backend-agent

Multi-agent intelligent irrigation system using Google Gemini ADK. Proactive plant care with explainable AI decisions, real-time monitoring, and automated watering control.

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

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

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