Satellite Analysis Agent
Processes new scenes into cloud-free indices per field
Tool: NDVI processor · Source: Sentinel / Landsat imagery
Industry
Conceptual visualization onlyAgriculture AI agent operations
Satellite and drone monitoring across fields, irrigation zones and weather stations
Operational layers in view
Satellite imageryNDVI mapDrone capturesWeather and soil sensorsIrrigation zonesCrop alerts
Satellite Analysis Agent
Crop Monitoring Agent
Yield Forecast Agent
Irrigation Agent
Pest-Risk Agent
Farm Advisory Agent
Node activity below is a demonstration using sample agriculture data.
Ask the live agents
Describe the problem in your own words. We classify the industry, assign specialist agents, and you watch the workflow run and produce the visual deliverables — maps, dashboards, charts, tables and reports — instead of one block of text.
Demo mode uses clearly labelled sample data — never presented as verified.
Assigned specialists
Agriculture
- Satellite Analysis Agent
- Crop Monitoring Agent
- Yield Forecast Agent
- Irrigation Agent
- Pest-Risk Agent
- Farm Advisory Agent
Deliverables planned
Interactive map
Charts
Data tables
Cited sources
Recommendations
Pauses for human approval
Agronomist approval
Workflow the run follows
- 1. User request
- 2. Requirement validation
- 3. Industry classification
- 4. Specialist agent selection
- 5. Data and file collection
- 6. Tool authorisation
- 7. Execution plan
- 8. Multi-agent collaboration
- 9. Analysis or generation
- 10. Quality validation
- 11. Human approval
- 12. Visual deliverables
- 13. Monitoring and audit
Demo
Agriculture AI agent operations centre
Workflow: New imagery received → Monitor next pass · Run pending · started — · elapsed 0s
Active agents
0
Tasks running
0
Completed steps
0/9
Tool calls
0
Human approvals
1
Warnings
0
Success rate
0%
Est. completion
36s
Active agents
Pending
Crop Monitoring Agent
Tracks vigour anomalies and stress zones over time
Tool: NDVI processor · Source: Sentinel / Landsat imagery
Yield Forecast Agent
Projects yield ranges per field and season
Tool: NDVI processor · Source: Sentinel / Landsat imagery
Irrigation Agent
Recommends zone-level irrigation volumes and timing
Tool: NDVI processor · Source: Sentinel / Landsat imagery
Workflow canvas
Live activity stream
No activity yet. Start the demo run to watch agriculture agents work through the workflow with sample data.
Demonstration using sample data — no real client operation is shown. Sign in and connect an approved AI provider and authorised industry data source to activate live execution.
Agriculture specialist agents
Each agent owns one step of the workflow, with its own tools, inputs and approval level.
Autonomous
Satellite Analysis Agent
Processes new scenes into cloud-free indices per field
- Inputs:
- Sentinel / Landsat imagery, Drone orthomosaics, Soil moisture sensors
- Tools:
- NDVI processor, Change detection, Yield forecast model
Autonomous
Crop Monitoring Agent
Tracks vigour anomalies and stress zones over time
- Inputs:
- Sentinel / Landsat imagery, Drone orthomosaics, Soil moisture sensors
- Tools:
- NDVI processor, Change detection, Yield forecast model
Autonomous
Yield Forecast Agent
Projects yield ranges per field and season
- Inputs:
- Sentinel / Landsat imagery, Drone orthomosaics, Soil moisture sensors
- Tools:
- NDVI processor, Change detection, Yield forecast model
Review advised
Irrigation Agent
Recommends zone-level irrigation volumes and timing
- Inputs:
- Sentinel / Landsat imagery, Drone orthomosaics, Soil moisture sensors
- Tools:
- NDVI processor, Change detection, Yield forecast model
Autonomous
Pest-Risk Agent
Combines weather and phenology into pest and disease risk
- Inputs:
- Sentinel / Landsat imagery, Drone orthomosaics, Soil moisture sensors
- Tools:
- NDVI processor, Change detection, Yield forecast model
Review advised
Farm Advisory Agent
Turns findings into an agronomist-ready action list
- Inputs:
- Sentinel / Landsat imagery, Drone orthomosaics, Soil moisture sensors
- Tools:
- NDVI processor, Change detection, Yield forecast model
Solution blueprint
Operational context
Industry challenges
- Operational data for agriculture is spread across disconnected systems
- Analysis-heavy work is repeated manually every cycle
- Risks and exceptions are found late
- Reporting and audit overhead grows with volume
Solution patterns
Recommended AI solutions
- Specialist agents that own one step of the workflow each
- Grounded retrieval over your permitted systems only
- Predictive scoring with explainable reasons
- Approval-gated execution into your systems of record
Data readiness
Required data
- Sentinel / Landsat imagery
- Drone orthomosaics
- Soil moisture sensors
- Weather forecasts
- Field boundaries
Controlled process
Example workflow
- New imagery received
- Validate cloud cover
- Compute NDVI
- Detect stress zones
- Forecast yield
Connection points
Integration opportunities
- Farm management systems
- Irrigation controllers
- Weather providers
- GIS services
Measure after baseline
Expected outcomes
- Analysis cycle time
- Manual review effort
- Decision auditability
- Earlier risk detection
Governance controls
Security and compliance
- Row-level security scopes every read to the requesting organisation
- Retrieval is limited to documents the user may already open
- Every agent action, tool call and approval is written to the audit log
- Sensitive steps pause until a named human approves them
Inputs, integrations and outputs
Inputs
- • Sentinel / Landsat imagery
- • Drone orthomosaics
- • Soil moisture sensors
- • Weather forecasts
- • Field boundaries
Integrations
- • Farm management systems
- • Irrigation controllers
- • Weather providers
- • GIS services
Outputs
- • Field health map (GeoJSON, PDF)
- • Yield forecast (CSV)
- • Irrigation and pest advisory (PDF)
Business outcomes to baseline and measure
Analysis cycle time
Target and measurement method agreed during discovery.
Manual review effort
Target and measurement method agreed during discovery.
Decision auditability
Target and measurement method agreed during discovery.
Earlier risk detection
Target and measurement method agreed during discovery.
No performance result is promised before a baseline is measured. Targets are agreed for each engagement and reported against real delivery data.
How This AI Agent Works
The process the agriculture agent team follows, from trigger to approved deliverable.
Crop monitoring workflow
From a farm boundary to an agronomist-reviewed advisory map.
Workflow preview
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
72%
Workflow summary as a text list
- 1. Farm boundary: Input. Field boundaries uploaded or drawn on the map.
- 2. Satellite and drone data: Approved tool. Pull the latest approved imagery for the fields.
- 3. Cloud and quality check: Validation. Reject unusable scenes and report the effective date.
- 4. Vegetation indices: Approved tool. Compute NDVI/NDWI and per-field zonal statistics.
- 5. Detect crop stress: AI agent. Flag stressed zones and rank them by area and severity.
- 6. Compare fields: AI agent. Benchmark each field against its own history and peers.
- 7. Agronomist review: Human approval. An agronomist approves the advisory before it is issued. Human approval required.
- 8. Advisory map and report: Output. Stress map, per-field statistics and recommended actions.
Approval gate: Agronomist review. Deliverable: Map.
Workflow audit for this page
Structure only. Run a demonstration to include events.
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| agriculture-monitoring-workflow | 8 | Yes. Policy: always | Map | Available | Available (signed in) | Available via Studio · requires ai_provider, python_service, earth_engine | Verified at 390px |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Relevant case study
Illustrative case study
Wastewater network GIS for a national water utility
A wastewater network needed utility-GIS structuring, mapping and quality-controlled network intelligence for daily operations.
View all case studiesReady to scope your AI initiative?
Share your objective and constraints. A specialist reviews every request before any recommendation is issued.