Intelligent Farm Management and Predictive Crop Analytics Platform

A Django-based web application that leverages Machine Learning to assist farmers in decision-making through crop recommendation and price prediction.

🚀 Features

• Crop Recommendation: Suggests the most suitable crops based on soil nutrients (Nitrogen, Phosphorous, Potassium), soil type, and season using a Random Forest Classifier.
• Price & Yield Prediction: Predicts future crop prices and potential yields using LSTM (Long Short-Term Memory) neural networks.
• Market Data: Provides real-time/historic seed and market price information across different states and districts.
• User Authentication: Secure signup and login system for farmers and researchers.
• Dataset Management: Ability to load and process custom crop datasets for analysis.

🛠️ Tech Stack

• Backend: Django (Python)
• Machine Learning: TensorFlow, Keras, Scikit-learn
• Data Processing: Pandas, NumPy
• Database: MySQL (via PyMySQL)
• Frontend: HTML, CSS (Vanilla)

📋 Prerequisites

Before running the project, ensure you have the following installed:

• Python 3.7+
• MySQL Server

⚙️ Installation & Setup

1. Clone the Repository

   git clone https://github.com/saiteja-5h0/Intelligent-Crop-Recomendation.git
   cd Intelligent-Crop-Recomendation
   

2. Install Dependencies

   pip install -r requirements.txt
   

3. Database Configuration

   • Create a MySQL database named crop.
   • Ensure your MySQL user is root with password root (or update the credentials in CropApp/views.py).
   • The system uses a signup table for user management.

4. Run the Application

   python manage.py runserver
   

   Access the application at http://127.0.0.1:8000/index.html.

🧠 Machine Learning Models