Agriculture Intelligence
Causal AI for crop yield, irrigation, fertilization and climate-resilient agriculture decisions.
Causal AI for yield, irrigation and climate-resilient farming
Agriculture Intelligence models the causal relationship between irrigation, fertilisation, soil, climate and yield. It recommends the intervention set that raises yield while cutting water and nitrogen use, with particular calibration for arid and semi-arid conditions.
Key capabilities
Separates the effect of water, nutrients, soil and weather on measured yield.
Water demand modelled per plot and crop stage, with drip and pivot comparison.
Split-dose strategies that raise uptake and reduce leaching.
Planting-window shifts and variety choice tested against heat-stress projections.
Use cases
- Water-scarce production
Cut irrigation volume at constant yield on sandy and saline soils.
- Input cost control
Reduce fertiliser spend without losing output.
- Sustainability reporting
Track carbon intensity and water productivity per tonne produced.
Frequently asked questions
Which crops are supported?
Cereals, date palm, forage and high-value horticulture, with additional crops calibrated on request.
Do I need field sensors?
Sensors improve precision but are not required — satellite indices, soil maps and weather series give a usable baseline.
How much water can be saved?
Typical modelled savings are 25–35% on sandy soils moving to scheduled drip irrigation at constant yield.