Identification of Species of Medicinal Leaves

This project implements a Streamlit application for identifying medicinal leaf species using a pre-trained Convolutional Neural Network (CNN) model.

Overview

The application leverages the following libraries:

• Streamlit: User interface creation
• TensorFlow's Keras: Deep learning framework for CNN model
• PIL (Python Imaging Library): Image processing
• NumPy: Numerical computations
• Matplotlib: Visualization (optional)

Here's a breakdown of the key functionalities:

1. Imports and Setup:

The code imports the necessary libraries and sets up the Streamlit application structure with:

• A sidebar containing menu options (e.g., Home, About Us)
• Page configuration for title and layout

2. Prediction Function predict_leaf():

This function takes an image file as input and performs these pre-processing steps:

• Resizing the image
• Converting it to a NumPy array
• Normalizing pixel values
• Expanding dimensions to match the model's input format

It then uses the pre-trained CNN model to predict the class probabilities, returning:

• Predicted class label
• Confidence score
• Predicted index
• Class labels
• Entire prediction array

3. Uploading and Predicting Image:

The application provides a file uploader using st.file_uploader(). When an image (supported formats: jpg, jpeg, png) is uploaded, the predict_leaf() function is called to process it. The predicted results are then displayed on the Streamlit interface using st.write().

4. Pre-Trained Model Loading:

The code loads the pre-trained CNN model from the model.h5 file using keras.models.load_model().

Image Processing:

• Image Acquisition: The process of obtaining images.
• Image Enhancement: Modifying an image to make it more visually pleasing or improve its quality.
• Image Restoration: Techniques to restore degraded images.

Image Enhancement:

• Image Enhancement involves modifying an image to make it more visually pleasing or improve its quality.
• It's primarily for the benefit of human perception and interpretation, aiming to improve interpretation and perception for human observers.

Filtering:

• Filtering is the process of applying a filter to an image to modify or extract information from it. It's used to improve image quality, enhance features, remove noise, and prepare images for further processing.
• It's commonly applied as a pre-processing step before feature extraction, object detection, or image segmentation.

RGB to Grayscale Image:

• Grayscale images contain only one channel of intensity information per pixel, while RGB images have three channels (Red, Blue, Green).
• Converting to grayscale reduces data dimensionality, making it more efficient for processing and storage, especially in situations with limited computational resources.
• Simplifies analysis.

Characteristics of Grayscale Images:

• Mean Intensity: Represents the average pixel value in an image, indicating brightness or darkness. Adjusting overall brightness is important for tasks like image recognition.
• Standard Deviation: Measures variation in pixel values. Higher standard deviation can indicate regions with strong edges, useful for edge detection or image classification.