TerraGrow: Grow Smart, Plant Right

Sample Machine Learning for Crop Growth

Overview

TerraGrow is an intelligent web app that uses a Random Forest model for crop recommendations and the Llama3-8b-8192 model from Groq to suggest yield improvement strategies. By analyzing factors like nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, and pH, it provides data-driven insights to help farmers optimize planting decisions and achieve higher yields.

Dataset

Crop Recommendation Dataset is being used for this project. You can get this dataset from kaggle.

✨ Features

• Smart Crop Recommendations
  Utilizes Random Forest models to suggest the best crops based on environmental data like NPK levels, temperature, humidity, and pH.

• Yield Improvement Suggestions
  Powered by the Llama3-8b-8192 model from Groq, it provides actionable strategies to boost crop yield, such as nutrient adjustments and irrigation optimization.

• User-Friendly Interface
  Offers an intuitive platform for inputting data and accessing tailored recommendations effortlessly.

• Precision Agriculture Made Simple
  Enhance efficiency and sustainability by aligning crop choices and yield strategies with specific conditions.

Demo

TerraGrow-daytona.webm

🚀 Getting Started

Open Using Daytona

1. Install Daytona: Follow the Daytona installation guide.

2. Create the Workspace:

   daytona create https://github.com/Subash-Lamichhane/TerraGrow-daytona

3. Add 3000 as forwarded ports.

4. Set up the environment variables by creating a .env file in the backend directory and add given details::

   PORT=3000
   GROQ_API_KEY=<YOUR_GROQ_API_KEY>

5. Start the Application:
   Navigate to backend folder

   npm start

   Navigate to frontend folder

   npm run dev

Screenshots

Landing Page:

Home Page:

Article

Check out my article on integrating Daytona into a Machine Learning project with React, Node, and Python on Dev.to: Integrate Daytona into a Machine Learning Project

Technologies Used

• Daytona: Development environment manager.
• React: Frontend library for building user interfaces.
• Vite: Fast frontend build tool.
• Tailwind CSS: Utility-first CSS framework.
• Express: Backend framework for APIs.
• Groq API: Fast AI interface.
• python-shell: Run Python scripts from Node.js.
• Scikit-learn: Machine learning library for Python.