Precision Agriculture
AI-powered crop intelligence for modern farming. KenAgri combines multispectral imagery, soil sensors, and weather data to increase yields by 35%, cut water use by 50%, and detect 200+ crop diseases at 99% accuracy.
Six AI capabilities that transform raw field data into season-winning decisions.
Multispectral drone and satellite imagery analysed by AI to detect stress, nutrient deficiency, and disease at field scale; days before visual symptoms appear.
7–30 day harvest forecasts with ±4–6% accuracy. Fuse crop health indices, historical data, and weather models to plan logistics, storage, and market timing.
Identify 200+ crop diseases, pests, and nutrient deficiencies at 99% accuracy using AI image analysis. Trigger targeted treatment before infestations spread.
AI-driven irrigation scheduling based on real-time soil moisture, evapotranspiration, and weather forecasts. Reduce water consumption by up to 50% without yield loss.
Integrate NPK, pH, moisture, and NDVI data from ground sensors and satellite into a unified soil health model. Apply inputs precisely where and when needed.
Correlate field conditions with hyperlocal weather forecasts to trigger preventive actions; frost protection, disease risk alerts, and optimal spray windows.
From field data to prescriptive action — precision agriculture in three steps.
Connects to drone imagery, satellite feeds, ground sensors, and weather data — building a unified picture of every field, updated continuously.
Deep learning models detect crop stress, pest infestation, disease spread, soil anomalies, and growth deviations — field by field, zone by zone.
Variable-rate prescription maps trigger automated irrigation, targeted treatment, and yield forecasts — so your team acts on intelligence, not guesswork.
Precision agriculture intelligence from field to estate scale.
Variable-rate inputs, optimised irrigation scheduling, and early disease intervention for corn, wheat, and soy.
Monitor growth stages, microclimate, and pest pressure across orchards and vineyards to improve yield and shelf life.
Block-level analytics, disease surveillance, and harvest optimisation for tea, rubber, palm, and cocoa estates.
IoT-driven water level management, anaerobic stress detection, and tillering stage monitoring for optimal grain fill.
Greenhouse climate automation, CO₂ management, and integrated pest surveillance for tomatoes, capsicum, and leafy greens.
Continuous animal behaviour monitoring, early illness detection, and automated feeding alerts for dairy and poultry operations.
Monitor cold storage temperature, humidity, and ethylene levels. Alert on deviations before spoilage affects produce quality.
Track provenance from field to factory with sensor-verified data. Meet traceability requirements for export and premium markets.
Connect your first field in minutes. Get a free crop health assessment and yield forecast for your current season.
KenAgri detects early-stage stress signatures in multispectral imagery 7-14 days before visible symptoms appear, depending on disease type. The system flags chlorophyll reduction and water stress patterns that precede visible discolouration — giving agronomists time to intervene before yield loss becomes irreversible.
Yes. KenAgri uses multi-band spectral analysis (NDVI, NDRE, and thermal where available) combined with ground sensor data to differentiate stress signatures. Nitrogen deficiency shows a different spectral pattern from fungal disease even when both produce yellowing visible to the eye. Where signatures are ambiguous, the system flags for agronomist review rather than making an incorrect automated call.
KenAgri can send control signals to compatible smart irrigation controllers and valve actuators via API or direct integration. It uses soil moisture sensor readings, evapotranspiration models, and growth-stage data to calculate zone-level water requirements and triggers irrigation accordingly. For farms without automated hardware, it outputs a daily irrigation schedule report instead.
No. KenAgri falls back to drone imagery and ground sensor data when satellite coverage is unavailable. The platform is designed for data gaps — it uses whatever sources are available for each field pass and flags fields where data quality is below the detection threshold rather than making low-confidence inferences.
Onboarding takes 1-2 weeks. We ingest your field boundaries, historical yield records, and current sensor configuration. The first drone or satellite pass produces a baseline crop health map. Active monitoring and alerts begin immediately from the first processed pass — the predictive models improve over the first full growing season as they learn your specific fields.
