Farm & boundary mapping
We turn farm locations and boundary information into dependable digital maps for planning, reporting, records and future monitoring.
- Digital field boundaries
- Area calculations
- Field and location maps
Services
Data is only useful
when it answers the question.
AfriScan combines geospatial evidence, remote sensing, environmental data and suitable AI-assisted methods after understanding the area, the operational question and how the result will be used.
Six practical capabilities
Clear outputs for real operational needs.
Crop condition monitoring
We combine repeated satellite observations with AI-assisted analysis to highlight patterns, changes and places that may require closer attention or a field check.
- Vegetation-condition maps
- Change summaries
- Inspection-priority areas
Rainfall, water & forecasting
We organise recent rainfall, short-term forecasts and available water or soil-moisture information around the farm or project area.
- Recent rainfall summaries
- Short-term forecast context
- Water-related indicators
Land & environmental change
We map land-cover change and visible disturbance to support planning, compliance, conservation and rehabilitation monitoring.
- Land-use and land-cover maps
- Change detection
- Disturbance and recovery summaries
Flood, mining & wildfire risk
We use satellite, spatial and analytical models to map flood extent, mining-related disturbance, burned areas and scenario-based wildfire spread or exposure.
- Hazard and exposure maps
- Affected-area estimates
- Scenario-based risk briefings
Custom geospatial analysis
We design a method, dataset and output around a specific land, water, infrastructure or operational question, using machine learning where it is appropriate and defensible.
- Custom maps
- Spatial analysis
- Decision-focused reports
Agriculture platform
CropIntel by AfriScan
AI-powered crop intelligence from space. CropIntel combines field boundaries, satellite Earth observation, rainfall, forecasts, weather and environmental information with GIS, remote sensing and machine-learning methods to support agricultural monitoring and decision-making. Deep-learning models are being integrated into the platform as training and validation data become available.
- Crop condition and vegetation-pattern analysis
- Detection of unusual changes over time
- Water, rainfall and soil-moisture context
- Irrigation and nutrition-condition screening
- Priority areas for field inspection
- Historical trend and predictive analysis where models are validated
CropIntel is a decision-support system. It distinguishes observations, forecasts and model-generated indicators, and it does not replace field diagnosis, farmers, agronomists, extension officers or professional judgement. Recommended changes to irrigation or fertiliser should be confirmed in the field.
Current early-access pilot
Free monitoring for a small group of farms.
AfriScan is inviting cooperatives, farmer-based organisations and district agriculture offices in Ghana to help test CropIntel with 5–20 farms in total. Participants receive crop, water and nutrition monitoring reports at no cost and help provide basic boundary information and field access.
Field observations and user feedback from the pilot will help AfriScan evaluate, validate and improve CropIntel's AI-assisted analytics and developing deep-learning components under real farming conditions.
Apply for the free pilot →
AI
Machine learning + geospatial analysis
AI & Deep Learning
Intelligent analysis, with field reality kept in view.
AfriScan uses artificial intelligence and machine learning to analyse patterns across satellite, weather and environmental datasets. Deep-learning models are being integrated into CropIntel to support more advanced spatial and temporal pattern recognition. Model outputs are presented as estimates or decision-support information, not as direct field measurements.
Pattern recognitionChange detectionPredictive modellingField validation
Built for scale
Process up to 10,000 boundaries in one batch.
Useful for large farm programmes, schemes, surveys and monitoring areas where many boundaries need to be handled consistently.
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