GRover: developing sensors for vineyard use

CSIRO AGRICULTURE AND FOOD

Everard Edwards and Matt Siebers Mark Thomas, Rob Walker

"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).

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

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:

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.

• 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?

• 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

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.

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.

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’.

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

02468

1012141618

0 200000 400000 600000

Leaf

are

a/pa

nel (

m2)

Number of voxels

Provides an accurate estimate of canopy area.

Using GRover to measure pruning weight

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 Pr

unin

g w

eigh

t / p

anel

(kg)

Dual wavelength ‘echidna’ LiDAR, DWEL

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

• 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

Demonstration of ‘point cloud’

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.