README.md

---
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

[![Watch Demo](https://img.shields.io/badge/Click%20Here-Live%20App-green?style=for-the-badge&logo=github)](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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