Agriculture faces growing challenges, including climate change, resource optimization, and food security. The Farmwise is an academic project for the 4th year of Data Science Engineering at Esprit. This project leverages data science to provide actionable insights that help farmers and agricultural businesses enhance productivity and sustainability.
- 📈 Predict Crop Yields: Utilize machine learning models to forecast agricultural output.
- 💧 Optimize Resource Usage: Improve efficiency in water, fertilizer, and pesticide consumption.
- 🌍 Analyze Climate Impact: Study weather patterns and their effects on farming.
- 📉 Reduce Losses: Develop strategies to minimize production cycle wastage.
- Languages & Frameworks: Python, Machine Learning, Deep Learning, Web Development
- Key Areas: Data Analysis, Artificial Intelligence, Data Visualization, Open Source, Education
- Data Sources: Satellite imagery, weather APIs, government agricultural datasets
- Tools: Jupyter Notebook, Git, and more
| Role | Member | Contact |
|---|---|---|
| Solution Architect | Sana Khiari | [email protected] |
| Solution Architect | Mehdi Bchir | [email protected] |
| Data Scientist | Oumaima BenHaj | [email protected] |
| Project Manager | Mohamed Amine Brahmi | [email protected] |
| Project Lead | Ines Neji | [email protected] |
| Data Scientist | Sarra Bouden | [email protected] |
- 🎓 ESPRIT for academic guidance.
- 🔧 Open-source communities for tools like TensorFlow and Pandas.
- 🌱 Farmers & Agronomists who shared their domain expertise.