This document discusses using drones and PostgreSQL/PostGIS for agricultural applications. It describes how drones can capture imaging data for tasks like measuring crop health through NDVI analysis. PostgreSQL is useful for organizing the large amounts of drone data, like flight plans, sensor readings, and imagery. The document provides an example of importing this data into PostgreSQL and using PostGIS functions to process imagery, extract waypoints of problem areas, and more.

1. PostGIS and Agribotics
Gary Evans

2. Agriculture in Australia
Interest grew in Agribotics from my hobbies
where spatial awareness is very important:

3. Outline
 Agriculture in Australia
 Potential of RPASs in Agriculture
 Current capabilities (imaging)
 An example scenario that utilises PostgreSQL:
 JSON
 Import capabilities (Geospatial Data Abstraction Library)
 Vector Geometry functions
 Raster functions

4. Agriculture in Australia
 Australian farmers produce enough food to feed 80
million people
 93% of the domestic food supply is meet by
Australian farmers
 Export market is valued at $42 Billion per annum
 Agriculture and related services represent 12% of
Australia's GDP
 Significant new investment in this sector

5. Challenges
 Climate change resulting in unpredictable rainfall
 Falling/Unpredictable commodity prices
 Skill shortages
 Lower dollar resulting in higher cost of fertilisers and
farming machinery
 High wastage in the supply chain (estimated > 30%)

6. Common Direction
 Natural Resources
 Agriculture Within Society
 Competitiveness
 Innovation, Research, Development

7. Drones in Agriculture
 Use of Remotely Piloted Aircraft Systems (RPAS) is
not really new:
 Radio controlled target drones were used in the military in the
1930’s
 Electronic information gathering and dropping of propaganda
leaflets was utilised in the 1960’s
 The availability of hobby grade kits has accelerated
use of RPAS in commercial applications

8. Scout Aerial and Media

9. Drones in Agriculture
 Why RPAS in agriculture?

10. Drones in Agriculture
 Why RPAS in agriculture?
 Large and remote
 Largest = 23,677sq km 50th largest = 5,334 sq km

11. Drones in Agriculture

12. Types of Systems
 Fixed Wing
 Multirotor

13. Current Capabilities
 Data - Detailed information
 Sensor information
 Temperatures
 Moisture
 Co2
 Payloads
 Cameras

14. Current Capabilities
 Data:
 Flight plans
 Flight tracks
 Telemetry data
 Sensor/Imaging data:
• Obstacle mapping
• Yield estimates
• Ground cover profiling
• Temp/Pressure profiling
• Spore, pollen counts
• C02, ammonia sensing
• Data capture from ground sensors
• Water quality/survey
• Vegetation status
• Pest damage
• Dam/Drainage survey
• Topography
• Pathogen/weed tracking
• Wind/shear profiles
• Detassel assessment

15. Capabilities - Next
 Protection – Protecting crops from harm
 Precision herbicides, pesticides and fungicides
 Disease detection and tracking
 Identification of wildlife threats and thwarting them
 Birds
 Rabbits
 Insect/worm identification

16. Capabilities - Future
 Seeding and Harvesting
 Crop planting
 Feeding
 Harvesting

17. Why is PostgreSQL/PostGIS useful
 Organisation of lots of information
 Integrated toolset
 Flexibility and extensibility

18. A scenario
 Import a mission plan into PostgreSQL for future use
 Find stored mission plans that are within a distance
of where I need to collect data from on next trip
 Importing logged track, telemetry data, sensor data
and images after performing a survey flight
 Process a set of collected images to extract useful
data
 Identify and export waypoints of problem areas
requiring further investigation by agricultural
consultants

19. Flight Plans and Tracks

20. Flight Plans and Tracks
 Tracking information – GPS exchange format

21. Flight Plans and Tracks
 OGR2OGR
 -lco GEOMETRY_NAME – sets column name
 -lco LAUNDER – makes more PostgreSQL compatible
 -nln tablename – Sets the table name to be created
 -f “PostgreSQL” (or “TIGER” “ESRI Shapefile” “GML”
 OGRInfo

22. Imagery
 The combination of Drones and todays digital
camera is enabling smaller organisation to offer
NDVI services
 Much higher resolution
 Cloudy days aren’t so much an issue
 Reflected radiation doesn’t have to travel so far
(NIR-VIS)/(NIR+VIS)

23. Imagery
 Layers found on the back of healthy leaves reflect
higher levels of near infrared
NIR
NIR
Unhealthy
leaves
Healthy
leaves

24. Landsat Program
 Longest running program for acquiring satellite
imagery of the earth
 Landsat 1: Visible light (RGB) & near infrared
 Landsat 8: GeoTIFF with pixel size to 30 meters

25. NDVI Image
 Band values from -1 to 1
 High levels of reflected NIR closer to 1
 Low levels of reflected NIR closer to -1
 -1 to 0 normally non living material
 Colour coded image with legend is often the final
representation

26. Rasters
Landsat8 handbook
 Raster2pgsql
 Import single or multiple rasters
 Break up rasters
 Create thumbnails/overviews
 Gdal_translate
 Modify resolution
 Gdalwarp
 Modify spatial reference system

27. Index Accuracy
 Variations during the year…..
CanolaCorn

28. NDVI Image from a multi spectral camera

29. Image from a multi spectral camera

30. ndvi
CCDs in cameras
capture
frequencies up to
around 1300 nm
(Near Infrared)
(Channel 1) Red
(Channel 2) Blue
(Channel 3) Green
IR filter blocks
700nm upwards

31. Camera Modification

32. (Channel 1) NIR
(Channel 2) Blue
(Channel 3)
ndvi
(NIR-VIS)
(NIR+VIS)
NIR = Channel 1
VIS = Channel 2

33. Image processing
 Generate OrthoMosaic

34. Image Processing

35. Beyond NDVI

36. Map Algebra
 ST_MapAlgebra
 ST_Colormap
 ST_PixelAsPoint
 ST_Contains
 ST_Intersection
 ST_Histogram
 ST_AsJPEG

37. Summary
 Main capability of RPASs in Agriculture (imaging)
 Typical image processing
 Current features of PostgreSQL that are useful
 Next:
 How to capture and represent the data required to produce
useful results
 Automation of the process