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.
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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 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
- 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
- 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