GRover: developing sensors for vineyard use
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The document discusses the development and application of sensors for vineyard management, particularly focusing on non-destructive and advanced technologies like Lidar for crop assessment. It highlights the potential of sensors in improving agricultural practices, emphasizing the need for accurate field measurements related to phenomics, yield forecasts, and disease management. The CSIRO and its projects aim to enhance precision agriculture through innovative sensing and data analytics, which are expected to significantly impact the industry in the coming years.
GRover: developing sensors for vineyard use
- 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 âsensorsâ since early 19th century: ⢠Daguerrotypes (1830âs), ⢠bolometer (1880), sensitive to 0.0001°C, ⢠X-ray image (1896), ⢠etc. Non-classified data from satellite imaging since 1960: ⢠e.g. infra-red â used for monitoring cloud cover. Sensing Boulevard du Temple - 1838
- 4. Landsat 8 (2013) â 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, ⢠âinstantâ 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 âcolour spectrumâ sensor. ⢠Field measurements are labour intensive (whether for science or farming) and always benefit from greater ground coverage. âDigital viticultureâ 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 & âDigi Vitâ 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 âphenomicsâ; 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 âpoint cloudâ. Point cloud is analysed to provide field measurements. One example âis voxelisationâ.
- 11. Using GRover to measure canopy size The LiDAR sensor generates a 3D âpoint cloudâ. Point cloud is analysed to provide field measurements. One example âis voxelisationâ. 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 âechidnaâ 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 âechidnaâ 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 âpoint cloudâ
- 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.