Plant Leaf Disease Classification

This project aims to classify diseases in plant leaves using deep learning and computer vision techniques. It includes four main parts:

1. Data Analysis: Analyze and visualize the dataset to understand class distributions. The dataset consists of images of plant leaves with various diseases, organized into subdirectories by plant type and disease category.

1. Data Augmentation: Balance dataset classes and improve model generalization using various augmentation techniques.

3. Image Transformation: Apply image transformation methods for feature extraction and model learning.

6. Classification: Develop and train convolutional neural network (CNN) models to accurately classify diseases in plant leaves.

Usage

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Data analysis

To analyze the dataset and generate pie charts and bar charts for each plant type.

python3 Distribution.py /path/to/dataset

Data Augmentation

To create augmented images using flip, rotate, crop, distortion , brightness and saturation techniques.

python3 Augmentation.py /path/to/image

Image Transformation

To apply image transformation methods such as crop, skew, and rotation for feature extraction:

python3 Transformation.py /path/to/image

Classification

To train CNN model classify diseases in plant leaves.

python3 train.py /path/to/dataset

To predict which disease the image represents :

python3 predict.py /path/to/image

Requirements

• Python 3.x
• TensorFlow
• Keras
• NumPy
• OpenCV
• Matplotlib

Contributors