LeafSeek is a powerful Deep Learning-based solution for the accurate identification and classification of medicinal plant leaves. Using cutting-edge technologies and a robust tech stack, including Python, Tensorflow, and Keras, LeafSeek achieves an impressive 99% accuracy on real-time data. The system has been thoroughly validated and integrated into a functional Flask web application for easy access and usability.
- High Accuracy: LeafSeek utilizes advanced Deep Learning algorithms, trained on extensive datasets, to achieve an accuracy rate of 99% in identifying and classifying medicinal plant leaves.
- Real-time Processing: With its efficient architecture, LeafSeek can process leaf images in real-time, providing instant results to users.
- User-Friendly Web Application: The functionality of LeafSeek has been encapsulated in a user-friendly Flask web application, allowing users to easily upload leaf images and obtain quick and accurate identifications.
- Extensible and Customizable: LeafSeek's architecture is designed to be extensible and customizable, enabling users to integrate it into their own applications or research projects.
- Data Collection: LeafSeek's training data comprises a diverse and extensive collection of labeled images of medicinal plant leaves, ensuring robust model training.
- Model Training: The powerful combination of Python, Tensorflow, and Keras enables the creation of a highly accurate Deep Learning model for leaf identification and classification.
- Validation: The model is rigorously tested and validated on real-world datasets to ensure its reliability and accuracy.
- Flask Web Application: The validated model is integrated into a Flask web application, providing an intuitive interface for users to interact with LeafSeek.
Follow these steps to get LeafSeek up and running:
- Clone the LeafSeek repository to your local machine.
- Install the necessary dependencies by running pip install -r requirements.txt.
- Launch the Flask web application with python app.py.
- Access the web application in your browser at http://localhost:5000.
- Upload an image of a medicinal plant leaf and get instant identification results!
