walter-kudzai-kamanga/agriconnect_ai

AgriConnect AI – Farm-to-Market Logistics Intelligence Platform Problem Statement Across Africa, smallholder farmers lose around30–50% of their produce due to poor access to affordable, reliable transport. Trucks often return empty, while nearby farmers struggle to get their harvest to markets. Consequences: * Food spoilage * Reduced farmer income * Inefficient use of transport resources * Limited economic growth in rural communities Objectives 1. Connect smallholder farmers to available transporters in real-time. 2. Optimize delivery routes to reduce spoilage and cost. 3. Provide market insights best prices, nearby demand, predicted arrival times. 4. Demonstrate multi-context AI reasoning via MCP: transport, weather, and market data fused intelligently. Target Users * Primary - Smallholder farmers in rural Africa * Secondary - Transport operators (trucks, vans, local couriers) * Tertiary - Market traders, agricultural cooperatives, NGOs MCP AI Architecture Description 1. Input Layer * Farmer data (voice/text, crop type, quantity, location) * Transporter data (GPS, vehicle capacity, availability) * Market data (prices, demand, location) * Environmental data (weather, road conditions) 2. MCP Context Integration * AI agent fuses all inputs into single contextual reasoning layer * Generates delivery matches, optimal routes, and predicted delivery times 3. Decision Engine * Predicts best transport match and route * Generates alerts (delays, spoilage risk) * Updates dashboard and notifications 4. Output Layer * Farmer (SMS ) * Admin dashboard (for NGOs or cooperatives) Storyboard 1.Step 1 - Farmer opens the app, inputs crop (e.g., tomatoes), quantity, and location via voice or text. 2. Step 2 - MCP agent retrieves nearby available transporters, road conditions, weather forecasts, and market demand. 3. Step 3 - AI recommends optimal transport match and delivery route; sends notification to farmer and transporter. 4. Step 4 - Dashboard …