Plant-Disease-Classification

A ResNet50-DCBAM Framework for Plant Disease Classification on the PlantVillage Dataset

---

Project Overview

This repository provides a reproducible implementation of a deep learning framework for plant disease classification.

The project investigates an architecture based on a pretrained ResNet50 backbone enhanced with DCBAM (Dual Channel & Spatial Attention Module) for image-based plant disease recognition.

The main objective is to improve discriminative feature learning by combining:

• ImageNet pretrained feature extraction
• ResNet50 hierarchical feature representation
• Channel and spatial attention refinement using DCBAM
• Flexible multi-level feature selection
• Efficient classification using global average pooling

The framework is designed for controlled experiments and reproducible research.

Overview of the proposed ResNet50-DCBAM framework for plant disease classification.

---

Key Features

• ResNet50 backbone with pretrained ImageNet weights
• DCBAM attention integration
  • Channel attention
  • Spatial attention
• Flexible feature-level selection (c2, c3, c4, c5)
• PlantVillage dataset support
• Official train/test split support
• Data augmentation during training
• Automatic checkpoint management
• Validation-based model selection:
  • Best Validation Loss
  • Best Validation Accuracy
• Classification report generation
• Normalized confusion matrix visualization
• Experiment directory organization

---

Project Structure

Plant-Disease-Classification/
│
├── data/
│   └── PlantVillage/
│       ├── raw/
│       └── splits/
│
├── docs/
│   ├── figures/
│   │   └── architecture_overview.png
│   │
│   └── project_structure.md
│
├── experiments/
│   ├── checkpoints/
│   ├── logs/
│   ├── plots/
│   └── results/
│
├── src/
│   ├── config/
│   │   └── config.py
│   │
│   ├── datasets/
│   │   └── plant_dataset.py
│   │
│   ├── models/
│   │   ├── attention/
│   │   │   └── dcbam.py
│   │   │
│   │   ├── backbone/
│   │   │   └── resnet_dcbam.py
│   │   │
│   │   ├── classifier.py
│   │   └── plant_model.py
│   │
│   ├── training/
│   │   ├── train.py
│   │   └── test.py
│   │
│   └── utils/
│       ├── plot_logs.py
│       └── export_requirements.py
│
├── README.md
├── requirements.txt
├── LICENSE
└── .gitignore

---

Dataset

The experiments use the public PlantVillage dataset.

Dataset access: PlantVillage Dataset

The dataset contains plant leaf images from multiple crop species and disease categories.

The project uses official split files:

data/
└── PlantVillage/
    ├── raw/
    │   └── color/
    │
    └── splits/
        ├── color_train.txt
        └── color_test.txt

Each split file contains relative image paths:

raw/color/Tomato___Late_blight/image_name.JPG

---

Installation

Clone the repository:

git clone https://github.com/AhmadJeddi/Plant-Disease-Classification.git
cd Plant-Disease-Classification

Install dependencies:

pip install -r requirements.txt

Recommended environment:

• Python >= 3.10
• PyTorch >= 2.5

---

Configuration

All experiments are controlled through:

src/config/config.py

Main configurable parameters:

• Dataset paths
• Image size
• Batch size
• Learning rate
• Training epochs
• Weight decay
• Early stopping patience
• Feature extraction level
• Pretrained backbone option
• Experiment directories

Example:

IMAGE_SIZE = 224

FEATURE_LEVEL = "c5"

PRETRAINED = True

BATCH_SIZE = 32

---

Model Architecture

The proposed model consists of three main components:

Input Image
     |
     v
ResNet50 Backbone
     |
     +--> DCBAM Attention Refinement
     |
     v
Feature Selection (c2/c3/c4/c5)
     |
     v
Global Average Pooling
     |
     v
Linear Classifier
     |
     v
Disease Class Prediction

ResNet50 Backbone

The backbone extracts hierarchical visual representations:

• c2
• c3
• c4
• c5

The selected feature map is passed to the classifier.

DCBAM Attention

DCBAM improves feature representation through:

• Channel attention:
  • Adaptive average pooling
  • Adaptive max pooling
  • Shared MLP transformation
• Spatial attention:
  • Channel-wise average pooling
  • Channel-wise max pooling
  • Convolution-based spatial refinement

---

Training

Run training:

python src/training/train.py

Training includes:

• Cross Entropy Loss
• Adam optimizer
• Data augmentation
• Validation monitoring
• Early stopping
• Automatic checkpoint saving
• Training curve visualization

Generated checkpoints:

experiments/
└── checkpoints/
    ├── best_loss.pth
    └── best_acc.pth

---

Evaluation

Run evaluation:

python src/training/test.py

Evaluation provides:

• Classification report
• Precision
• Recall
• F1-score
• Normalized confusion matrix
• Saved experiment results

Results are stored in:

experiments/
└── results/

Visualizations:

experiments/
└── plots/

---

Experiments

Two checkpoints are evaluated:

Best Validation Loss

Selected based on minimum validation loss.

Best Validation Accuracy

Selected based on maximum validation accuracy.

Both checkpoints are evaluated using the identical test protocol.

---

Results

The final model was evaluated on the PlantVillage test split containing 10,709 images across 38 plant disease classes.

Metric	Score
Accuracy	99.79%
Macro F1-score	99.69%
Weighted F1-score	99.79%

Both best-loss and best-accuracy checkpoints achieved identical test performance.

---

Reproducibility

The project provides:

• Fixed random seed
• Centralized configuration
• Deterministic experiment structure
• Saved checkpoints
• Automated result logging

---

Acknowledgements

This project builds upon the ResNet50-DCBAM implementation from:

ResNet-DCBAM

The original implementation was integrated into this framework with improved documentation, project-specific organization, and adaptation for the Plant Disease Classification task.

---

License

This project is licensed under the MIT License.

© Ahmad J. Zahed — Please keep the copyright notice and attribution when using or modifying this code. For full details, see the LICENSE file.