Presentation by Professor Kim Bryceson Full webinar:https://www.youtube.com/watch?v=lxANBB95b58&list=PLG25fMbdLRa5qsPiBGPaj2NHqPyG8X435&index=12 Individual snippet:https://youtu.be/jeHLDDwWSBc?list=PLG25fMbdLRa5qsPiBGPaj2NHqPyG8X435

1. Slide 1
Smart Agriculture at UQ Gatton
IoT and Drones
Professor Kim Bryceson
Associate Dean Academic (Science Faculty)
The University of Queensland
Australia

2. Slide 2
The ‘Buzz’ of today’s Agriculture
The Internet of Things, Big Data, Drone technology, Smart
Agriculture
– these are “Buzz” phrases commonly tossed around now-a-days that
encompass a vast array of different technologies which themselves
underpin the capability of the world to deliver a Food Secure future

3. Slide 3

4. Slide 4
Wireless Sensor Network (WSN)
Mesh typology, flexible, self configuring & healing (fault tolerant)
Robust and low maintenance
Multiple sensors 6+
Multiple communication protocols
• Point to point, star, mesh.
• >12 radio interfaces (i.e. Wi-Fi, 3G, LoRA, ZigBee)
Nodes solar powered + 24hrs backup
Wi-Fi connected to Eduroam
GRAND TOTAL in the network = 60 nodes, 4 Meshliums, 8 cameras
All OK Problem develops Self healing
Nodes
Meshlium
(router for
communication)

5. Slide 5
Libelium Nodes and Sensors
Smart Agriculture
Smart Water
Smart Environment
Smart Security

6. Slide 6
Types of Sensors Installed
Libelium Waspmote
Node Model
Sensors possible
Smart Environment Carbon Monoxide (CO), Carbon Dioxide (CO2), Oxygen (O2), Methane
(CH4), Hydrogen (H2), Ammonia (NH3), Hydrogen Sulfide (H2S), Nitrogen
Dioxide (NO2), Ozone (O3), Hydrocarbons (VOC), Isobutane, Ethanol,
Toluene, Temperature, Humidity, Pressure atmospheric, dust particles,
Luminosity.
Smart Agriculture Soil moisture, Soil temperature, Leaf wetness, Solar radiation, Atmospheric
pressure, Stem diameter, Anemometer, Wind vane, Pluviometer, Ambient
temperature, Humidity
Smart Water Temperature, pH, dissolved oxygen, oxidation-reduction potential,
conductivity (salinity), turbidity, dissolved ions (Calcium (Ca2+), Fluoride (F-),
Fluoroborate (BF4
-), Nitrate (NO3
-), Bromide (Br-), Chloride (Cl-), Cupric
(Cu2+), Iodide (I-), Silver (Ag+)
Smart Security Water presence, Liquid level, Liquid flow, Temperature, Humidity, Luminosity

7. Slide 7
IoT and Big Data Use
DataUseIoT

8. Slide 8
Interactive GUI
(DGLux5)
QR Code:
Scan to access site
Dashboard

9. Slide 9
So What?
Ok – we have large amounts of biophysical data coming in –
what else do we need – and for what?
• Want to use it for lots of things
 Pasture monitoring & management
 Animal monitoring & management
 Crop monitoring & management
 Education
More data ! - eg Aerial data
Drones

10. Slide 10
Back to the Future
Remote Sensing (since early 1970s)
– Gathering data at a distance across large areas for spatial variability
monitoring
– Eg Satellite or Airborne
Chequered history in Ag with 2 main issues
1. Data
• Cost of acquisition & processing
• Revisit Frequency
• Resolution
• Requires cloud/haze free environment
• Computing/processing grunt available
2. Lack of skills available in the Agricultural sector

11. Slide 11
Developing Specialist Skills
Understanding Spatial
Variability & Agricultural
Remote Sensing
Set up Agricultural Remote
Sensing Lab
– Fosters the design & build of small drones
which students learn to fly and collect
remotely sensed data of the agricultural
environment
Design & Build Drones
Precision Farming

12. Slide 12
Drones
Reason for use
– Cheap platform to carry high res sensors
– Collect data on spatial variability (SV)
Optimising Production Efficiency & Quality
Minimising Risk & Environmental Impact
= SMART FOOD PRODUCTION
= SMART ENVIRONMENTAL MANAGEMENT
= SMART SKILLS DEVELOPMENT
Multiple skills and fun!
Drones at UQ
– 2017 UQ’s fleet consists of 5 DJI Phantoms + 4 bespoke
Quads + 3 bespoke Hexicopters + 10 MiniAg drones
– 10 mins to learn to fly
** Vegetables Australia Magazine Article March/April 2015 http://www.ausveg.com.au/publications/VA/VA-MarApr2015.pdf
SV at ground level
SV from the Air

13. Slide 13
Design & Build = Active Learning & Research
Learn general design & build
principles including testing
Learn to fly
Undertake project
Write Report
– develop Recipe/Poster/Paper

14. Slide 14
Navas, 2015
UQGatton Drones
Drone Design Projects 2013-2016
TestDrone, BugDrone, NetDrone, WeedDrone, WildDrone, WhaleDrone,
RFIDDrone, Mini-AgDrones with NoIR camera
2013
2014/15
2014
2015
2015/2016
2015/16
2013-2016

15. Slide 15
First Student project in 2013
2013 Masters Project in Fiji - Image Data from Mangrove swamp Taveuni – DJIPro 1 GoPro 3
2003
2013
2013 Change

16. Slide 16
WeedDrone
Bryceson, 2014

17. Slide 17
Beneficial BugDrone
Rowena Dione Photography 2015

18. Slide 18
NetDrone
Photo: Sotiris Ioannou UQ, 2015

19. Slide 19
MSS Data displayed on screen (B&W)
IR Red Green
MSS

20. Slide 20
UQMiniAg Drone
Navas, 2015

21. Slide 21
MinAgDrone Sensor NDVI Data

22. Slide 22
Multispectral Sensors/Cameras
Based on Raspberry Pi & 1-4 NoIR multiplexed
cameras.
4 selectable wavelength bands (research quality
filters)

23. Slide 23
RFIDDrone

24. Slide 24
RFIDDrone Directional Antennae Data
Drone as represented in software
Drone
Direction
Of
Movement (Y)
Strength
of signal

25. Slide 25
Types of Research & Learning involved
Electronics - including soldering
Avionics
Programming – Arduino/Raspberry Pi
Physics - Electromagnetic Spectrum
Maths - Spectral indices + Crop growth indices
Chemistry - Chemicals in plants
Plant physiology - how does it work
Animal welfare - Cattle herd management)
Food Traceability
Design + Build
Science
Management

26. Slide 26
Summer & Winter Projects
2 Summer Science Research Scholars
– BAg Sci – Drone and Sensor Development for Crop Monitoring
– BEng - Computer Vision in the Piggery for rat monitoring / animal
welfare monitoring
1 Master of Engineering Student
– Robotic Arm for capturing Drones out of sky for autonomous
recharging
3x BEng student interns
– Dev of automated underwater camera to monitor phytoplankton in
environmental lakes
– IoT – Equine health monitoring via remote wireless sensors
– Development of visualisation dashboard for environmental
monitoring of waste water management
BEng graduate
– Remote RFID monitoring of cattle from drone technology

27. Slide 27
The End – Thank you and Qs?
Navas 2016