Researcher | Deep Learning Engineer | Computer Vision for Agriculture
Advanced Channel-Enhanced Multi-Scale Pest Detection Network
Published in Expert Systems with Applications (2025)
| Dataset | mAP / Acc | Improvement | FPS |
|---|---|---|---|
| Jute17 | 85.6% mAP | +9.2% | 350+ |
| Pest24 | 78.22% mAP | +8.02% | 370+ |
| IP102 | 78.15% Acc | -- | -- |
I am a researcher focused on developing efficient deep learning solutions for real-world agricultural challenges. My work emphasizes crop pest detection in complex field environments, addressing small objects, camouflage, and multi-scale variability.
Research Interests:
- Object Detection & Computer Vision
- Efficient Neural Architectures (YOLO-based)
- Agricultural AI & Precision Farming
- Multi-scale Feature Extraction & Channel Attention
"Advanced deep learning model for crop-specific and cross-crop pest identification"
- CE-GELAN (Channel-Enhanced Generalized Efficient Layer Aggregation Network)
- GMSFE-ELAN (Generalized Multi-Scale Feature Extraction with 1×1, 3×3, 5×5, 7×7 kernels)
- Custom Re-parameterization for real-time inference (350+ FPS)
- New Jute17 Dataset (12,916 images, 17 pest classes)
- Jute17: 85.6% mAP (9.2% better than baseline)
- Pest24: 78.22% mAP (excellent on tiny pests)
- IP102: 78.15% Accuracy (outperforms multiple SOTA models)
- Strong generalization across crop-specific and cross-crop scenarios
- Framework: PyTorch 2.1+
- Architecture: Enhanced YOLO-style with custom modules
- Tools: OpenCV, Albumentations, Grad-CAM, etc.
CMPestNet/
├── README.md # ← You are here
├── LICENSE
├── requirements.txt
├── setup.py
├── assets/ # Visuals & Results
│ ├── teaser.jpg
│ ├── architecture.png
│ ├── gradcam_comparison.png
│ └── performance_charts/
├── models/
│ ├── cmpestnet.py
│ ├── ce_gelan.py
│ ├── gmsfe_elan.py
│ └── reparam.py
├── utils/
│ ├── augmentations.py
│ ├── loss.py
│ └── metrics.py
├── scripts/
│ ├── train.py
│ ├── infer.py
│ └── export.py
├── notebooks/
│ ├── training_demo.ipynb
│ └── inference_demo.ipynb
├── config/
│ └── hyperparameters.yaml
├── data/ # Download scripts
└── weights/ # Pre-trained models (Git LFS)
## 🚀 About Me
# Clone the repo
git clone https://github.com/yourusername/CMPestNet.git
cd CMPestNet
# Install dependencies
pip install -r requirements.txt
# Inference example
python scripts/infer.py --weights weights/cmpestnet_jute17.pt --source test_images/ --save-txt
# Clone the repo
git clone https://github.com/yourusername/CMPestNet.git
cd CMPestNet
# Install dependencies
pip install -r requirements.txt
# Inference example
python scripts/infer.py --weights weights/cmpestnet_jute17.pt --source test_images/ --save-txt
📄 Citation
@article{suzauddola2025cmpestnet,
title = {Advanced deep learning model for crop-specific and cross-crop pest identification},
author = {Md Suzauddola and Defu Zhang and Adnan Zeb and Junde Chen and others},
journal = {Expert Systems with Applications},
year = {2025},
doi = {10.1016/j.eswa.2025.126896}
}
🌐 Connect With Me
GitHub: @suzauddola
Google Scholar / ResearchGate / LinkedIn: https://sites.google.com/view/mdsuzauddola/home