Intelligent Irrigation Agent

Plant Animation

Multi‑agent irrigation backend using Google Gemini ADK. Adds proactive monitoring, explainable decisions and API endpoints over an IoT irrigation setup (sensors + pumps).

--- ## Table of Contents - Project Overview - Quick Start - Architecture - Installation - Configuration - API Endpoints - Audio (TTS/STT) - Agriculture (USDA Quick Stats) - Deployment (Cloud Run) - Development --- ## Project Overview This service turns a traditional automated irrigation system into an intelligent assistant. It analyzes sensor history, weather, and crop context to recommend or trigger actions with clear reasons. Key benefits - Proactive: predicts issues 12–48 hours ahead - Efficient: 15–25% water savings via optimization - Explainable: every decision includes reasoning - Context‑aware: integrates weather, soil and plant data - Smart alerts: priority‑based notifications --- ## Quick Start Prerequisites - Python 3.10+ - pip Setup ```bash pip install -r requirements.txt # Create .env and fill keys (see Configuration) # Run locally uvicorn main:app --reload --port 8080 # Health check curl http://localhost:8080/health ``` --- ## Architecture ### System Architecture Diagram

GCP Architecture

### Components Overview **API Layer** (FastAPI) - REST endpoints organized by domain (plants, gardens, agriculture, audio) - WebSocket server for real-time notifications - Background monitoring task (30-60s intervals) **Agent Layer** (Google Gemini ADK) - Multi-agent orchestration with 4 specialized sub-agents - Sensor monitor, nutrient analyzer, alert manager, optimization agent - Explainable AI decisions with reasoning **Tools Layer** (Modular) - Sensors: moisture, tank level, history - Control: irrigation triggers - A

Agents

backend-agent