CROP RECOMMENDATION SYSTEM USING MACHINE LEARNING

Project Overview

The Crop Recommendation System is a web-based application that predicts the most suitable crop to grow based on various environmental factors and soil nutrient content. The application leverages a machine learning model trained on historical agricultural data and provides recommendations by inputting the following parameters:

Nitrogen (N): Nutrient essential for plant growth.

Phosphorus (P): Helps with the energy transfer in plants.

Potassium (K): Important for overall plant health.

Temperature: Measured in Celsius, crucial for plant metabolic processes.

Humidity: Important for transpiration and water absorption.

pH: Measures soil acidity/alkalinity, critical for nutrient availability.

Rainfall: Amount of precipitation that can affect water availability for crops.

Based on these inputs, the model predicts the crop best suited for these conditions from a predefined set of crops, including rice, maize, apple, banana, etc.

Technologies Used

Flask: Used to create the web application and handle HTTP requests.

Pandas: For data manipulation and processing.

NumPy: For efficient numerical computation.

Scikit-learn: For training and implementing the machine learning model.

Gunicorn: Used to deploy the Flask app in a production environment.

Project Files

• app.py: The Flask application that handles the web interface and model predictions.
• index.html: The frontend HTML file where users input soil and environmental conditions.
• new_rf_model.joblib: The saved machine learning model (RandomForestClassifier) used for predictions.
• features_data.joblib: The file containing feature information used for prediction input.
• requirements.txt: Contains the dependencies required for the project.
• wsgi.py: The entry point for running the Flask app with Gunicorn. running the Flask app with Gunicorn.