KilimoPRO (kilimo.pro) is an AI-powered agricultural intelligence platform I am building for Kenyan and African farmers. The name combines "Kilimo" (Swahili for agriculture) with "PRO" denoting professional-grade intelligence. This article is the technical story of how it's being built.
The Problem
Kenya has 5.5 million smallholder farming households producing 70% of the country's agricultural output. Maize yields average 1.4-1.8 t/ha against a global average of 5.8. Post-harvest losses consume 30-40% of food produced. The extension worker-to-farmer ratio is 1:785 (FAO recommends 1:400). There is simply no way to provide personalized agricultural advisory to millions of farmers through human extension workers alone.
22 Data Sources
I spent weeks researching and verifying 22 operational data sources that KilimoPRO integrates:
- KilimoSTAT (statistics.kilimo.go.ke) — Kenya's official agricultural statistics platform with a documented Swagger API
- FAOSTAT API — Global agricultural statistics via SDMX-based REST API, launched in late 2024
- KALRO Digital Agriculture Platform (keep.kalro.org) — Soil, land, crop, and weather data
- Google Earth Engine — Satellite imagery (Sentinel-2, Landsat) for NDVI crop health monitoring
- CHIRPS — Daily satellite rainfall data at 5km resolution
- Africa Regional Data Cube — 17 years of analysis-ready satellite data for Kenya
- Semantic Scholar API — Latest agricultural research papers for the advisory module
- Firecrawl — Web scraping for agricultural news and market prices
On-Device Crop Disease Detection
One of the most powerful features is on-device crop disease detection using TensorFlow Lite. I trained a MobileNetV3 CNN on the PlantVillage dataset (54,305 images, 26 diseases), quantized it to int8 (reducing model size from 25MB to 5MB), and it runs inference in under 500ms on a low-end Android phone. Completely offline. This means a farmer in rural Makueni with no internet can point their phone at a diseased maize leaf and get a diagnosis in seconds.
Multi-LLM Council
For complex farming decisions, a single AI response isn't enough. KilimoPRO implements a "Council of High Intelligence" — 5 AI expert personas (agronomist, economist, climate scientist, extension officer, risk manager) that each analyze the question from their professional perspective, then synthesize their deliberation into a unified recommendation. This is inspired by the council-of-high-intelligence project on GitHub.
NASIP Alignment
KilimoPRO is designed to operationalize Kenya's National Agri-Food Systems Investment Plan (NASIP) 2026-2030, a KES 1.08 trillion investment framework. The platform directly supports 7 of 9 NASIP flagship programs, particularly Flagship 7 (Data & Digital Infrastructure, KES 45B) by serving as the farmer-facing interface for the National Agriculture Data Hub.
Quant-Based Price Forecasting
For market intelligence, I borrowed quantitative finance techniques from the awesome-quant project. The price forecasting module uses EWMA volatility (RiskMetrics λ=0.94), GARCH(1,1) for volatility clustering, Value at Risk (VaR 95% and 99%), RSI for overbought/oversold signals, and SMA crossovers for buy/sell/hold recommendations. These models, originally designed for financial markets, adapt surprisingly well to agricultural commodity prices.