Use Case: PostGIS and Agribotics
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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.
Use Case: PostGIS and Agribotics
- 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 ï Agriculture in Australia ï Potential of RPASs in Agriculture ï Current capabilities (imaging) ï An example scenario that utilises PostgreSQL: ïĄ JSON ïĄ Import capabilities (Geospatial Data Abstraction Library) ïĄ Vector Geometry functions ïĄ Raster functions
- 4. Agriculture in Australia ï Australian farmers produce enough food to feed 80 million people ï 93% of the domestic food supply is meet by Australian farmers ï Export market is valued at $42 Billion per annum ï Agriculture and related services represent 12% of Australia's GDP ï Significant new investment in this sector
- 5. Challenges ï Climate change resulting in unpredictable rainfall ï Falling/Unpredictable commodity prices ï Skill shortages ï Lower dollar resulting in higher cost of fertilisers and farming machinery ï High wastage in the supply chain (estimated > 30%)
- 6. Common Direction ï Natural Resources ï Agriculture Within Society ï Competitiveness ï Innovation, Research, Development
- 7. Drones in Agriculture ï Use of Remotely Piloted Aircraft Systems (RPAS) is not really new: ïĄ Radio controlled target drones were used in the military in the 1930âs ïĄ Electronic information gathering and dropping of propaganda leaflets was utilised in the 1960âs ï The availability of hobby grade kits has accelerated use of RPAS in commercial applications
- 8. Scout Aerial and Media
- 9. Drones in Agriculture ï Why RPAS in agriculture?
- 10. Drones in Agriculture ï Why RPAS in agriculture? ïĄ Large and remote ïĄ Largest = 23,677sq km 50th largest = 5,334 sq km
- 12. Types of Systems ï Fixed Wing ï Multirotor
- 13. Current Capabilities ï Data - Detailed information ïĄ Sensor information ï· Temperatures ï· Moisture ï· Co2 ïĄ Payloads ï· Cameras
- 14. Current Capabilities ï Data: ïĄ Flight plans ïĄ Flight tracks ïĄ Telemetry data ïĄ Sensor/Imaging data: âą Obstacle mapping âą Yield estimates âą Ground cover profiling âą Temp/Pressure profiling âą Spore, pollen counts âą C02, ammonia sensing âą Data capture from ground sensors âą Water quality/survey âą Vegetation status âą Pest damage âą Dam/Drainage survey âą Topography âą Pathogen/weed tracking âą Wind/shear profiles âą Detassel assessment
- 15. Capabilities - Next ï Protection â Protecting crops from harm ïĄ Precision herbicides, pesticides and fungicides ïĄ Disease detection and tracking ïĄ Identification of wildlife threats and thwarting them ï· Birds ï· Rabbits ïĄ Insect/worm identification
- 16. Capabilities - Future ï Seeding and Harvesting ïĄ Crop planting ïĄ Feeding ïĄ Harvesting
- 17. Why is PostgreSQL/PostGIS useful ï Organisation of lots of information ï Integrated toolset ï Flexibility and extensibility
- 18. A scenario ï Import a mission plan into PostgreSQL for future use ï Find stored mission plans that are within a distance of where I need to collect data from on next trip ï Importing logged track, telemetry data, sensor data and images after performing a survey flight ï Process a set of collected images to extract useful data ï Identify and export waypoints of problem areas requiring further investigation by agricultural consultants
- 19. Flight Plans and Tracks
- 20. Flight Plans and Tracks ï Tracking information â GPS exchange format
- 21. Flight Plans and Tracks ï OGR2OGR ïĄ -lco GEOMETRY_NAME â sets column name ïĄ -lco LAUNDER â makes more PostgreSQL compatible ïĄ -nln tablename â Sets the table name to be created ïĄ -f âPostgreSQLâ (or âTIGERâ âESRI Shapefileâ âGMLâ ï OGRInfo
- 22. Imagery ï The combination of Drones and todays digital camera is enabling smaller organisation to offer NDVI services ï Much higher resolution ï Cloudy days arenât so much an issue ï Reflected radiation doesnât have to travel so far (NIR-VIS)/(NIR+VIS)
- 23. Imagery ï Layers found on the back of healthy leaves reflect higher levels of near infrared NIR NIR Unhealthy leaves Healthy leaves
- 24. Landsat Program ï Longest running program for acquiring satellite imagery of the earth ï Landsat 1: Visible light (RGB) & near infrared ï Landsat 8: GeoTIFF with pixel size to 30 meters
- 25. NDVI Image ï Band values from -1 to 1 ï High levels of reflected NIR closer to 1 ï Low levels of reflected NIR closer to -1 ï -1 to 0 normally non living material ï Colour coded image with legend is often the final representation
- 26. Rasters Landsat8 handbook ï Raster2pgsql ïĄ Import single or multiple rasters ïĄ Break up rasters ïĄ Create thumbnails/overviews ï Gdal_translate ïĄ Modify resolution ï Gdalwarp ïĄ Modify spatial reference system
- 27. Index Accuracy ï Variations during the yearâŠ.. 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
- 32. (Channel 1) NIR (Channel 2) Blue (Channel 3) ndvi (NIR-VIS) (NIR+VIS) NIR = Channel 1 VIS = Channel 2
- 33. Image processing ï Generate OrthoMosaic
- 34. Image Processing
- 35. Beyond NDVI
- 36. Map Algebra ï ST_MapAlgebra ï ST_Colormap ï ST_PixelAsPoint ï ST_Contains ï ST_Intersection ï ST_Histogram ï ST_AsJPEG
- 37. Summary ï Main capability of RPASs in Agriculture (imaging) ï Typical image processing ï Current features of PostgreSQL that are useful ï Next: ïĄ How to capture and represent the data required to produce useful results ïĄ Automation of the process