https://github.com/mansi1309/crop-recommendation-system
The Crop Recommendation System is a machine learning-based application designed to assist farmers in selecting the most suitable crop to cultivate based on soil and climatic conditions. By analyzing factors like soil nutrients (NPK), temperature, humidity, and rainfall, the system provides accurate recommendations to optimize agricultural yield.
https://github.com/mansi1309/crop-recommendation-system
css flask html5 javascript jyputer-notebook machine-learning-algorithms python3
Last synced: 3 months ago
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The Crop Recommendation System is a machine learning-based application designed to assist farmers in selecting the most suitable crop to cultivate based on soil and climatic conditions. By analyzing factors like soil nutrients (NPK), temperature, humidity, and rainfall, the system provides accurate recommendations to optimize agricultural yield.
- Host: GitHub
- URL: https://github.com/mansi1309/crop-recommendation-system
- Owner: mansi1309
- Created: 2024-08-18T08:49:14.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-30T06:33:06.000Z (over 1 year ago)
- Last Synced: 2025-01-28T18:44:08.685Z (over 1 year ago)
- Topics: css, flask, html5, javascript, jyputer-notebook, machine-learning-algorithms, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 3.57 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **Crop Recommendation System 🌾**
This project is a machine learning-based system that helps farmers determine the most suitable crop to grow based on various environmental conditions. It uses a dataset containing soil and climate parameters to make predictions, ensuring optimized agricultural output and sustainability.
---
## **Features**
- Predicts the best crop to grow based on input parameters such as:
- Nitrogen, Phosphorus, Potassium levels in the soil
- Temperature and Humidity
- Rainfall
- User-friendly web interface for farmers and users.
- Provides a clear and actionable recommendation.
- Fully responsive UI with attractive design.
---
## **Technologies Used**
### **Frontend**
- HTML5, CSS3, and Bootstrap
- Templates: Jinja2 (for Flask integration)
### **Backend**
- Python (Flask Framework)
- Machine Learning:
- Random Forest Classifier
- Support Vector Machine (SVM)
### **Database**
- **Dataset**: Crop Recommendation Dataset (`crop_recommendation.csv`)
---
### **Screenshorts**
