The Crop Recommendation System is a machine learning-based application designed to assist farmers in making informed decisions about which crops to plant. By analyzing critical factors such as soil nutrients (Nitrogen, Phosphorus, Potassium), temperature, humidity, pH, and rainfall, the system predicts the optimal crop for a given set of environmental conditions. This project aims to enhance agricultural productivity, promote sustainable farming, and empower farmers with accessible technology.
- Accurate Crop Predictions: Tried different models and finally utilized a Random Forest classifier for precise crop recommendations based on environmental and soil data.
- User-Friendly Web Interface: Built with Flask, the application provides an easy-to-use platform for farmers to input data and receive real-time recommendations.
- Data-Driven Insights: Helps farmers make informed decisions, improving yield and optimizing resource use.
- Accessible Anywhere: Lightweight, web-based application designed to work on various devices.
- Programming Language: Python
- Frameworks: Flask (for the web application)
- Frontend: HTML, Bootstrap CSS
- Libraries: NumPy, Pandas, Scikit-learn, Matplotlib
Clone this repository:
- git clone https://github.com/Apoorva-011/Crop-Prediction.git
- cd Crop-Recommendation-System
- python app.py
- Open your browser and go to http://127.0.0.1:5000.
The dataset used in this project includes features: Soil Nutrients: Nitrogen (N), Phosphorus (P), and Potassium (K) levels. Environmental Conditions: Temperature, Humidity, pH, and Rainfall. The data was sourced from Kaggle and curated for model training and evaluation.
