Crop Recommendation System using Machine Learning

A machine learning-based system that recommends optimal crops based on soil parameters, weather conditions, and environmental factors to enhance agricultural productivity.The model predicts the most suitable crop for a given region to enhance agricultural productivity

Key Features

• Data Input: Collects soil parameters (N, P, K), climate data, and location details
• ML Models: Implements Random Forest, SVM, and Gradient Boosting algorithms
• Recommendation Engine: Suggests best crops and required fertilizers
• Web Interface: User-friendly Flask web application

Technologies Used

• Python: Programming language used for model development, data preprocessing, and web application development.
• Scikit-learn: Machine learning library used for model training, evaluation, and prediction.
• Pandas: Data manipulation library used for data preprocessing and analysis.
• NumPy: Library for numerical computing used for handling arrays and mathematical operations.
• Flask: Web framework used for building the user interface and handling HTTP requests.
• HTML/CSS: Markup and styling languages used for designing the web interface.
• JavaScript: Scripting language used for client-side interactions and enhancing the user interface.

Installation

Prerequisites

• Python 3.8+
• pip package manager

Setup Instructions

1. Clone the repository:

   git clone https://github.com/Astrother26/Crop-Detection-using-Machine-Learning
   cd Crop-Detection-using-Machine-Learning

   python -m venv venv source venv/bin/activate # Linux/Mac venv\Scripts\activate # Windows

   Install the required dependencies: pip install -r requirements.txt Run the application: python app.py Access the application through the web browser at http://localhost:5000