Frequently Asked Questions
Table of Contents
- General Questions
- How the platform works in practice
- Identifying What’s Planted Where: Satellite-Based Crop Detection
- Crop Production Forecasting
- Detecting adverse events and monitoring risk
General Questions
What is Oracle Agriculture Intelligence?
Oracle Government Data Intelligence for Agriculture is a SaaS application on Oracle Cloud Infrastructure (OCI), typically procured by:
- Government ministries
- Public-sector agencies
- Non-government agencies (NGOs)
It unifies:
- Satellite imagery
- Weather data
- Soil data
- Historical production data
- Government or private datasets
The platform delivers real-time visibility and predictive insights on crop production and risk drivers.
It identifies:
- What crops are growing where
- Production forecasts before harvest
- Weather-related risks at national-to-local levels
It quantifies potential impacts by crop, highlights under/overperforming regions, and supports proactive interventions through structured projects and feedback loops.
How does it work?
Using advanced analytics, AI/ML, and Bayesian inference, the system:
- Integrates diverse datasets at temporal and geographic resolution
- Surfaces trends and anomalies
- Provides dashboards and a Visual Explorer for map-aligned analysis
- Monitors adverse events
- Estimates potential impacts on expected yields
Crop detection models are specifically trained and tuned for each country to reflect local crop types and conditions.
Why does it matter?
Early visibility into shortfalls or surpluses—weeks before harvest—enables:
- Risk mitigation
- Resource prioritization
- Preparedness planning
The application helps teams:
- Plan, track, and adjust interventions
- Retain historical lessons
- Align stakeholders around a transparent source of truth
What are the benefits to its users?
- Proactive risk management
- Reliable regional monitoring and forecasting
- Actionable response planning
- Faster, clearer decision-making
- Guided best-practice interventions
What to expect in a demo
- National dashboard walkthrough
- Visual Explorer navigation
- Identification of at-risk crops and regions
- Comparison of current vs. historical production
- Initiating response projects
How the platform works in practice
Which tools and capabilities does Agriculture Intelligence provide?
The platform is organized around three pillars:
- Visual Explorer
- Insights
- Projects
The Visual Explorer
Provides a map-aligned view from national to field level, showing:
- Where crops are growing
- Production progress in-season
- Comparison to prior years
- Production vs. performance views
- Early under/overperformance detection
Insights
Actionable alerts combining:
- Weather signals
- Hydrology data
- Crop detection
- Seasonal context
Detects:
- Drought
- Flood risk
- Heat stress
Helps quantify exposure by crop and region.
Projects
Allows teams to:
- Initiate response plans
- Select best-practice interventions
- Assign owners and timelines
- Track outcomes
Projects remain connected to insights and create a feedback loop for continuous improvement.
What geographic resolution and refresh rates are available?
Satellite Data
- Sentinel-2 imagery
- ~10 meters per pixel
- 5–7 day revisit cycle
- Cloud removal and preprocessing applied
Weather Data
- Updated daily
- 7-day forecast horizon
- Currently Open-Meteo (ensemble integration in progress)
Dashboards refresh as new validated data are ingested.
Where do the action recommendations originate, and how should they be used?
Recommendations originate from a curated library developed with subject-matter experts.
They:
- Serve as defaults and starting points
- Encourage adaptation to local context
- Track actions, owners, and timelines
- Feed lessons learned back into models
Identifying What’s Planted Where: Satellite-Based Crop Detection
How is crop detection performed?
Using:
- Sentinel-2 imagery (~10m resolution)
- Multi-spectral analysis
- AI/ML classification
- Bayesian uncertainty handling
Features include:
- NDVI (biomass)
- EVI
- NDWI (canopy water content)
- Red-edge metrics (chlorophyll)
Models are trained with:
- Ground truth data
- Country-specific agronomic conditions
- Historical production records
Outputs show planted areas and seasonal progression.
Limits and strengths of Sentinel-2
Strengths
- 10m spatial resolution
- Multi-spectral bands
- National-scale monitoring
Limitations
- Cloud cover
- Small-plot noise
- Sub-pixel uncertainty
Mitigations include temporal smoothing and multi-source fusion.
Model validation and improvement
Validation includes:
- Historical production benchmarking
- Expert review
- Ground truth validation
- Hold-out testing
- Post-season comparison
Models improve through structured feedback loops.
Crop Production Forecasting
How does forecasting work?
- Map planted area
- Convert square meters to hectares
- Apply agronomic assumptions (density, yield factors)
- Adjust for moisture and losses
- Modify using vegetation health signals
- Incorporate weather risk impacts
- Aggregate to municipality and national levels
Use of historical production values
Historical values:
- Provide context
- Allow comparison to prior years
Forecasts and historical data originate from separate sources and are not directly linked.
Detecting adverse events and monitoring risk
General approach
Continuous integration of:
- Satellite data
- Weather feeds
- Hydrology layers
- Terrain data
Operational grid: ~4.8 km
Maintains event continuity across days.
Drought detection
Uses Standard Precipitation Index (SPI):
- 1–3 months: agricultural drought
- Up to 2 years: hydrological drought
Events begin below threshold and continue while conditions persist.
Heatwave detection
Detected when:
- Temperature + humidity exceed thresholds
- Consecutive exceedances tracked
Future releases will include graded intensity scoring.
Flood risk - Current approach
Flood risk flagged using:
- Rainfall anomaly detection
- Terrain flow modeling
- Accumulation zones
- Satellite water indicators
Outputs show presence/absence of elevated flood risk.
Flood risk - Future approach (with enhanced hydrology model)
Future enhancements will include:
- Country-scale hydrology modeling
- Time-varying runoff and flow accumulation
- Flood depth likelihoods
- Soil infiltration modeling
- Drainage density and terrain constraints
Outputs will include:
- Graded hazard surfaces
- Depth-probability bands
- Uncertainty indicators
- Overlay with planted area and crop type