Smart Agriculture: Weather Prediction and Disease Identification

Welcome to the Smart Agriculture and Weather Prediction and Disease Identification project!
This project leverages advanced technologies such as Machine Learning, IoT, and Computer Vision to empower farmers and agricultural researchers with intelligent tools for weather prediction, crop disease identification, and improved decision making.

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🌱 Features

• Weather Prediction
  Accurate, real-time weather forecasting using machine learning models to help with sowing, irrigation, and harvesting.

• Crop Disease Identification
  AI-based image recognition to detect plant diseases from leaf images, enabling early intervention and crop protection.

• Data Collection & Visualization
  Sensor and satellite data integration. Intuitive dashboards for monitoring and analysis.

• Automated Alerts
  Receive notifications and recommendations for disease outbreaks, weather anomalies, and smart farming tips.

• Multi-language Codebase
  Core logic in Python, with components in C++, C, Cython, and Jupyter Notebooks for data science workflows.

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📂 Project Structure

smart-Agriculture-and-weather-predicion-and-disease-identification/
│
├── data/                # Datasets used for training and evaluation
├── models/              # Trained ML/DL models
├── src/                 # Source code (Python, C++, Cython)
│   ├── weather/         # Weather prediction modules
│   └── disease/         # Disease identification modules
├── notebooks/           # Jupyter Notebooks for EDA and experiments
├── utils/               # Helper scripts and utilities
├── requirements.txt     # Python dependencies
├── README.md            # This file
└── ...                  # Other files

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🏗️ Architecture Overview

High-Level Flow:

1. Data Collection:
   IoT sensors, satellite APIs, and user uploads gather weather and crop data.

2. Data Preprocessing:
   Cleans and transforms data for machine learning models.

3. Prediction Modules:

   • Weather Prediction: Time-series models forecast local weather.
   • Disease Identification: Computer vision models classify leaf images.

4. Visualization & Alerting:
   Dashboards and automated notifications for actionable insights.

5. User Interface:
   Web/Mobile app for farmers and researchers to access results.

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🚀 Getting Started

1. Clone the Repository

git clone https://github.com/BorudePiyush/smart-Agriculture-and-weather-predicion-and-disease-identification.git
cd smart-Agriculture-and-weather-predicion-and-disease-identification

2. Install Dependencies

pip install -r requirements.txt

If you wish to build C++/Cython components, follow instructions in the respective src/ subfolders.

3. Prepare Data

• Download or collect agricultural, weather, and plant disease datasets.
• Place datasets in the data/ directory as described in the documentation.

4. Train or Use Models

• Use Jupyter Notebooks in notebooks/ for experiments.
• Run scripts in src/weather/ or src/disease/ for prediction and identification.

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📊 Example Usage

Weather Prediction

from src.weather.predictor import WeatherPredictor

predictor = WeatherPredictor(model_path="models/weather_model.pkl")
forecast = predictor.predict(location="Pune", date="2025-09-22")
print(forecast)

Disease Identification

from src.disease.classifier import DiseaseClassifier

classifier = DiseaseClassifier(model_path="models/plant_disease_model.h5")
result = classifier.classify_leaf_image("samples/leaf.jpg")
print(result)

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📖 Documentation

• ARCHITECTURE.md: Full system architecture
• Weather Prediction: Approach & Models
• Disease Identification: Pipeline & Dataset
• Data Formats & Sources
• API Usage (if applicable)

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🤖 Technologies Used

• Python: Core ML and data processing
• C++/C: Performance-critical modules
• Cython: Python/C interoperability and speed
• Jupyter Notebook: Data analysis and visualization
• Machine Learning: scikit-learn, TensorFlow, Keras, OpenCV
• IoT/Sensors: (If applicable, e.g. Arduino, Raspberry Pi integration)

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💡 Contributing

Contributions are welcome!

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Open a Pull Request.

See CONTRIBUTING.md for more details.

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📝 License

This project is licensed under the MIT License.

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🙏 Acknowledgements

• Open-source datasets and tools
• Community contributors
• [Your University/Organization, if any]

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📬 Contact

• Author: Piyush Borude
• Email: [your.email@example.com]
• GitHub Issues: For bug reports and feature requests

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Empowering agriculture with intelligence, one prediction at a time.