AgroSmart

AI-powered Smart Agriculture Platform

Crop recommendation, disease detection, weather insights, and market intelligence in one beautiful app.

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🚀 Features

• AI Crop Recommendation: Get personalized crop suggestions based on soil and climate data.
• Plant Disease Detection: Upload leaf images and detect diseases using deep learning.
• Weather Dashboard: Real-time weather and 7-day forecasts for major Indian cities.
• Market Prices: View current crop prices and trends from top markets.
• Beautiful UI: Modern, responsive design for desktop and mobile.

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🗂️ Project Structure

Sunflower Project/
├── backend/           # Flask backend (API endpoints)
├── frontend/          # HTML, CSS, JS (UI)
├── data_ml/           # ML models, notebooks, datasets
│   ├── models/        # Trained model files (.pth, .pkl, .json)
│   └── notebooks/     # Jupyter notebooks for training
├── crop_information.csv
├── crop_recommendation_dataset.csv
├── market_prices.csv
├── weather_forecast.csv
├── requirements.txt   # Python dependencies
└── README.md          # Project documentation

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⚡ Quick Start

1. Clone the repo:

   git clone https://github.com/Tejeswar001/Sunflower-Project.git
   cd Sunflower-Project

2. Install Python dependencies:

   pip install -r requirements.txt

3. Run the Flask backend:

   cd backend
   python app.py

4. Open index.html in your browser (or use a local server for full functionality).

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🧠 Machine Learning

• Crop Recommendation: Trained with scikit-learn on soil and climate features.
• Disease Detection: MobileNetV3 deep learning model, trained on Kaggle's New Plant Diseases Dataset.
• Class Labels: See data_ml/models/class_labels.json for all supported diseases.

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🌐 APIs Used

• Open-Meteo API (open-meteo.com) — Free weather forecast API for real-time and 7-day weather data.
• Nominatim Geocoding API (nominatim.org) — Free geocoding service to convert city names to latitude/longitude.
• Agmarknet (Govt. of India) (agmarknet.gov.in) — Official crop price data (used for sample/extension, not direct API).

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🌦️ Weather & Market Data

• Weather: Powered by Open-Meteo API and Nominatim geocoding.
• Market Prices: Sample data included; can be extended with real-time sources.

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📁 Notebooks & Datasets

• Jupyter notebooks for model training and evaluation in data_ml/notebooks/
• Datasets and model files in data_ml/models/

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🖥️ Frontend

• Responsive UI with modern design
• Crop advisor, weather dashboard, disease detection, and market analytics
• Easy navigation and mobile support

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🛠️ Tech Stack

• Python, Flask
• scikit-learn, PyTorch, torchvision
• HTML, CSS, JavaScript
• Chart.js for analytics

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📚 Documentation

• All endpoints and features are documented in code comments and this README.
• For ML details, see the Jupyter notebooks in data_ml/notebooks/.

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🤝 Contributing

Pull requests and suggestions are welcome! Please open an issue for major changes.

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📄 License

This project is licensed under the MIT License