metadata

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

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.

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)
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

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

# 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

The model is trained on UAV imagery at 640x640 resolution
Optimized for detecting single class: "unhealthy" trees
Performance may vary with different tree species or image conditions
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:

@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 by Zhou et al.
Framework: Ultralytics YOLOv8
Training performed on Google Colab with NVIDIA A100 GPU

Contact

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