🌱 Smart Crop Advisory System

A Machine Learning based web application that helps farmers choose the most suitable crop based on soil and environmental conditions. The system predicts crops using parameters like nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall.

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🚀 Features

• 🌾 Crop recommendation based on soil nutrients and weather conditions
• 🤖 Machine Learning prediction model
• 📊 User-friendly interface for input and results
• 🌦 Uses environmental factors like rainfall, humidity, and temperature
• ⚡ Fast and accurate crop prediction
• 💻 Simple and responsive web interface

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🛠️ Technologies Used

Frontend

• HTML
• CSS
• JavaScript

Backend

• Python
• Flask

Machine Learning

• Scikit-learn
• Pandas
• NumPy

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📂 Project Structure

Smart-crop-advisory/
│── static/             # CSS, JS, images
│── templates/          # HTML templates
│── model/              # Trained ML model
│── app.py              # Main Flask application
│── requirements.txt    # Dependencies
│── dataset.csv         # Crop dataset
│── README.md

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⚙️ Installation

1️⃣ Clone the Repository

git clone https://github.com/divyansh678/Smart-crop-advisory.git

2️⃣ Navigate to Project Folder

cd Smart-crop-advisory

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the Application

python app.py

5️⃣ Open in Browser

http://127.0.0.1:5000

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📊 Input Parameters

The model predicts crops using:

• Nitrogen (N)
• Phosphorus (P)
• Potassium (K)
• Temperature
• Humidity
• pH Value
• Rainfall

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🤖 Machine Learning Model

The project uses a supervised Machine Learning algorithm trained on agricultural datasets to recommend the best crop for farming conditions.

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🎯 Future Improvements

• 🌐 Deploy on cloud platform
• 📱 Mobile responsive UI
• 🌍 Multi-language support
• 📈 Weather API integration
• 🧠 Advanced AI-based recommendations

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👨‍💻 Author

Developed by Divyansh

GitHub:
https://github.com/divyansh678

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⭐ Contributing

Contributions are welcome!
Feel free to fork the repository and submit pull requests.

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📜 License

This project is open-source and available under the MIT License.