon-itoring crop development and planning agricultural activitieswhich can be used as part of a larger PA framework. This willbe achieved by using drones to take pictures of farm parcels.

The problem of proficiently monitoring crop development inmodern industrial agriculture increases with the size of thefarmland. For this reason we established a partnership withRyegaard og Trudsholm Godser1 which operate an area of1000ha with a yield of 850ha per year. Their main crop iswheat and they are already by their own needs, trying to de-velop ways to detect and respond to crop threats in more effi-cient ways.

For planning and crop-development purposes we think dronescould be useful within organic agriculture and permacultureas well.

RELATED WORKThe usage of UAV is not new, it have been in production sincethe early 1900s. The technology was initially driven by mili-tary applications in World War I and expanded by World WarII. The military UAV applications are more advanced than thecivilian applications. [4]

The civilians applications are likewise evolving in the samedirections, due to their rapidly utilisation in various applica-tions such as firefighting assistance, police observations ofcivil disturbances and scenes of crimes, reconnaissance fornatural disaster response, border security, traffic surveillanceand precision agriculture [3, 4, 7].

Primicerio et al utilises unmanned UAV (VIPtero) equippedwith camera for site-specific vineyard management. Nor-malised Differential Vegetation Index (NDVI) values ac-quired by the Tetracam ADC-lite camera mounted onVIPtero were compared to ground-based NDVI valuesmeasured with the FieldSpec Pro spectroradiometer to ver-ify the precision of the ADC system. The vegetation indices

1http://www.ryegaard.dk/

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obtained from UAV images are in excellent agreement withthose acquired with a ground-based high-resolution spectro-radiometer.[5]

Merz et al addresses the design of an autonomous unmannedhelicopter systems for remote sensing missions in unknownenvironments. Focuses on the dependable autonomous capa-bilities in operations related to Beyond Visual Range(BVR)without a backup pilot by providing flight services. Utilizesa method called Laser Imaging Detection and Ranging (LI-DAR) for object detection which are applicable in real worlddevelopment.[4]

Barrientos et al. utilises a team of UAVs to take pictures inorder to create a full map, applying mosaicking proceduresfor post processing and automatic task partitioning manage-ment which is based on negotiation among the UAVs, consid-ering their state and capabilities. Thus they combine a strat-egy which encompass multi robot task planning, path plan-ning and UAV control for the coverage of a crop field for datacollection. [1]

Table 1 compares the industry UAVs with the research-basedprojects. It emphasise on product costs and survey speed. Seethe table on page 3.

The parameters mentioned in the table description:

Frame type The type of UAV fixed or and rotary wings

Project Name If the project was an industry project or re-search project

Retail Price How much the products cost

Control interface Is it controlled by a remote,pilot, au-tonomously or continuous trajectories, and what systemsis utilised to control them.

Imaging specification is utilised with photogrammetry thatanalyses images providing information regarding therecognition and identification of objects and their signifi-cance with respect to the particular application.

Coverage per trip How long it takes to cover a specific area.

Storage How the data is stored2

[TODO: include http://precisiondrone.com/ and Conserva-tionDrnes + include the parameters in the Main Contributionssubsection]

The parameters in Price and Survey speed are not com-pletely up-to-date, accurate or comparable. There are fourfactors that affect the survey speed: 1) Camera angle of view,2) Drone height, 3) Requested image overlap and 4) Dronecruise speed.

Most companies (except Trimble) did not provide informa-tion on the value of the factors used for calculating the survey2http://www.questuav.com/index.php, http://uas.trimble.com/ux5, http://www.cansel.ca/en/products/survey-instruments/data-collectors/trimble-ux5?vmcchk=1,http://www.cropcam.com/, https://www.sensefly.com/drones/ebee.html, Pic-colo CC http://www.cloudcaptech.com/piccolo command center.shtm,http://www.geistware.com/rcmodeling/articles/beginner 1/#basic engine

speed. In Denmark authorisation must be gained to fly above100m.

Regarding price it should be noted that we are not includ-ing development costs for AgriDrone. One of the main ad-vantages of the commercial UAVs compared with our OpenSource UAV is the camera specification and the survey spec-ification. As the table shows, the commercial UAVs are su-perior in those categories, but comes with an expensive pricecompared to our UAV which is more cheaper and adequate tocarry out the essential tasks as the commercial products.

Another notable difference is that most of these other prod-ucts are using a fixed-wing design. This implies that theyhave to be pushed into the air on launch, either by hand or bycatapult, and do a crash landing at the end of the mission.Conversely in our prototype we have a rotary wing framewhich enables us to do vertical take-off and landing (VTOL).VTOL enables the user to initiate multiple aerial missionswithout low-level user involvement in takeoff or landing.

Main ContributionsAs it has been mentioned in the interview section , farmershave numerous tasks of routines to carry out in their dailyactivities. The farms are really huge, thus it takes time toplan for the tasks. However the solution presented enables thefarmers to monitor and investigate the area in a sufficient waycompared to current solution where the stakeholders have toaccomplish the tasks manually by going out and checkingeach place, which consumes a great deal of time.

Below we list some of the main features that our solution con-tributes which none of the other projects offer.

GSM Our solution provides communication over the GSMdata network. This allows for true remote control beyondvisual range.

LPIS Our solution integrates with the Danish implementa-tion of the EU Land Parcel Information Systems to presenta custom overlay of the fields belonging to the farmer. InDenmark more than 300.000 fields are registered in LPIS.3In all of EU this figure is 135 million fields belonging to 8million farmers.4

Our systems contributes and facilities the farmers daily work.It enables farmers to operate the UAV on the different devicesthat have internet access thus providing flexibility.

3http://www.cowi.dk/menu/NyhederogMedier/Nyheder/Geografiskinformationogit/pages/digitaliseringaf300000marker.aspx

4http://ies.jrc.ec.europa.eu/our-activities/support-for-member-states/lpis-iacs.html

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ProjectName

Frame typeRetailPrice[EUR]

Control interface Imaging specification Coverage pertrip Storage

QuestUAV200

Fixed WingSkyCircuitautopilot

18000 Custom made laptop and RC Hardware Modified LumixLX5 10MP

100Ha(1km2) in7-15 min SD card

QuestUAV300

Fixed WingSkyCircuitautopilot

20000 Custom made laptop and RC Hardware Tetracam MCA 6 100Ha (1km2) in

7-15 min SD card

eBee Fixed Wing 8800 eMotion 2 software for desk-tops and NoteBooks

Hardware 16 MP CameraSony NEX5R Photogram-metry Postflight Terra 3D-EB powered by Pix4D

150-1000Ha(1-10 km2) in 45min Resolution:3 cm/pixel

SD card

SwingletCAM

Fixed Wing 7800 eMotion 2 software for desk-tops and NoteBooks

Hardware 16 MP CameraSony NEX5R Photogram-metry Postflight Terra 3D-EB powered by Pix4D

150-600Ha (1-6km2) in 30 minResolution: 3cm/pixel

SD card

CropCam Fixed Wing 5200 Custom made software forwindows 98

Hardware 12MP Pentax Op-tio A40

64Ha (0.64km2)in 20 min SD card

Trimble UX5 Fixed Wing 33100 Mission Planning Hardware 16.1 MP mirror-less APS-C

100Ha (1 km2)in 31min Resolu-tion: 2.4cm/pixel

SD card

TrimbleX100

Fixed Wing less thanUX5 Mission Planning Hardware 10 MP compact100Ha (1 km2)in 41min Resolu-tion: 3.3cm/pixel

SD card

AgEagle(early 2014)

Fixed WingPiccoloautopilot

5100 Piccolo Command Center Photogrammetry with Ag-Pixel

240Ha (2.4km2)in less than 60mins

MetaVRCFixed WingAPM2.5 au-topilot

MissionPlanner for Win-dows

Hardware Canon EOS MPhotogrammetry MetaVRTerrain Tools extension toESRI ArcGIS

(up to 65 kmph)Resolution:3cm/pixel

SD card

AeryonScout

RotaryWings cus-tom autopilot

2200-3600 custom tablet Hardware Photo3S-NIRPhotogrammetry Optionalsupplementary productpowered by Pix4D

(40 kmph) Reso-lution: 1cm/pixel

on-board andsent to basestation by ra-dio link

MicroDronesRotaryWings cus-tom autopilot

from 8000 GPS Waypoint navigationsystem

Photogrammetry poweredby Orbit GT

Resolution:1-7cm/pixel

VIPtero(modifiedMkrokopterHexa-II)

RotaryWings cus-tom autopilot

4000 koptertool Hardware Tetracam ADC-lite camera

0.5 ha. in 7.5min5.6 cm/pixelground resolutionat a flight heightof 150m.

micro SDcard

CSIRO RotaryWings custom navigation computer Hardware RICOH GX200digital camera with zoomleRICOH GR Digital III dig-ital cameraRICOH GR Dig-ital III camera mod. forNIRFLIR Photon 640 ther-mal imaging camera GNCSystem (LIDAR)

SD card

AR100 Plat-form

RotaryWings custom backstepping+FSTcontroller

Hardware commercial zoomdigital camerato be tilted upto 100 deg

SD card

AgriDrone

RotaryWingsAPM2.5autopilot

600 web-application Hardware ArduCAM next25mm Photogramme-try NDVI capable withDroneMapper or Pix4cloud75% image overlap

22ha in 12-20min(at 36 to 20kmph) Resolu-tion: 2cm/pixel

web-application(radio link)

Table 1: Comparison of commercial industry UAVs with research based UAVs based on the costs and the survey speed.The last4 projects have been used in research projets on precision farming specicly.

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METHODWork in the agriculture sector is very much a situated experi-ence and the work required at one farm might not be the sameas the work required at another farm at a given day. Becauseof this nature of situated variability, we found it important toselect some research methods that allows us to catch some ofthis situated variability in a feasible way.

To gain an understanding of the domain, we initially made aliterature survey and brainstorming session. Based on this wewere able to formulate an interview guideline that we usedand made telephone interviews with our stakeholders to getmore information about what work scenarios play out in theirdomain, what factors influence their work and how they en-visioned the use of drones could be a part of their daily workdays.

Grouping together some of the things we found out during ourinterviews, we quickly proceeded to make a low-fidelity pro-totype a paper prototype that simulates the software run-ning in a browser on a tablet device. Experience prototypingwas used to evaluate our paper prototype together with ourstakeholder, and we filmed the session to catch the subtle in-teraction taking place. This feedback was then used to refineand implement our prototype into an early product design.

Final evaluation was done as a usability study together with aquantitative measure, namely a measure for the time it takesto do an aerial survey compared against the time it wouldtake the farmer to make the reconnaissance by foot and directobservation.

THE DESIGN OF THE PROTOTYPEOur design process was iterative and comprised around 8weeks in total. In this period we gained a broad understand-ing of Precision Agriculture and Arduino-based drones andwere able to design and evaluate a prototype system.

Semi-structured InterviewsWith the domain in hand, we prepared a short interview andthen got in contact with different people involved in Agricul-ture in Denmark and located within 100km from ITU. Thisgeographic restriction was to allow us to go visit the farmersand try out our prototype in a situated context.

One of the first things we noticed was that everyone we asked,knew and were enthusiastic about the potential of drones foragriculture. This perhaps reflects the position of this categoryof technology on the Gartner Hype Cycle5.

The interviews were designed to give us some key infor-mation about current working procedures, determining mainchallenges, and general conditions for work. While talkingover a telephone was not enough to get a full picture of thescenarios of the stakeholders, it provided us with a startingpoint for scenario analysis. Second part of the interview wasdesigned to be a brainstorm on how they perceived that dronescould be applied within their work.

The farmers seemed to be in consensus that the developmentof drones within agriculture should start with large farms5http://diydrones.com/profiles/blogs/where-drones-sit-on-the-gartner-hype-cycle

(> 500ha) since tenant farmers of small farms usually willbe able to maintain an overview of the state of the farm indi-vidually.

In Agriculture, not one day is the sameA common answer we got to the question What is a normalday for you? was that they all replied that not one day wasthe same. In Agriculture, the season, climate, growth states,nutrient levels as well as human and facility resources allplayed together and affected the required work and ultimatelythe crop-yield. Examples of this include: 1) If wind is strong,spraying with pesticides is prohibited, therefore spraying usu-ally takes place 05.00 to 11.00 and 16.00 to 20.00; 2) Ifquickspots6 develop on the field, they must be detected andremoved quickly to protect the crop; 3) If crop is mature andclimate conditions are dry, sunny and windy, then harvest cantake place; 4) If parts of a field are covered in snow, thenspraying with fertilisers is prohibited; 5) When a tractor hasbeen loaded with pesticides, fungicides, herbicides or fertilis-ers, it has to run until tank is empty, it is not advised to keepremains in the tank for next run; 6) If grasslike weed have de-veloped to two leaves, then they have to be sprayed to avoidnutrient loss from the desired crop; 7) If [TODO: chec withEric about facts] wheat have developed four leaves, then ithas reached high maturity and can soon be harvested.

Another thing that came out of the interviews was that inrelation to the planning and accounting/documentation workthat takes place on the farm, being able to use maps with theproper Markblok field parcel overlay was crucial to the us-ability of the system. The Danish Markblok database is anational implementation of the EU regulated Land Parcel In-formation System (LPIS) initiative.7

We identified following application areas for drone-basedICT systems within Agriculture based on our interviews:

Biomass estimation Weed detection Nutrient estimation Farmland aerial monitoring, task management and commu-

nication

Facility monitoring and managementAside from these, application areas such as detection of nitro-gen stress, water stress, pests and crop diseases have also beenmentioned in literature [1] 8 and researchers from KU are cur-rently working on a project on weed detection and drones9

Paper PrototypingWe designed our system so that a web interface is themain point of interaction. The drones base stations, batteryrecharging requirements [TODO: reference drone-charging6quickspots are small areas of weed on the field that is expanding quickly7EU LPIS http://marswiki.jrc.ec.europa.eu/wikicap/index.php/LPISIn Denmark, the Danish AgriFish Agency under the Ministry of Food, Agriculture andFisheries is the agency responsible of implementing the LPIS nationally.

8http://www.weeklytimesnow.com.au/article/2013/10/25/586771 grain-and-hay.html9http://ing.dk/artikel/droner-spotter-ukrudt-i-danske-marker-161076

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group] and image transfer and post-processing should hap-pen behind the curtains by the system requiring little userinvolvement. To lower the entry barrier (increasing the rate oftechnological adaptation), we decided to mimic the interfaceto something that the user is already familiar with - GoogleMaps. Figure 1 shows our initial mockup of a Google Mapinterface.

Figure 1: Google Map mockup used for early paperprototype

Then we printed and used scissors and pencil to create apaper-based mockup of the UI and all the interactible ele-ments down to 3rd level of interaction. See figure 2

Experience PrototypingWe used experience prototyping as an evaluation method ofour paper prototype. This was carried out over the course ofan hour in which the user was given the prototype and told toimagine that this was a browser window on a touch-enabledtablet computer. We assumed the role of silent facilitatorsof interaction giving the user the space to conceptualise thesystem in his own words. See figure 3

EvaluationThe user found the UI pretty intuitive and easy to utilise. Wehad forgotten to draw the Markblok boundaries on the mapand that was a point of critique because this is a necessity tobe able to select which farmland to perform an action on.

Based on the feedback we also decided to deprecate our con-cept of Task Management from the GUI. We had originallyconceived this as a task management component for plan-ning human resources, but during the experience prototypingit quickly became apparent that this was not a use case. Theargument is that rather than trying to augment the captureddata with a layer of task management logic, it would be betterto remain transparent to the captured data and provide APIsthat can be utilised in tractor GPS computers, boomspray con-trollers or other peripheral computer-controlled farm equip-ment.

(a) Main page GUI

(b) Interaction-dependent GUIelements

Figure 2: Paper prototype

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T(a) User interacts with View Data page

(b) User interacts with 3rd layer functionality on Main page

Figure 3: Experience Prototyping: Evaluation of thepaper-based prototype

The Final DesignThe system consists of a web-based application that providesall the information that is needed to carry out the drone oper-ations on the field via its web-interface. The web-based ap-plication provides a communication link between drones anduser via GSM/GPRS connection to the web interface.

Figure 1 illustrates the main idea behind the implementa-tion. We have 6 main parts in the application. The dronewhich is equipped with an APM2.5 Arduino board, a uBlockGPS module, RC telemetry link, GSM/GPRS module that im-plements the necessary Mavlink communication protocol10and a Sony NEX5R modified NIR for NDVI11. The secondlayer is the Web application which controls the communi-cation between the integrated web-based application and theflying drone. Using GSM module, the drone sends the sta-tus information via the GPRS connection to the web appli-cation, then the web application gathers and process the in-formation it gets and do other steps according to the tasks(e.g. sends HTTP post with the data to the interactive web-based map). The third layer we have is the web-based in-teractive interface from which the stakeholders can monitoreverything.The fourth layer is database, where we store allnecessary transaction information. The fifth layer is the inte-gration with openlayers.com API to provide a base map forour interface, together with an overlay map of all danish farmparcels (provided by the Markblok database of Danish Agri-

10https://pixhawk.ethz.ch/mavlink/11Open Source Single Camera NDVI http://flightriot.com/vegetation-mapping-ndvi/

Fish Agency12). The sixth layer integrates a cloud or ded-icated server based app for photogrammetry converting ourdrone captures into orthomosaics that can be used for analy-sis.

Figure 4: System architecture

Flying the DroneThe drone model: 3DR DIY Hexa Copter. It was pre-build with main features: camera, gps, telemetry, autocontrolboard, arduino APM board. To this concept we added a GSMmodule with the SIM card, which extended the possibilitiesfor the drone. On the arduino based GSM module we im-plemented and integrated a service, which can communicatewith our main web application on top of Google (We foundtwo similar but currently defunct projects13). The connectionwas made through GPRS and HTTP json based commands.The flow of the drones operations are illustrated by this se-quence:

1. The drone receives the GPS coordinates from the web-application and a trigger to start mission

2. Starts the motors/engine

3. Fly to specified GPS coordinates

4. When reaching the coordinates, drone starts taking picturesat the user-specified setting

5. Drone goes home to base station

6. Captured images are sent to web application over WiFi linkfor post processing

The same workflow applies to the Scheduling mode as wellas the instant-command mode.

Web ApplicationThe web-application in this project takes the most valuableposition. It controls the communication between web inter-face and the flying drones. All requests and responses are12https://kortdata.fvm.dk/geoserver/web/?wicket:bookmarkablePage=:org.geoserver.

web.demo.MapPreviewPage13https://code.google.com/r/andreas-apm2-wip/ and https://code.google.com/p/

ardupilot-cellular-extension/

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based on JSON. The request is sent through the HTTP pro-tocol. The application is hosted using Google app engine,it gives us 99.9% system up-time, and ensures minimum re-sponse time. We provide small API in the application, whichallow us to connect our application to another third party ap-plications like periphery farm equipment. It stable and recog-nise (response) only due to predefine commands: get status,send gps coordinates, get pictures, get biomass index at coor-dinate, check battery and etc.

Interactive web-based map applicationAfter the interviews and the evaluation of the paper prototypewe decided to change the following:

Status page The status page is the main page and allows userto instantiate new missions quickly. Either as a scheduledevent to be executed later or an instant event to be executednow.

Schedule page Our implementation provide the functional-ity where you are able to schedule drone to fly to specificplace at certain date and time.

Concept of Task We changed this from our original post-flight human-centered notion of task planning to a pre-flight drone-centered notion of task planning.

LPIS/Markblok Integrating with the national database offarmland boundaries allowed us to provide a way of userinteraction where the could just click to select a given fieldinstead of drawing custom polygons.

Multiple fields and actions We enabled user to specify mul-tiple fields and actions for single missions.

The system is compatible with multiple devices. So the sys-tem can be accessed from smartphone as well as from com-puter or tablet. Nowadays mobile phones are one of the mostwidely used devices in the world, this is the main reason whywe decided to make a browser-based system. Figure 5 showsthe final design of the web interface.

Challenges encountered in developmentBelow we list some of the challenges we encountered in theprocess of developing our system.

Calibration We had to perform a live calibration on ourcompass. It was difficult to find the compass calibrationposition because no positions were shown the same appliesto calibrate accelerometer.

Compassmot This part came us a surprise. It was an oner-ous task to carry out. It required us to disconnect the pro-pellers ,flip them over and rotate them one position aroundthe frame. And simultaneously we had to push the copterdown into the ground when the throttle is raised i.e. thepropellers are rotating.

Battery challenges (charging problems) At the first begin-ning the connection cables doesnt fit our needs, that is whywe need to manually replace the connection cables on thebattery itself. In addition we had some problems with bat-tery charger.

Propellers During first assembly we didnt know that pro-pellers of the drone are different and they need to be assem-bled in special way. So at the beginning the drone couldnot take off because of the propellers pulling and pushingin the wrong way, while we were thinking that there someproblems with calibration.

GSM/GPRS module To fully accomplish communicationbetween APM2.5 board drone and web-application wesuppose to connect and integrate GPRS shield on APM2.5board. We did Arduino UNO communication channel withGPRS shield and web-application, but it was challenging tomove everything on APM2.5 board. Firstly, GPRS shieldrequires 5V to work with.It was difficult to find the 5V pinalso on APM board. Moreover we had problems connect-ing to the internet using some GSM providers SIM cards.Also we could not figure out how to transfer Arduino UNObased implementation code to APM2.5 board, the compi-lation were failing due to missing different libraries.

EVALUATIONThe challenges that we encountered during this project meantthat we could not in effect implement the fully workign sys-tem as per our design. In order to do the evaluation anywaywe used a version of the Wizard of Oz method in which weasked the user to imagine that interacting with the web inter-face would control the drone.

One of our goals was to have an intuitive interface so that thebarrier of entry could remain low. Based on the evaluationfrom our test person, this goal appears to have been achieved.Another of our goals was to integrate a communication linkbetween the GSM module and our web server. As mentionedbefore we could actually get this to work in testing with theArduino UNO board, but porting it to the APM2.5 wasnt suc-cessful. The third goal that we wanted to show was that util-ising a drone can be a big timesaver for the farmer. Our testperson informed us that, if it should be very thorough, then in-specting a 20ha field would take him 3 hours. By calculationsbased on an altitude of 100m and image overlap of 75%, thenit would take the drone 12min at top speed (36kmph) and 20min at a cruising speed of 20kmph. After capturing the datawe estimate another 30 min in post-processing of the imageson a server to produce the orthomosaic maps at 2cm/pixelresolution which the user then can inspect. This means thedrone-based solution is approximately 3times faster than thecurrent method. The bottleneck is likely to be the rate atwhich we can transfer the images off the drone to the cloudfor processing and this processing time increases when thenumber of images increases.

Now, because we have only evaluated the system with one testperson we do not have enough data to make a very strong andcompelling argument. At Ryegaard there are 12 fulltime all-year employes. Our test person was one of these employes.Their organisational structure is very flat meaning that theyare all involved in field inspection tasks when needed. Thatthey are all exposed to the same type of field inspection tasksis a good indicator that they will all find our system equallyuser friendly as our main test person did.

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T(a) Main Page GUI of AgriDrone (b) Schedule Page GUI of AgriDrone

Figure 5: Web interface of AgriDrone system

As part of a more general discussion, it is also worth noting,as our test person commented. That the real difficulty facedis in knowing how new measures of biomass or weed densityshould be translated into actual measures of the amount ofchemicals to use on the fields. With precision farming andthe ability to look at the field with greater variability, then weare also able to spray chemicals in matching variability, butinitially this does not necessarily entail that less chemials willbe used - only trial and error will lead to an understanding ofhow chemical usage can be reduced in the long run. This isimportant because, in light of the interviews, the value of thedrone system is proportional to the value that the farmer cansave by adopting the drone system. Saving human resourcesis one thing, but being able to use the information captured ina way as to save costs on chemicals is probably what is goingto be the crux of the argument.

In Denmark the rules and regulations for flying with dronesare governed by Trafikstyrelsen in BL9-414. Specifically itmentions that operation must be more than 150m away fromresidential areas and main roads, 5km away from aerodromesand military areas, and at a maximum height of 100m withfull pilot control override at all times during flight. Exemp-tions can be granted for research and commercial applica-tions. We think that as imaging technology becomes moreadvanced, the desire to go above 100m in altitude is going toincrease because people would want to capture bigger areasfaster.

FUTURE WORKDuring the research and the user-participation, many ideascame to us. We will list them here.

Suggestions for improvements based on Farmer:

1. Place multiple waypoints on map

2. Integration with Trimble GPS control system on tractors

Copter14http://www.trafikstyrelsen.dk/DA/Civil-luftfart/Luftfartserhverv/

Unmanned-Aircraft-Systems-UAS.aspx

The 3DR Hexa-C Copter that we are using has a maximumspeed of 36kmph. The performance of a drone has a lot to dowith the aerodynamic properties caused by the physical de-sign of the drone. 3DR has since we started this project, dep-recated the HexaCopter model and replaced it with the Y6model which is a tricopter frame with rotors on both sides.They have also provided a guide to do this modification one-self 15. Use the 3DR Iris QuadCopter16 or some of the mini-UAV quadcopters available[TODO: find link to the micro uavsystems].

PhotogrammetryDronemapper.org17 allows us to upload our photos and turnthem into orthomosaics which will be a much nicer way topresent to the stakeholders. Pix4uav Cloud

In the open-source world, maybe there will be possiblity touse the frameworks GRASS 18 or ImageJ19 to integrate thephotogrammetry features within our own web framework.

To improve precision of measurements, it might be necessaryto introduce ground control points on the fields or maybe justat the base station.

Data ManagementAs the geospatial image data grows we need better waysof control and optimizations. The tools provided by thePostGIS20 project allow easy hookup with a PostgreSQLdatabase.

Integration with Danish Agro ICT

15Guide to turning the HexaCopter into a Y6 http://3drobotics.com/wp-content/uploads/2013/06/Y6-Conversion-Kit-Assembly-Instructions-Rev4.pdf

16Iris QuadCopter: http://store.3drobotics.com/products/iris17Guide to Dronemapper service - http://dronemapper.com/guidelines;

Desktop solutions: BAE Mosaic Manager http://www.geospatialexploitationproducts.com/content/products/product-modules/mosaic-manager, http://grass.osgeo.org/, http://www.sensefly.com/operations/maps-and-3d.html, http://conservationdrones.org/ http://pix4d.com/products/

18GRASS GIS http://grass.osgeo.org19ImageJ http://rsbweb.nih.gov/ij/20PostGIS http://postgis.net

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PlanteIT21 offers a mobile application that registers whenusers enter and leave a farm and prompts user to do time-tracking of activities. Extending this solution with the infor-mation gathered via AgriDrone will allow us to push infor-mation to the user in a location-aware context.

Camera SystemAside from the TetraCam option mentioned in [5]. In theConservationDrones project they use cameras that do NDVIin one camera. These cameras are modified by the companyLDP LLC22 and they offer conversions of many consumercameras.

Integration with Agricultural ProductsIn our usecase the farmer had a Fendt tractor with TrimbleGPS system. It would be very beneficial to provide a methodof sending the data (tables of GPS coordinates and corre-sponding biomass index and other measured parameters) tothe Trimble system that the farmer already brings with himinto the field.

Crowd Sourcing and User Submitted ReadingsIn an article from IBM Research 23, they mention another usescenario which we have not yet explored. The scenario theypropose is to allow users to, with their mobile device, takepictures in the farm of that is uploaded to the website and canbe analysed by distributed experts. It bears some resemblanceto Trimbles ConnectedFarm platform24.

Manual OverrideEven though various failsafe parameters are already in place,to increase safety, disaster management and user control fur-ther, there should be an option of manual override to force thedrone to land. One way to do this could be to have a AbortMission and a Emergency Halt button available as part ofthe webinterface. But that would only work with internet ac-cess. A better way would perhaps be to use the GSM moduledirectly. Where placing a call to the drone would invoke theEmergency Halt behaviour and land the drone instantly onground.

Authentication and scaleabilityWe didnt implement any kind of authentication in our systemwhich means that anyone on the internet could in effect trig-ger a drone to start flying a mission. To be a useful systemfor farmers each tenant farmer needs to be secured exclusivedrone operation rights and data-access to the fields belongingto him. To allow for collective drone-sharing, it might maesaense also to allow farmers to create groups in which theyshare operational access to a shared set of drones with basestation between their properties.

Integration with wireless sensor networks (WSN)Our interviews showed that another important parameter forfarmers is to know about the microclimatic variations across21http://it.dlbr.dk/DLBRPlanteIT/22http://www.maxmax.com/digital still cameras.htm23IBM Research - The future of PA: http://www.research.ibm.com/articles/precision

agriculture.shtml24http://www.connectedfarm.com/

the fields - soil moisture levels, soil nutrient levels and croplocal temperature conditions. These kinds of data would notbe feasible to acquire using a drone flying at 100m altitude.Instead we propose a solution that involves a WSN of Wasp-motes using Libeliums Agricultural sensors25 as a startingpoint.

CONCLUSIONModern industrial agriculture is a very highly coupled systemwith dependencies on many uncontrollable components. Thisis in general why introducing a system that can facilitate earlydetection early response is a good idea.

ACKNOWLEDGEMENTSWe would like to thank all the people who participated in theinterviews and helped ground this project: stdansk Land-brugsradgivning, Nordsjllands Landboforening, Ryegaardand Trudsholm godser and our supervisors and technical per-sonnel at ITU.

REFERENCES1. Barrientos, A., Colorado, J., Cerro, J. d., Martinez, A.,

Rossi, C., Sanz, D., and Valente, J. Aerial remote sensingin agriculture: A practical approach to area coverage andpath planning for fleets of mini aerial robots. Journal ofField Robotics 28, 5 (2011), 667689.

2. Bramley, R. Lessons from nearly 20 years of precisionagriculture research, development, and adoption as aguide to its appropriate application. Crop and PastureScience 60, 3 (2009), 197217.

3. Eisenbeiss, H. The autonomous mini helicopter: apowerful platform for mobile mapping. Int. Arch.Photogramm. Remote Sens. Spat. Inf. Sci 37 (2008),977983.

4. Merz, T., and Chapman, S. Autonomous unmannedhelicopter system for remote sensing missions inunknown environments. In Conference on UnmannedAerial Vehicle in Geomatics (UAV-g) (2011), 1416.

5. Primicerio, J., Di Gennaro, S. F., Fiorillo, E., Genesio, L.,Lugato, E., Matese, A., and Vaccari, F. P. A flexibleunmanned aerial vehicle for precision agriculture.Precision Agriculture 13, 4 (2012), 517523.

6. Xiang, H., and Tian, L. Development of autonomousunmanned helicopter based agricultural remote sensingsystem. In ASABE Annual Meeting. Portland, Oregon(2006).

7. Zecha, C., Link, J., and Claupein, W. Mobile sensorplatforms: categorisation and research applications inprecision farming.

25http://www.libelium.com/development/waspmote/documentation/agriculture-board-technical-guide/

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IntroductionRelated WorkMain Contributions

MethodThe Design of the prototypeSemi-structured InterviewsIn Agriculture, not one day is the same

Paper PrototypingExperience PrototypingEvaluationThe Final DesignFlying the DroneWeb ApplicationInteractive web-based map applicationChallenges encountered in development

EvaluationFuture WorkCopterPhotogrammetryData ManagementIntegration with Danish Agro ICTCamera SystemIntegration with Agricultural ProductsCrowd Sourcing and User Submitted ReadingsManual OverrideAuthentication and scaleabilityIntegration with wireless sensor networks (WSN)

ConclusionAcknowledgementsREFERENCES