EcoGrow: Greenhouse Environmental Monitoring and Management Platform

EcoGrow is an integrated IoT solution designed for precision agriculture. The platform leverages edge computing, real-time telemetry, and predictive analytics to monitor greenhouse environments and mitigate biological risks to plant growth.

The system utilizes an ESP32-based hardware layer to collect high-granularity sensor data, which is then processed through a dual-service backend architecture to provide real-time monitoring, AI-driven risk assessment, and comprehensive historical reporting.

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System Architecture

The EcoGrow ecosystem is partitioned into several distinct functional layers:

1. Edge Layer (Hardware)

• ESP32 Microcontroller: Serves as the primary IoT gateway.
• Sensor Integration: Captures temperature, relative humidity, and CO2 concentrations.
• Data Ingestion: Transmits telemetry over MQTT to the centralized broker.

2. Communication and Messaging

• MQTT (Paho): High-performance messaging protocol for low-latency data transmission.
• Socket.io: Enables bi-directional, real-time communication between the backend services and the presentation layer.

3. Core Services Layer (Python/Flask)

• IoT Hub: Manages MQTT client sessions and sensor data normalization.
• Inference Engine: Executes predictive models to identify risks of bacterial or fungal proliferation.
• Background Scheduler: Periodically evaluates environmental conditions and triggers autonomous alerts.
• Data Persistence: Interfaces with MySQL for historical record-keeping.

4. Identity and Session Management (Node.js/Express)

• Authentication Microservice: Handles JWT-based user authentication and Google OAuth 2.0 integration.
• Secure Sessions: Implements cookie-based session management and identity verification.

5. Presentation Layer (React/Vite)

• Monitoring Dashboard: Real-time visualization using Recharts for telemetric data.
• Administrative Interface: Dedicated portal for user management and system status monitoring.
• Risk Analytics: Dedicated interface for AI-generated environmental insights.

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Technical Specifications

• Predictive Analytics: Integrated with Gemini AI for advanced environmental risk forecasting and mitigation suggestions.
• Telemetry Processing: Real-time data stream processing with sub-second latency from edge to dashboard.
• Identity Management: Modular authentication service with support for multi-provider login and role-based access control (RBAC).
• Automated Testing: Comprehensive end-to-end validation suite using Selenium.

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Project Structure

ecogrow/
├── src/                    # Frontend Application (React/Vite)
│   ├── pages/              # Functional views (Dashboard, Admin, Auth)
│   ├── components/         # Modular UI elements
│   └── services/           # API integration layer
├── server/
│   ├── backend/            # Core IoT and AI Service (Python/Flask)
│   │   ├── app.py          # Service entry point and scheduler
│   │   ├── ai_service.py   # Risk analysis logic
│   │   └── mqtt_service.py # Telemetry ingestion client
│   ├── index.js            # Authentication Service (Node.js/Express)
│   └── db/                 # Database schema and migration scripts
├── seleniumtest.py         # E2E Automation Suite
└── package.json            # Deployment and dependency configuration

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Installation and Configuration

Prerequisites

• Python 3.9+
• Node.js 18+
• MySQL Server 8.0+
• MQTT Broker (EMQX or Mosquitto recommended)

1. Database Initialization

Execute the following SQL commands or import server/db/schema.sql into your MySQL instance:

CREATE DATABASE ecogrow_db;

2. Core Service Configuration (Python)

Navigate to server/backend/ and install dependencies:

pip install -r requirements.txt

Configure external service parameters in the .env file (Database, MQTT Broker, Gemini API Key).

Start the core service:

python app.py

3. Authentication Service Configuration (Node.js)

Install Node.js dependencies in the project root:

npm install

Start the authentication microservice:

npm run dev:server

4. Development Environment (Frontend)

Initialize the Vite development server:

npm run dev

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Testing Infrastructure

The project includes a Selenium-based automated testing suite for validating critical user flows:

• TC01: Authenticated session establishment.
• TC02: CSV/Excel report generation and data export validation.
• TC03: Real-time notification toggle and UI state persistence.

To execute tests:

python seleniumtest.py

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Deployment Reference

The application is architected for deployment on AWS EC2 instances. Ensure that the security groups are configured to allow traffic on the following ports:

• 5173: Frontend Development Server
• 5000: Core IoT/AI Service
• 4000: Authentication Microservice
• 8883/1883: MQTT Broker Traffic