Agriculture uses AI to produce more with fewer inputs and to manage risk.
Applications
- Crop monitoring: satellite and drone imagery to detect stress, disease and pests.
- Precision agriculture: applying water, fertiliser and pesticides only where needed.
- Yield prediction: forecasting harvests from weather, soil and imagery.
- Weed detection: vision systems targeting weeds for removal or spraying.
- Livestock monitoring: detecting illness and behaviour changes.
- Harvesting robots for delicate crops.
- Supply chain forecasting.
Data Sources
Satellites, drones, soil sensors, weather stations, machinery telemetry and farm records.
Challenges
- Connectivity in rural areas.
- Cost for smaller farms.
- Variation between regions, crops and seasons limits model transfer.
- Data ownership between farmers and technology providers.
Getting Value
Start with decisions that directly affect costs or yields, and validate models on local conditions.