https://github.com/nagarathnatechie/agrigenius
Agrigenius is a machine learning-based crop recommendation system that suggests the best crop to cultivate based on soil and climate conditions. Built with Flask, Python, and Random Forest, it helps farmers make data-driven decisions to maximize yield.
https://github.com/nagarathnatechie/agrigenius
flask python random-forest
Last synced: about 2 months ago
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Agrigenius is a machine learning-based crop recommendation system that suggests the best crop to cultivate based on soil and climate conditions. Built with Flask, Python, and Random Forest, it helps farmers make data-driven decisions to maximize yield.
- Host: GitHub
- URL: https://github.com/nagarathnatechie/agrigenius
- Owner: NagarathnaTechie
- Created: 2025-02-27T18:31:44.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-08T19:44:45.000Z (over 1 year ago)
- Last Synced: 2025-03-08T20:27:30.327Z (over 1 year ago)
- Topics: flask, python, random-forest
- Language: Jupyter Notebook
- Homepage:
- Size: 794 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🌾 AgriGenius: Intelligent Crop Recommendation System 🌱
**AgriGenius** helps farmers and agricultural enthusiasts by recommending the most suitable crop based on soil and environmental parameters using Machine Learning.
👉 [Live Demo](https://agrigenius-pc9m.onrender.com)
---
# 🌟 Key Features
- 🚜 Predicts the best crop to cultivate based on:
- Nitrogen, Phosphorus, Potassium content
- Temperature & Humidity
- pH level
- Rainfall
- 🧠 Trained Random Forest model with pre-processed scalers
- 🌐 Simple Web Interface using Flask
---
# 🛠️ Tech Stack
| Category | Technologies |
|----------------|-------------------------------|
| **Backend** | Python, Flask |
| **ML Model** | Random Forest (Scikit-learn) |
| **UI** | HTML (Jinja2 via Flask) |
| **Deployment** | Render |
---
# ⚙️ Installation
```bash
# Clone and setup
git clone https://github.com/NagarathnaTechie/AgriGenius.git
cd AgriGenius
# Install dependencies
pip install -r requirements.txt
# Run the app locally
python app.py
```
---
# ⚠️ Disclaimer
## 🚨 Important:
This project is a learning and experimental prototype. It is not intended for real-world agricultural decision making. The predictions provided by this system may be inaccurate or misleading.
The author is not responsible for any outcomes, losses, or damages caused by using this tool in actual farming practices.
Please consult certified agronomists and agricultural experts for making farming decisions.
## Here is snapshot of the project
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# 📞 Contact
Have questions or suggestions?
📧 Email(nagarathnashenoy123@gmail.com)
🔗 LinkedIn(https://www.linkedin.com/in/nagarathna-shenoy-457751218).