Architecture:
Following a client-server model, the backend, developed in Python with Flask/Django, connects to a MySQL database. Machine learning models for soil classification and crop prediction are seamlessly integrated. The frontend, developed with HTML, CSS, and JavaScript, offers an intuitive interface accessible across devices.
Technologies Used:
- Python for backend development
- Flask/Django web framework for server-side logic
- HTML/CSS/JavaScript for frontend development
- MySQL for database management
- Machine learning libraries (e.g., scikit-learn, TensorFlow) for algorithm implementation
Web User Interface:
The SEO-friendly web interface allows farmers to input soil data, view classification results, and receive crop recommendations effortlessly. The interface includes engaging visualizations of soil characteristics and historical crop performance, enhancing user understanding and adoption.
In summary, the “Optimizing Agriculture” project introduces a transformative approach to agriculture, providing farmers with precise soil insights and optimal crop recommendations through an accessible web interface. This innovative system aims to enhance agricultural productivity and sustainability.
