Define
a repeatable workflow to monitor vine growth using multispectral vegetation indexes
Establish
a workflow to build a 3D measurable twin of the vineyard using Lidar data
Define
and measure areas of vine row that are missing, and correlate to missed yield
Lidar and Multispectral Drones
in Viticulture by LOW ALTITUDE. Low Altitude provides advanced aerial surveys for Viticulturists, using the latest Multispectral imaging sensors mounted on rotary wing UAV platforms.
DJI Mavic 3 , DJI M350
- Multispectral cameras
- Zenmuse L2 Lidar
Vegetation indices: NDVI common crop health indicator, NDRE replaces NDVI in later season, ReCI good indicator of photosynthetic activity, GCI good general indicator of health, PMI sensitive to Powdery Mildew.
Outcomes
- Worked closely with the Vineyard Manager to ensure that the findings mirrored issues on the ground, and also gave the insight he needed.
- Processed data in Pix4DFields and outputs delivered as PDF insight report and statistic tracking.
- Data processed using DJI Terra to create accurate point cloud and model. Model shared with Vineyard Manager via cloud . Advanced processing using Routescene’s LidarViewer Pro. Developed an MVP to identify gaps in the vine, which can be measured. Gaps are defined as a drop in height of greater that 50% of the vine height.
Challenges Faced
- Traditionally monitored by literally walking around fields or vineyards to inspect the plants, taking time and effort best used elsewhere.
- Multispectral sensing detects the light reflected by plant canopy in non-visible wavelengths. This is then measured and converted by vegetation indexes to provide a state of health alongside actionable outputs. By using drones, we can gather much more detailed data than available via satellite.
- LiDAR will enable precision mapping of the complete vineyard and establish a measurable digital twin, which can be updated to monitor growth, predict yield, aid in planning, and detect vine gaps for profit protection.
- Due to grass coverage between the rows, used a histogram function to narrow the filter, leaving us with the vine row data.
Impact (Socio-economic & Environmental)
- The vineyard manager noticed that a handful of leaves in a block were presenting small signs of stress. We flew a survey of the full block that showed that there was a much wider stressed area than visible to the naked eye, which enabled swift remediation through seaweed application. This contained and reversed the situation before it became a wider problem.
- By reviewing the report on photosynthetic activity, the vineyard manager was able to modify the harvest schedule to start picking the grapes from the vines with lower readings. This meant the vines with stronger readings were still active and could be left a little longer.
