AI that pays its way by harvest
Multispectral crop intelligence that catches canopy stress weeks before the naked eye — turning drone, satellite and soil-sensor data into plot-by-plot decisions that lifted yields 22% while cutting water and chemical spend.
A Hominis app module · your agriculture data, app-ified
See crop stress weeks before the eye can
Multispectral, soil and weather data fused into plot-by-plot decisions across the season.
NDVI crop-health monitoring
Multispectral imagery scored into a per-parcel NDVI map that flags canopy stress, disease and nutrient deficiency before it spreads — surfaced while there's still a season to save it.
Stress zones caught weeks earlier
Yield prediction by field zone
Temporal models project harvest tonnage plot by plot, not farm-wide averages, so planning, contracts and storage are sized to what the field will actually deliver.
22% higher yields across pilot farms
Variable-rate irrigation
Soil-sensor, weather and canopy data fused into zone-level water schedules — drench the parcels that need it, hold back on the ones that don't.
30% water-usage reduction
Targeted pest & disease control
Spot-treatment recommendations replace blanket spraying, applying chemistry only where the imagery shows an outbreak forming.
45% less pesticide applied
Multi-source data fusion
Satellite passes, drone flights, IoT soil probes and weather forecasts unified into one decision layer — no single feed left to interpret in isolation.
Satellite + drone + soil + weather
Built for the variability of real fields
The architecture and UX that carried it to a 3.5x first-season ROI.
A model built for agronomic variability
A temporal graph neural network captures the spatial relationships between field zones while a custom computer-vision pipeline segments individual plant-health indicators — the architecture that lets one model generalize across crop types, soil conditions and climate zones instead of breaking at the next farm.
A dashboard a farm manager actually uses
The intelligence ships in an interface built for non-technical operators, and the system improves continuously through active learning from farmer feedback — adoption in the field, not just accuracy in a notebook, is what carried it to a 3.5x first-season ROI.
One method, tuned for agriculture
Assess, Transform, Sustain — the cycle every organization runs, dropped one level deeper for your sector's pains and sticky aspects.
Assess
Data-readiness audit across drone, satellite, soil and weather feeds
We inventory every data feed already on the farm — drone flights, satellite passes, IoT soil probes, weather and historic yield records — and map where canopy stress, water waste and over-spraying are costing the most. Output is a ranked opportunity map keyed to your crop mix, soil zones and climate, so the first model targets the parcels with the biggest payback, not a generic pilot.
Transform
Multi-modal CV + temporal-graph models behind an operator-grade dashboard
We stand up the multi-modal pipeline — computer vision segmenting plant-health indicators, a temporal graph neural network reading spatial relationships between field zones — and fuse satellite, drone, soil and weather into per-parcel NDVI, yield and intervention outputs. It ships inside a dashboard built for farm managers, so recommendations reach the people driving the sprayer and the irrigation valves.
Sustain (AIOps)
Season-over-season retraining with active learning from farmer feedback
Agriculture's ground truth changes every season, so models are retrained on each harvest's outcomes and corrected through active learning when operators flag a missed or false stress zone. Monitoring is tuned to the growing calendar — drift watched as soil, weather and crop rotations shift — so the system holds its edge across years, not just the first growing season.
Precision agriculture, from drone to decision
Drone imagery + predictive models optimising irrigation, fertilisation and pest management.