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