abfAgri connects live satellite imagery, open-source AI agents, and consequence modelling to tell fertilizer producers across the SADC region exactly where crop stress is happening, what it will cost if untreated, and which of your products fixes it — field by field, in under 5 seconds.
✅ Omnia product recommendation
Trusted by SADC's leading fertilizer & agri-input producers
Most agri-AI stops at prediction. abfAgri models how effects propagate through the crop system, quantifies what inaction costs in ZAR, and serves the optimal product recommendation from your catalogue — closing the loop from satellite pixel to sale.
| Alert-based AI (e.g. mAgri) | abfAgri — Consequence Model |
|---|---|
| "Nitrogen deficiency detected" | "N deficiency → 23% yield loss in 7d → R 41,200/ha at risk" |
| Diagnoses from a single photo | Correlates photo + live NDVI + soil moisture + weather forecast |
| "Apply fertilizer" (generic) | "Apply Omnia LAN 28% @ 150kg/ha within 48h → 92% recovery" |
| No revenue attribution loop | Tracks recommendation → purchase → yield outcome → reorder |
| Consumer app — no B2B data | Producer dashboard with territory & geospatial heatmaps |
| Pan-African — no SADC specificity | Calibrated per SADC country: soils, crops, seasons, regulations |
Sentinel-2/Landsat NDVI, soil moisture anomaly, and weather overlays fused with farmer's field photo. Precise farm-boundary data pulled from live satellite feeds.
Causal graph propagates: NDVI decline → nutrient stress → growth inhibition → yield reduction → ZAR revenue at risk — quantified for actual field size and current commodity price.
Best-fit product selected from your catalogue. Application rate, method, and intervention window specified. Farmer sees net ZAR gain vs cost of inaction — no convincing needed.
Purchase data feeds back. Outcome data improves the model. You see which products moved where, and why — compounding accuracy and commercial advantage over time.
abfAgri's engine layers geospatial state, temporal evolution, causal reasoning, multi-scenario simulation, and profit-optimised recommendation — moving producers from reactive alerts to anticipatory, consequence-aware commercial intelligence.
Fuses satellite NDVI, soil moisture, weather, GPS field boundaries, crop stage, and farmer-uploaded photos into a single geospatial field model. Handles connectivity gaps common across SADC rural areas via async sync.
Tracks NDVI trajectories, soil moisture trends, and crop growth stage curves across the season. Detects stress before visible field symptoms, maps seasonal demand patterns, and forecasts product requirement windows by geography.
Maps causal pathways: low N → reduced chlorophyll → NDVI decline → stunted growth → yield loss → ZAR revenue risk. Enables intervention reasoning that distinguishes correlation from cause — so recommendations are defensible, not probabilistic guesses.
Simulates Product A vs B, apply now vs in 3 days, 100 vs 150 kg/ha — presenting each as a concrete ZAR ROI scenario. Turns field uncertainty into structured, comparable choices. Agronomists and farmers see tradeoffs explicitly, not intuitively.
Evaluates simulated interventions against farmer objectives (yield recovery, cost) and your commercial constraints (product availability, margin, geography). Outputs a ranked recommendation — your SKU, rate, timing, delivery — with full justification the farmer trusts and your sales team can track.
abfAgri's satellite intelligence platform gives fertilizer producers a real-time view of crop health across every field in the SADC region — detecting stress before farmers even notice the problem on the ground.
Multi-spectral satellite imagery processed daily. NDVI, EVI, and LAI indices computed per farm boundary. Stress alerts triggered when vegetation indices fall below crop-stage thresholds — days before visible symptoms appear in the field.
Soil moisture, precipitation anomaly, and rainfall data fused with SAR imagery for soil structure analysis. Real-time drought stress maps updated daily across all 16 SADC member states.
Automated field delineation from satellite imagery. Each field gets its own geospatial profile — area, soil type, elevation, slope, historical yield zones, and proximity to distribution points for your products.
Your RSA, Zimbabwe, Zambia, or Mozambique sales reps get a live map of crop stress severity, deficiency type, and affected area — so they pre-position the right product before the retailer calls.
abfAgri is calibrated for the crops, soils, seasons, and regulations of every SADC member state — from the maize triangle of South Africa to the tobacco lands of Zimbabwe, the cotton belt of Zambia, and the rice paddies of Madagascar.
abfAgri's intelligence layer is a pipeline of four specialised AI agents — each handling a distinct layer of the 5-layer decision stack. Independently orchestrated, fully auditable, and replaceable without vendor lock-in. Full agent definitions exported as open JSON specs.
Analyses field photos and satellite indices to identify crop stress, nutrient deficiencies, pests, and diseases across 40+ SADC crop varieties. Returns structured JSON with condition, confidence score, severity, and causal chain.
Takes diagnosis output and propagates effects through the causal graph: deficiency → yield impact → ZAR revenue at risk. Runs in 2–3s. Outputs consequence chain with confidence intervals and time-to-threshold calculations.
Selects the optimal product from the fertilizer producer's SKU catalogue based on nutrient profile, crop stage, soil pH, application method, and geographic availability. Returns product name, rate, timing, and projected ZAR ROI.
Orchestrates live satellite intelligence to compute NDVI anomaly, soil moisture deviation, and weather outlook per field boundary. Feeds structured geospatial context into the consequence model pipeline — fully automated, updated every 3 hours.
Real data from public annual reports, JSE filings, FASA Fertilizer Reviews, and FAO statistics. No fabricated market share figures — cited per source.
Omnia and Yara have broad SADC distribution but operate with traditional sales models — no consequence modelling, no field-level AI recommendations. Their scale is a distribution advantage, not a digital moat.
YaraConnect provides basic weather alerts and crop calendars. It does not model yield loss, quantify financial risk in ZAR, or generate SKU-specific ROI recommendations. The capability gap to abfAgri's 5-layer stack is substantial.
Omnia's R8.9B agriculture segment serves the exact farmer base abfAgri's consequence engine addresses. Licensing abfAgri into Omnia's co-op and retail network turns every agronomist visit into a ZAR-quantified consequence conversation — driving product pull-through at scale across 8 SADC countries.
Six high-conviction growth opportunities identified from satellite field data, SAFEX price signals, and SADC agronomic gap analysis. Each pocket is sized by addressable revenue and ranked by time-to-close.
Full closed loop — recommendation → purchase → yield outcome → reorder signal. See exactly which AI recommendations drove which sales across which territories.
Deploy under your brand in weeks. Your app, your colours, your farmer relationships — abfAgri's consequence engine running silently underneath.
REST + GraphQL API connects field diagnostic data into your ERP, CRM, or order management system. Geospatial stress signals flow directly into your commercial workflows.
Consequence reports delivered in isiZulu, Afrikaans, Shona, Chichewa, Kiswahili, Portuguese, French, Malagasy, and more — ensuring adoption across all farmer segments.
POPIA (SA), PDPA (Tanzania), and SADC data localisation compliant. Farmer data stored in-country. Zero third-party harvesting. You own your farmer relationships.
Executive dashboards showing territory-level crop stress, product demand forecast, revenue attribution, and seasonal opportunity maps — ready for quarterly business reviews.
abfAgri changed the conversation with our co-op partners entirely. The consequence model shows farmers their yield risk in rands — not agronomic jargon. Our Fertasa product attachment rate at the counter jumped 38% in the first season.
The satellite heatmaps are exactly what our Highveld sales team needed. We now pre-position LAN and potassium before demand peaks — not after. The geospatial intelligence gives us a genuine week's head start over competitors.
We white-labelled abfAgri as "NutriFlo Advisor" in eight weeks. The open-source agent stack means we're not locked into any cloud AI vendor. Limpopo citrus farmers use it daily — micronutrient retail sell-through is up 35%.
Select a field scenario or enter your own parameters. The AI pipeline returns a diagnosis, yield impact, and product recommendation in seconds.
Get a live demo connecting your product catalogue to satellite field data across the SADC region — and watch the consequence model close the loop from pixel to purchase.