---
tags:
- computer-vision
- object-detection
- yolo
- yolov10
- agriculture
- plant-disease
- biology
- mobile-app
- pytorch
library_name: ultralytics
license: agpl-3.0
---

# 🧅 OnionScan AI : Automated Detection of Onion Diseases

![OnionScan Banner](https://img.shields.io/badge/Project-OnionScan%20AI-success?style=for-the-badge) ![Grade](https://img.shields.io/badge/Grade-19%2F20%20(Excellent)-gold?style=for-the-badge)

This model is the core Computer Vision engine behind **OnionScan**, an intelligent mobile application designed for the automated detection of onion diseases in Burkina Faso. It is based on the state-of-the-art **YOLOv10x** architecture, fine-tuned specifically for agricultural diagnosis.

## 🎓 Academic Context
This project was developed as a Bachelor’s thesis at the **Burkina Institute of Technology**.
*   **Thesis Title:** *“Development of an Intelligent Mobile Application for the Automated Detection of Onion Diseases Using Computer Vision: Case of Burkina Faso.”*
*   **Supervision:** Dr. Rachid Gaetan NABOLLE
*   **Grade:** 19/20 (4.0/4.0) – Excellent Distinction
*   **Impact:** Demonstrated the practical use of Artificial Intelligence for sustainable agriculture in Burkina Faso.

## 📱 The OnionScan Application
While this repository hosts the AI model weights, the full solution is a comprehensive mobile application designed to adapt to the realities of rural environments (usable by farmers with limited technical skills).

**Key Features of the App:**
*   **Multi-source Input:** Capture images directly via camera, import from the gallery, or use aerial images taken by drones (e.g., DJI Phantom) for large-scale fields.
*   **Offline / Online Mode:** Fully functional in rural areas with low internet connectivity.
*   **Actionable Insights:** Provides tailored treatment recommendations based on the AI diagnosis.
*   **Community & Tracking:** Features a scan history, a community forum for farmer interaction, local notifications, and a built-in help center.

## 🧠 Model Details
*   **Architecture:** YOLOv10-X (Extra-large for maximum precision)
*   **Task:** Object Detection (Bounding boxes)
*   **Classes (4 Onion Diseases):**
    1. `alternariose` (Alternaria porri)
    2. `fusariose` (Fusarium)
    3. `pourriture` (Rot)
    4. `virose` (Viral diseases)

## 📊 Training Metrics & Performance
The model was trained over 150 epochs (~47 minutes of training time) with carefully tuned hyperparameters (including Mosaic, Mixup, and Class Weights to handle dataset imbalance).

**Key Validation Metrics:**
*   **mAP50:** 58.98 %
*   **mAP50-95:** 36.88 %
*   **Precision:** 59.63 %
*   **Recall:** 54.99 %

*(Detailed graphs, F1 curves, and the confusion matrix are available in the `Files and versions` tab of this repository).*

## 💻 How to use the model (Inference)
You can test the model directly in Python using the `ultralytics` library:

```python
!pip install git+https://github.com/THU-MIG/yolov10.git
from huggingface_hub import hf_hub_download
from ultralytics import YOLOv10

# Download the best weights from Hugging Face
model_path = hf_hub_download(repo_id="Dama12/yolov10x-onion-disease-detection", filename="weights/best.pt")

# Load the YOLOv10x model
model = YOLOv10(model_path)

# Run inference on an image
results = model.predict(source="path/to/your/onion_leaf.jpg", conf=0.25)
results[0].show()
```

## ⚖️ License & Citation

### License
This model is built upon the YOLOv10 architecture. Therefore, the model weights are released under the **AGPL-3.0 License** (free for academic and research purposes). 
However, the dataset, methodology, and this specific implementation are intended strictly for **non-commercial research purposes**. If you intend to use this model for commercial applications, please contact the author.

### Citation
If you use this model or the OnionScan application concept in your research, please cite it as follows:

```bibtex
@misc{dama2025onionscan,
  author = {Soumana Dama},
  title = {Development of an Intelligent Mobile Application for the Automated Detection of Onion Diseases Using Computer Vision: Case of Burkina Faso},
  year = {2024},
  publisher = {Burkina Institute of Technology},
  note = {Bachelor's Thesis, supervised by Dr. Rachid Gaetan NABOLLE}
}
```

## 👨‍💻 Developer Information

- **Developed by:** [Soumana Dama](https://soumanadama.netlify.app/) — Full stack developer & AI engineer - Founder & Lead AI Engineer at [Scoinvestigator AI](https://www.scoinvestigator.com/)
- **LinkedIn:** [Soumana Dama](https://www.linkedin.com/in/soumana-dama-445096253/)
- **GitHub:** [Damasoumana1](https://github.com/Damasoumana1)

---
*Built to empower sustainable agriculture through Artificial Intelligence.* 🌾