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---
title: "Crop Recommendation System"
emoji: 🌾
colorFrom: green
colorTo: yellow
sdk: docker
app_file: app.py
pinned: false
license: mit
---
# 🌾 Smart Farming: Crop Recommendation System for Popular Indian Crops
> 🔬 A Machine Learning-based Web Application to Recommend Suitable Crops Based on Environmental Conditions
> 👨🌾 Built by [Saksham Pathak](https://github.com/parthmax2) | IIIT Lucknow | 94.3% Accuracy | Deployed using Flask
---
## 📌 Overview
**Smart Farming** is a crop recommendation system that leverages machine learning to assist Indian farmers in selecting the most suitable crop for cultivation based on key environmental parameters. The system uses a **Random Forest Classifier** trained on soil and climatic data to suggest the best crop from a set of **22 popular Indian crops**.
🌱 This project empowers sustainable agriculture, efficient resource utilization, and smarter farming decisions through AI.
---
## 🧠 Key Features
- ✅ **ML-Powered Predictions** (94.3% Accuracy)
- 🧪 Inputs: Nitrogen, Phosphorus, Potassium, Temperature, Humidity, pH, Rainfall
- 🌾 Outputs: Rice, Maize, Banana, Cotton, Sugarcane, etc.
- 📊 Model Used: **Random Forest Classifier**
- 🌐 **Flask-based Web App** with real-time crop suggestions
- 🎨 Clean and responsive UI (HTML + CSS + JS)
---
## 🚀 Live Demo
[](https://your-deployment-link.com)
*🔒 Link coming soon or hosted locally*
---
## 🖥️ Tech Stack
| Component | Technology |
|---------------|---------------------------|
| 👨💻 Language | Python, HTML, CSS, JS |
| 🔍 ML Model | Random Forest Classifier |
| ⚙️ Backend | Flask (Python) |
| 🖼 Frontend | HTML + CSS + JavaScript |
| 📦 Deployment | Pickle Model Serialization |
---
## 📂 Project Structure
```
📁 crop-recommendation
├── 📁 static/
│ └── images
├── 📁 templates/
│ └── index.html
├── 📄 app.py # Flask backend
├── 📄 crop\minmaxscaler.pkl,standscaler.pkl # Trained ML model
├── 📄 requirements.txt
├── 📄 Dockerfile
└── 📄 README.md
````
---
## 📊 Model Performance
| Model | Accuracy | Precision | Recall | F1-Score |
|------------------|----------|-----------|--------|----------|
| 🌟 Random Forest | 94.3% | 92.7% | 91.5% | 92.1% |
| Gradient Boosting | 92.1% | 90.5% | 89.3% | 89.9% |
| XGBoost | 91.4% | 88.9% | 87.5% | 88.2% |
| SVM | 85.7% | 83.2% | 81.6% | 82.4% |
📌 **Key Factors Influencing Prediction:**
- Rainfall
- Soil pH
- Temperature
- NPK (Nitrogen, Phosphorus, Potassium)
---
## 📥 How to Run Locally
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/parthmax2/crop-recommendation-system.git
cd crop-recommendation-system
````
### 2️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 3️⃣ Run the Flask App
```bash
python app.py
```
### 4️⃣ Open in Browser
Visit `http://localhost:5000/` to use the web app.
---
## 📈 Future Enhancements
* [ ] 🌤 Real-time Weather API Integration
* [ ] 📡 IoT-based Soil Sensor Integration
* [ ] 🐛 Pest & Disease Prediction Module
* [ ] 🌍 Satellite/GIS data for advanced insights
* [ ] 🌐 Multilingual and Offline Support
---
## ✍️ Author
**Saksham Pathak**
M.Sc. AI & ML, IIIT Lucknow
🔗 [GitHub](https://github.com/parthmax2) | [LinkedIn](https://linkedin.com/in/sakshampathak)
---
## 📄 License
This project is licensed under the **MIT License** – see the [LICENSE](LICENSE) file for details.
---
## 📚 References
* [IEEE Paper 1](https://ieeexplore.ieee.org/document/10575152)
* [Crop Dataset - Kaggle](https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset)
* Full list of academic references is included in the `paper.pdf`.
---
> 🌱 *"Empowering farmers through AI-driven decisions for a greener tomorrow."*
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