Sycolee78/agrimesh-autonomous

Agent driven Farm OS for Zimbabwe (simulation first MVP) # AgriMesh Autonomous Agent-driven Farm OS for Zimbabwe (simulation-first MVP). ## Vision Build a distributed agricultural intelligence platform where software agents make daily farm decisions, learn from outcomes, and improve productivity/resilience over time. ## Zimbabwe mixed-farm operating context (now encoded) This project now explicitly follows an integrated crop-livestock operating model for Zimbabwe: - Mixed-loop sustainability: crop residues and by-products feed livestock; manure/compost returns nutrients to fields. - AEZ-aware planning: farm decisions must be region- and rainfall-context aware (not one static recipe). - Dual operating cadence: - Daily loops: livestock water/feed/health, irrigation checks, maintenance, security/biosecurity. - Seasonal modes: wet-season vs dry-season priorities and constraints. - Welfare + biosecurity + water-security are hard constraints, not optional optimizations. ## Project Status ### ✅ Phase 1: Complete - Simulation MVP with Irrigation Agent + Yield Forecast Agent - Multi-agent orchestrator with specialized agents - AEZ-aware crop policies for Zimbabwe zones I-V - Streamlit frontend with map visualization ### ✅ Phase 2: Complete (Feb 2026) - **Non-linear yield model** — Crop-specific optimal moisture ranges - **Real weather integration** — Open-Meteo API with Zimbabwe locations - **Hardware interface specs** — Sensor/actuator protocols for IoT - **LLM reasoning agent** — Decision explanations via Ollama/OpenAI/Claude - **Pilot validation** — 12 scenarios, 96.2% avg water savings ### Key Results | Metric | Result | |--------|--------| | Avg Water Savings | 96.2% | | Avg Yield Impact | -2.5% | | Best Case | +5.4% yield (Bulawayo) | | Locations Tested | 4 (Harare, Bulawayo, Mutare, Masvingo) | ## Phase 1 Original Goals Within 6 months, deliver: - Simulated farm environment - 2 working agents: - Irrigation Agent - Yield Forecast Agent - Daily decision logs - Measurable improvement over baseline schedule ## Day 1 De …