https://github.com/eniddealla/leaffliction
Deep Learning - Disease recognition on leaves Using CNNs
https://github.com/eniddealla/leaffliction
cnn computer-vision data-science deep-learning machine-learning python resnet-18
Last synced: 3 months ago
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Deep Learning - Disease recognition on leaves Using CNNs
- Host: GitHub
- URL: https://github.com/eniddealla/leaffliction
- Owner: EniddeallA
- Created: 2024-09-04T15:27:53.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-11-12T10:14:09.000Z (7 months ago)
- Last Synced: 2025-01-14T05:28:14.635Z (5 months ago)
- Topics: cnn, computer-vision, data-science, deep-learning, machine-learning, python, resnet-18
- Language: Python
- Homepage:
- Size: 57.8 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Leaffliction
#### The project is an introduction to Deep Learning/Computer Vision
This project implements a deep learning solution using ResNet18 to classify plant diseases from leaf images, with comprehensive data preprocessing and augmentation pipelines.
## Project Structure
```
./
├── Distribution.py # Dataset distribution analysis
├── Augmentation.py # Image augmentation implementation
├── Transformation.py # PlantCV-based image transformations
├── PlantDiseaseDataset.py # Custom PyTorch dataset
├── train.py # Model training script
├── predict.py # Disease prediction script
└── Leaves.zip # Leave image dataset information
```
## Leaves Directory Structure
```
dataset/
├── Plant_Type1/
│ ├── Disease1/
│ │ ├── original_images/
│ │ └── Augmented/
│ │ └── Transformed/
│ └── Disease2/
│ ├── original_images/
│ └── Augmented/
│ │ └── Transformed/
└── Plant_Type2/
└── ...
```
## Components
### 1. Data Analysis
Our `Distribution.py` script analyzes and visualizes the dataset structure:
```bash
python Distribution.py ./path/to/dataset
```
Features:
- Pie chart visualization of disease distribution
- Bar chart showing image count per disease
- Handles both original and augmented images
- Excludes augmented directories from double-counting
### 2. Data Augmentation
The `Augmentation.py` script implements various augmentation techniques using torchvision:
```bash
python Augmentation.py ./path/to/image.jpg
# or
python Augmentation.py ./path/to/directory
```
Implemented transformations:
- Gaussian Blur (kernel_size=(3, 7), sigma=(1.5, 6))
- Random Rotation (-80° to 80°)
- Color Jitter (brightness, contrast, saturation)
- Random Resized Crop (200x200)
- Random Affine Shear (45°)
- Random Horizontal/Vertical Flip
### 3. Image Transformation
`Transformation.py` uses PlantCV for advanced image processing:
```bash
python Transformation.py ./path/to/image.jpg
# or
python Transformation.py -src source_dir -dst destination_dir
```
Pipeline includes:
- LAB colorspace conversion
- Binary thresholding
- Gaussian blur
- Mask application
- ROI extraction
- Shape analysis
- X/Y axis pseudolandmarks
### 4. Custom Dataset
`PlantDiseaseDataset.py` implements a PyTorch Dataset class with:
- Automatic data balancing
- Support for multiple image formats
- Caching mechanism for faster loading
- Minimum image count validation (500 images)
- Even distribution across classes
### 5. Model Architecture and Training
We use a modified ResNet18 architecture with transfer learning:
```python
model = models.resnet18(weights=ResNet18_Weights.DEFAULT)
num_features = model.fc.in_features
model.fc = torch.nn.Linear(num_features, num_classes)
```
Training pipeline features:
- 80/20 train-validation split
- Batch size of 32
- Adam optimizer with learning rate 0.0001 and weight decay 1e-5
- CrossEntropyLoss criterion
- Training progress monitoring with loss and accuracy metrics
- Validation accuracy evaluation after each epoch
Image preprocessing pipeline:
```python
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.GaussianBlur(kernel_size=(3, 3), sigma=(0.1, 2.0)),
transforms.RandomGrayscale(0.1),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
```
To train the model:
```bash
python train.py --data_train /path/to/dataset --total_images 1000
```
The trained model is saved as 'plant_disease_model.pth' with both the model state dictionary and class labels.
### 6. Prediction
The `predict.py` script provides disease classification with visualization:
```bash
python predict.py path/to/image1.jpg path/to/image2.jpg ...
```
Features:
- Side-by-side visualization of original and transformed images
- Clear prediction display
- Batch prediction support
- CPU/GPU compatibility