Why it matters
Without this system, vigour gaps, misapplied inputs, missed early stress signals, and inconsistent block performance can occur due to manual scouting limits and delayed data interpretation.
From design to safety
Design
AI-informed canopy zoning design
NDVI concepts, data needs, and system requirements inform site planning, trellis layout, block allocation, irrigation strategy, and initial drone-satellite coordination and harvest windows aligned to season planning calendars.
- Drone Multispectral Camera
- Satellite Imagery Service
- NDVI Processing Server
- GIS Tablet
Build
Baseline data collection framework
Flight plans, sensor calibrations, and mosaics establish baselines; data pipelines connect to vineyard records and ERP foundations including block-level yield history pruning schedules and crew calendars for readiness.
- Drone Multispectral Camera
- Ground Truth Sensor
- NDVI Processing Server
- GIS Tablet
Operation
Ongoing vigour monitoring and zoning
Regular NDVI updates identify stressed zones, track leaf area indices, and refine pruning, irrigation, and foliar input timing and canopy changes guiding thinning and cover crop decisions seasonally.
- Drone Multispectral Camera
- Satellite Imagery Service
- NDVI Processing Server
- GIS Tablet
Maintenance
Data hygiene and model retraining
Calendarized calibration, sensor maintenance, and seasonal retraining ensure accurate vigour classification and map consistency. Data hygiene routines, archive retention, and model drift checks sustain long-term reliability across vintages.
- NDVI Processing Server
- Ground Truth Sensor
- Satellite Imagery Service
- GIS Tablet
Safety
Traceable decision signing and recordkeeping
All vigour decisions, maps, and input actions are logged and signed by qualified personnel for compliance and audits. All operator actions align with safety rules, pesticide approvals, and irrigation permits valid.
- Drone Multispectral Camera
- Ground Truth Sensor
- GIS Tablet
- NDVI Processing Server
Part by part
- 1
Drone Multispectral Camera
- Watch for
- Lens dust, calibration drift, and spectral band misalignment during flight operations.
- AI action
- Calibrate bands and flag drift; trigger fresh flight plan if anomaly detected.
- 2
Satellite Imagery Service
- Watch for
- Cloud cover gaps, update latency, and coverage gaps in target blocks.
- AI action
- Fill gaps with modeled NDVI if coverage lags more than 24 hours.
- 3
NDVI Processing Server
- Watch for
- Data pipeline failures, incorrect georeferencing, or outlier vigour values.
- AI action
- Re-run mosaic, reproject coordinates, alert data team of anomalies.
- 4
Ground Truth Sensor
- Watch for
- Sensor drift, placement shifts, or moisture sensor saturation.
- AI action
- Flag drift, prompt recalibration, trigger field-check checklist.
- 5
Variable Rate Irrigation Controller
- Watch for
- Valve faults, pressure drops, or inconsistent block pacing.
- AI action
- Issue VR irrigation commands and log zone-level water allocation.
- 6
GIS Tablet
- Watch for
- Inaccurate offline maps, synchronization failures, or annotation drift.
- AI action
- Synchronize maps, approve zone edits, push to field crew devices.
Worked example
Catching a vigour gap before it expands
Illustrative scenario with sample data.
A dormant block shows rising NDVI anomalies indicating stress; early irrigation adjustment and targeted foliar nutrition are needed.
Signal in
blockID: B12, date: 2026-06-14, ndvi: 0.72 (block baseline 0.65), leaf_area_index: 3.2, soil_mcv: 1.8, sensor: drone-01, flight: 6-band
System out
alert: vigour gap identified in block B12; action: adjust VR irrigation by 15% in B12, apply targeted foliar spray; map updated with new zone.
Human sign-off: Vineyard manager approves the irrigation adjustment and foliar application plan after reviewing the NDVI map and soil data.
How to roll it out
Phase 1
Weeks 1 to 2
Install baseline hardware, configure flight plans, and train staff on NDVI data interpretation.
Phase 2
Weeks 3 to 6
Validate data pipelines, calibrate sensors, and run pilot blocks for vigour zoning.
Phase 3
Weeks 7 onward
Scale across all blocks, integrate with vineyard ERP, and establish routine review cadence.
Where people stay in charge
- All decisions to change irrigation, nutrient, or pesticide inputs must be signed off by the vineyard manager or agronomist.
- Data-derived actions with potential regulatory or labeling implications require counsel from the winery compliance lead before execution.
- Sensitive health indicators must be reviewed in weekly safety and operations meetings before field implementation.
Questions owners and crews ask
What data sources power NDVI vigour mapping in vineyards?
NDVI vigour mapping relies on drone-based multispectral imagery, satellite imagery, and ground truth sensors. These inputs feed the processing server to generate vigour maps and zone classifications.
How does AI decide where to apply variable rate inputs?
AI analyzes vigour zones, historical yield data, soil moisture, and canopy density to assign input rates, then triggers VR controllers and updates the vineyard map. Decisions are reviewed by the agronomist.
How is data quality ensured for NDVI mapping?
Calibration routines, band alignment checks, and georeferencing validation run before each season. Data anomalies trigger alerts and require field checks by the viticulturist.
Who signs off on vine health decisions informed by NDVI?
The vineyard manager, winemaker, and agronomist jointly approve input changes; regulatory compliance and traceability records are kept in the ERP and GIS systems.