GrowSense 🌿

An intelligent agriculture platform that helps farmers automate irrigation and make better farming decisions using real-time sensor data, AI-driven insights, and modern web technologies.

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Project Summary

GrowSense leverages IoT, cloud computing, and AI to provide farmers with an efficient, data-driven solution for smart farming. Using ESP32 microcontrollers and various sensors, the system collects environmental data (soil moisture, temperature, rainfall, etc.) and transmits it to a backend server. Farmers can securely monitor and control their devices via a user-friendly web interface.

Through integrated AI models and external APIs, GrowSense delivers valuable insights like optimal irrigation timings and crop rotation strategies, making agriculture more sustainable, efficient, and productive.

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👥 Team Details

• Team: ACE
• Category: Agri-Tech
• Team Members:
  • Pramit Kaucha Magar
  • Nisrit Baral
  • Nikash Lamsal
  • Aarogya Parajuli

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🔧 Features

• Real-time environmental monitoring (soil moisture, rainfall, temperature)
• AI-powered recommendations for irrigation and crop rotation
• Secure user authentication & device registration
• Multiple irrigation control modes: Auto / Timer / Manual
• Personalized dashboards for each user
• Weather forecasting using external APIs
• Data visualization using interactive charts

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🛠️ Tech Stack

Hardware:

• ESP32 Microcontroller (Programmed in C)

Backend:

• Node.js
• Express.js

Database:

• Firebase (Real-time data)
• MongoDB

Frontend:

• Next.js
• Chart.js (Real-time data visualization)

APIs & External Tools:

• OpenWeatherMap API (Weather forecasting)
• Agri APIs (Crop & field-specific data)
• OpenRouter API (AI-powered field suggestions)
• JWT (Secure authentication)
• bcrypt.js (Password hashing & user data security)

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🔒 Security

• User authentication with JWT
• Passwords securely hashed with bcrypt.js
• Device registration with unique hex-codes to ensure user-specific access

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🌐 How It Works

1. Data Collection:
   ESP32 sensors collect data on soil moisture, temperature, rainfall, etc.

2. Data Transmission:
   Sensor data is sent to the backend server for processing.

3. Data Analysis & Visualization:
   The backend processes data and presents it via the frontend dashboard built with Next.js & Chart.js.

4. AI Recommendations:
   Integrated AI models (via OpenRouter API) provide actionable farming insights.

5. Control System:
   Farmers can manage irrigation through auto, timer, or manual modes from their dashboards.

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📂 Project Status

This project was built as part of a hackathon under the Agri-Tech category.

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🎥 Live Demo

You can watch the full live demo of the GrowSense project here:
🔗 Watch Demo

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