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.
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
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
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
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