Leaffliction

Overview

Leaffliction is a computer vision project focused on image classification for disease recognition in leaves. It involves analyzing a dataset of leaf images, augmenting the dataset to improve balance, applying image transformations, and developing a classification model to identify leaf diseases.

Features

• Data Analysis: Extracts and visualizes information about the dataset using pie and bar charts.
• Data Augmentation: Enhances dataset balance by applying transformations like flipping, rotating, skewing, and cropping.
• Image Transformation: Processes images with techniques such as Gaussian blur, masking, and object analysis.
• Classification Model: Trains a model to recognize leaf diseases and predicts diseases from new images.

Installation

1. Ensure you have Python installed.
2. Install required dependencies:

   pip install -r requirements.txt

3. Clone the repository and navigate to the project folder.

   git clone <repo_url>
   cd leaffliction

Usage

Data Analysis

Run the dataset analysis script to visualize plant and disease distribution:

python Distribution.py ./path_to_dataset

Data Augmentation

Generate augmented images for dataset balancing:

python Augmentation.py ./path_to_image

Image Transformation

Apply transformations to images for feature extraction:

python Transformation.py -src ./source_dir -dst ./destination_dir

Model Training

Train a disease classification model:

python train.py ./path_to_dataset

Prediction

Predict the disease of a leaf image:

python predict.py ./path_to_image

Evaluation

• The dataset should be divided into training and validation sets.
• Validation accuracy should be at least 90%.
• A .zip file containing the trained model and dataset signature is required.
• Use sha1sum to verify the dataset signature:

  sha1sum dataset.zip