Use Case: PostGIS and Agribotics
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Transcript of Use Case: PostGIS and Agribotics
PostGIS and Agribotics
Gary Evans
Agriculture in Australia
Interest grew in Agribotics from my hobbies where spatial awareness is very important:
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
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
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%)
Common Direction
Natural Resources
Agriculture Within Society
Competitiveness
Innovation, Research, Development
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
Scout Aerial and Media
Drones in Agriculture
Why RPAS in agriculture?
Drones in Agriculture
Why RPAS in agriculture?
Large and remote
Largest = 23,677sq km 50th largest = 5,334 sq km
Drones in Agriculture
Types of Systems
Fixed Wing
Multirotor
Current Capabilities
Data - Detailed information Sensor information
Temperatures
Moisture
Co2
Payloads
Cameras
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
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
Capabilities - Future
Seeding and Harvesting Crop planting
Feeding
Harvesting
Why is PostgreSQL/PostGIS useful
Organisation of lots of information
Integrated toolset
Flexibility and extensibility
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
Flight Plans and Tracks
Flight Plans and Tracks
Tracking information – GPS exchange format
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
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)
Imagery
Layers found on the back of healthy leaves reflect higher levels of near infrared
NIR
NIR
Unhealthy leaves
Healthy leaves
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
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
Rasters
Landsat8 handbook
Raster2pgsql
Import single or multiple rasters
Break up rasters
Create thumbnails/overviews
Gdal_translate
Modify resolution
Gdalwarp
Modify spatial reference system
Index Accuracy
Variations during the year…..
Canola Corn
NDVI Image from a multi spectral camera
Image from a multi spectral camera
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
Camera Modification
(Channel 1) NIR
(Channel 2) Blue
(Channel 3)
ndvi
(NIR-VIS) (NIR+VIS)
NIR = Channel 1 VIS = Channel 2
Image processing
Generate OrthoMosaic
Image Processing
Beyond NDVI
Map Algebra
ST_MapAlgebra
ST_Colormap
ST_PixelAsPoint
ST_Contains
ST_Intersection
ST_Histogram
ST_AsJPEG
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