GRover: developing sensors for vineyard use by Everard Edwards, Matt Siebers, Mark Thomas & Rob Walker, CSIRO Australia. Presented at the Precision Viticulture of the Riverland event on 1st Dec 2016. This presentation includes information on sensors for the vineyard.

1. GRover: developing sensors for vineyard use
CSIRO AGRICULTURE AND FOOD
Everard Edwards and Matt Siebers
Mark Thomas, Rob Walker

2. "Infrared spectrum" by Ibarrac at English Wikipedia. Licensed under CC BY-SA 3.0 via Wikimedia Commons -
http://commons.wikimedia.org/wiki/File:Infrared_spectrum.gif#/media/File:Infrared_spectrum.gif
Non-destructive sensing
All objects emit radiation (passive sensing) and will absorb some
received radiation (active sensing).

3. Development of ‘sensors’ since early 19th
century:
• Daguerrotypes (1830’s),
• bolometer (1880), sensitive to 0.0001°C,
• X-ray image (1896),
• etc.
Non-classified data from satellite imaging
since 1960:
• e.g. infra-red – used
for monitoring cloud
cover.
Sensing
Boulevard du Temple - 1838

4. Landsat 8 (2013) – free satellite data
14th Oct 2016,
USGS Earth Explorer,
http://earthexplorer.usgs.gov
1 pixel = 100 m x 100 m
We are here:

5. Remote sensing (e.g. satellite, aerial):
• large area sampling,
• but limitations in:
• frequency of coverage,
• speed of data/analysis provision,
• view angles.
Proximal sensing (e.g. tractor mounted):
• potentially higher resolution,
• many possible viewing angles,
• ‘instant’ data availability, (local
hardware / web-based tools).
• but requires local knowledge/skills.
Remote sensing vs. proximal sensing
Multi-spectral image of
vineyard
Remote Sensing Australia
Greenseeker in use during
fertiliser application.

6. • New technologies (sensors and software) have become
pervasive through our lives and society.
• e.g. my phone contains: fingerprint, accelerometer, gyroscope,
proximity, barometer, compass, A-GPS + two RGB cameras, one with a
‘colour spectrum’ sensor.
• Field measurements are labour intensive (whether for
science or farming) and always benefit from greater ground
coverage.
‘Digital viticulture’
Can we utilise these technologies to improve crop management?

7. • Fast Phenomics: grapevine trait characterisation in the field.
• New non-destructive technologies for simultaneous yield, crop
condition and quality estimation.
• New technologies for dynamic canopy and disease management.
• Evaluation of new technology and new scion-rootstock
combinations for improved water use efficiency and reduced costs.
CSIRO & ‘Digi Vit’ Wine Australia Projects
Agriculture & Food

8. Sensors for crop management & phenomics
• Based in the Winegrapes and Horticulture Group at the
Waite Campus, Adelaide.
• Need for non-destructive, sensor based, systems to
make detailed large scale field measurements for:
• Field ‘phenomics’; the assessment of many breeding lines
in-field.
• Crop management utilising plant based measurements.
• New and developing technologies will provide non-
contact sensors for:
• accurate yield forecasts,
• fruit composition/ripeness,
• canopy management,
• disease assessment,
• water management, etc.

9. A mobile vineyard platform (GRover)
• Group has developed a self-propelled
(manual steer) platform with HRPPC.
• Will take multiple sensors at multiple
positions.
• Very large payload weight.
• Can view all parts of vine (aboveground).
• No regulatory compliance required.
• Currently fitted with LiDAR scanner
• biomass components,
• canopy properties,
• potentially yield estimation.
• Stereo RGB and hyperspectral in
process of being added.

10. Using GRover to measure canopy size
The LiDAR sensor generates a 3D
‘point cloud’.
Point cloud is analysed to provide
field measurements.
One example ‘is voxelisation’.

11. Using GRover to measure canopy size
The LiDAR sensor generates a 3D
‘point cloud’.
Point cloud is analysed to provide
field measurements.
One example ‘is voxelisation’.
R² = 0.8906
0
2
4
6
8
10
12
14
16
18
0 200000 400000 600000
Leafarea/panel(m2)
Number of voxels
Provides an accurate
estimate of canopy area.

12. Using GRover to measure pruning weight

13. Pruning weight correlations can differ between
genotypes and environments
R² = 0.6853
R² = 0.8184
0
2
4
6
8
10
12
14
0 10000 20000 30000
Voxel number
Pruning weight vs. voxel number
Pruningweight/panel(kg)

14. Dual wavelength ‘echidna’ LiDAR, DWEL

15. Greyscale renders of DWEL data, a) vines with leaves stripped away to expose
fruits, b) highlighted using a ratio of the two DWEL wavelengths, and c) vines
with leaves in place.
Dual wavelength ‘echidna’ LiDAR, DWEL

16. • Sensing & data analytics is a rapidly expanding
area.
• Likely to see major impact of this technology over
next 5-10 years.
• Offers a wealth of new tools for precision
agriculture.
• LiDAR is a robust instrument for biomass
measurements, but still expensive for commercial
vineyard use and optical parts need to be clean.
• New CSIRO projects are examining a range of
instruments for a variety of vineyard
measurements.
Summary

17. Demonstration of ‘point cloud’

18. CSIRO Agriculture and Food
Everard Edwards
Research Team Leader
t +61 8 8303 8649
e everard.edwards@csiro.au
w www.csiro.au/agriculture
CSIRO AGRICULTURE AND FOOD
Acknowledgements
Jose Jimenez-Berni & others at the High Resolution
Plant Phenomics Centre, Canberra.
Mick Schaefer, CSIRO Land & Water / Auscover.