Dive into the world of botany with LeafLens. Armed with the power of the Xception model, this project transforms your device into a leaf-identifying superhero. Simply upload an image, and let LeafLens unveil the mysteries of nature one leaf at a time! Whether you're a curious nature lover or a budding botanist, LeafLens is here to help you discover the leafy wonders around you. 🌿🔍

This project focuses on developing an automated system for identifying various species of leaves using the Xception model, a deep learning architecture known for its efficiency in image classification tasks. The model is trained on a diverse dataset of plant leaves to enhance accuracy and reliability.

With the increasing interest in plant biodiversity, the need for automated identification systems has become paramount. This project aims to leverage advanced machine learning techniques to accurately classify plant leaves based on image data.

• Objective: To create a robust system capable of identifying and classifying leaf species from images.
• Dataset: The dataset comprises images of 83 different leaf species, totaling over 7,000 images.
• Model Architecture: The Xception model is utilized due to its superior performance in handling image data through depthwise separable convolutions.

• The dataset consists of images sourced from various botanical collections.
• Each class corresponds to a specific leaf species, facilitating supervised learning.

The LeafLens project allows users to identify leaves by manually uploading an image. Follow these steps to get started:

1. Setup:

   • Ensure you have Python installed on your system.
   • Make sure the required libraries (streamlit, tensorflow, and numpy) are installed. You can install them using:

     pip install streamlit tensorflow numpy

2. Run the Program:

   • Navigate to the main folder of the project.
   • Open the command prompt (CMD) and execute:

     streamlit run main.py

3. Upload an Image:

   • Once the Streamlit app opens in your browser, you will see an option to upload an image.
   • Select the image of the leaf you want to identify and upload it.

4. View Prediction:

   • The model will process the uploaded image and display the predicted class along with confidence scores.
   • Additional information regarding the identified species will be shown on the screen.

5. Exit the Program:

   • Close the browser tab or stop the Streamlit server from the command line if needed.

• Ensure the image quality is good for better accuracy in predictions.
• The model's predictions depend on the training dataset; therefore, it may not recognize all leaf species accurately.

Here are some sample screenshots demonstrating the usage of LeafLens:

Screenshot of the image upload section:

Screenshot showing the prediction output:

Saving the model and its weights allows for efficient use of the trained model in future applications. By following the methods outlined in this section, you can ensure that your model is preserved and easily retrievable for later use, whether for inference or continued training.

By integrating this model into a user-friendly interface with manual image upload, LeafLens enhances its practical applications in biodiversity research and education.

Feel free to open issues or submit pull requests for any improvements. Contributions are welcome!

• Manan Punatu