---
license: mit
language:
- en
tags:
- object-detection
- yolov8
- tree-disease-detection
- agriculture
- computer-vision
- pytorch
- ultralytics
library_name: ultralytics
pipeline_tag: object-detection
datasets:
- qwer0213/PDT_dataset
metrics:
- mAP50
- mAP50-95
- precision
- recall
model-index:
- name: crop_desease_detection
  results:
  - task:
      type: object-detection
      name: Object Detection
    dataset:
      name: PDT Dataset
      type: qwer0213/PDT_dataset
    metrics:
    - type: map
      value: 0.933
      name: mAP50
    - type: map
      value: 0.659
      name: mAP50-95
    - type: precision
      value: 0.878
      name: Precision
    - type: recall
      value: 0.863
      name: Recall
inference: true
spaces:
- IsmatS/tree-disease-detector-demo
widget:
- src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/example_image.jpg
  example_title: Example Tree Image
---

# YOLOv8s Tree Disease Detection Model

Try the model in action: [🚀 Live Demo](https://huggingface.co/spaces/IsmatS/tree-disease-detector-demo)

This model detects unhealthy/diseased trees in aerial UAV imagery using YOLOv8s architecture. It was trained on the PDT (Pests and Diseases Tree) dataset and achieves high accuracy for agricultural monitoring applications.

<!-- Embed the Space -->
<iframe
  src="https://IsmatS-tree-disease-detector-demo.hf.space"
  frameborder="0"
  width="850"
  height="450"
></iframe>

## Model Description

This YOLOv8s model has been fine-tuned specifically for detecting unhealthy trees affected by pests and diseases in high-resolution UAV imagery. The model is particularly effective for:

- Precision agriculture monitoring
- Forest health assessment  
- Early disease detection in orchards
- Large-scale plantation management
- Environmental monitoring

### Architecture
- **Base Model**: YOLOv8s
- **Input Size**: 640x640 pixels
- **Framework**: Ultralytics YOLOv8
- **Classes**: 1 (unhealthy)

## Training Details

### Dataset
- **Dataset**: [PDT (Pests and Diseases Tree)](https://huggingface.co/datasets/qwer0213/PDT_dataset)
- **Training Images**: 4,536
- **Validation Images**: 567
- **Test Images**: 567
- **Resolution**: 640x640 (Low Resolution version)

### Training Configuration
- **Epochs**: 50
- **Batch Size**: 16
- **Optimizer**: SGD
- **Learning Rate**: 0.01
- **Momentum**: 0.9  
- **Weight Decay**: 0.001
- **Device**: NVIDIA A100-SXM4-40GB
- **Training Time**: 0.408 hours

## Performance Metrics

| Metric | Value |
|--------|-------|
| mAP50 | 0.933 |
| mAP50-95 | 0.659 |
| Precision | 0.878 |
| Recall | 0.863 |

## Usage

### Installation

```bash
pip install ultralytics

### Inference

```python
from ultralytics import YOLO
import cv2

# Load model
model = YOLO('best.pt')  # or path to downloaded model

# Run inference on an image
results = model('path/to/your/image.jpg')

# Process results
for result in results:
    boxes = result.boxes
    if boxes is not None:
        for box in boxes:
            confidence = box.conf[0]
            coordinates = box.xyxy[0]
            print(f"Unhealthy tree detected with {confidence:.2f} confidence")
            
# Visualize results
annotated_image = results[0].plot()
cv2.imwrite('detection_result.jpg', annotated_image)
```

### Advanced Usage

```python
# Custom inference settings
results = model.predict(
    source='path/to/image.jpg',
    conf=0.25,  # confidence threshold
    iou=0.45,   # IoU threshold for NMS
    imgsz=640,  # inference size
    save=True   # save results
)

# Batch processing
import glob
image_paths = glob.glob('path/to/images/*.jpg')
results = model(image_paths, batch=8)
```

## Model Files

- `best.pt`: Best model weights from training
- `tree_disease_detector.pt`: Final saved model
- `training_results.png`: Training curves and metrics

## Limitations and Considerations

1. The model is trained on UAV imagery at 640x640 resolution
2. Optimized for detecting single class: "unhealthy" trees
3. Performance may vary with different tree species or image conditions
4. Best results with aerial/drone imagery similar to training data

## Applications

- **Precision Agriculture**: Early detection of diseased trees in orchards
- **Forest Management**: Large-scale monitoring of forest health
- **Environmental Monitoring**: Tracking disease spread patterns
- **Research**: Studying tree disease progression and patterns

## Citation

If you use this model in your research, please cite:

```bibtex
@model{yolov8_tree_disease_2024,
  title={YOLOv8s Tree Disease Detection Model},
  author={IsmatS},
  year={2024},
  publisher={HuggingFace},
  url={https://huggingface.co/IsmatS/crop_desease_detection}
}

@dataset{pdt_dataset,
  title={PDT: UAV Pests and Diseases Tree Dataset},
  author={Zhou et al.},
  year={2024},
  publisher={HuggingFace},
  conference={ECCV 2024}
}
```

## License

This model is released under the MIT License.

## Acknowledgments

- Dataset: [PDT Dataset](https://huggingface.co/datasets/qwer0213/PDT_dataset) by Zhou et al.
- Framework: [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics)
- Training performed on Google Colab with NVIDIA A100 GPU

## Contact

For questions or collaborations, please reach out through the HuggingFace repository discussions.