Weed Detection using YOLOv8

This project leverages YOLOv8, a cutting-edge object detection model, to identify weeds in images. The workflow includes:

1. Data Acquisition: Using the "Weed Detection ISA" dataset from Roboflow, which provides labeled images of various weeds.
2. Model Training: Utilizing Ultralytics YOLOv8 to train the model on the acquired dataset.
3. Predictions: Making predictions on new images to detect weeds with the trained model.

Technologies Used

• YOLOv8: For real-time object detection.
• Roboflow: For dataset management and preprocessing.
• Python and Jupyter Notebook: For coding and execution.

Steps

1. Install Required Packages:

   %pip install ultralytics roboflow

2. Download Dataset:

   from roboflow import Roboflow
   
   rf = Roboflow(api_key="YOUR_API_KEY")
   project = rf.workspace("object-detection-dt-wzpc6").project("weed-detection-isa")
   dataset = project.version(1).download("yolov8")

3. Train the Model:

   !yolo train model=yolov8n.pt data=/path/to/your/data.yaml epochs=100 imgsz=640

4. Fine-tune the Model:

   !yolo train model=/path/to/your/previous/weights/best.pt data=/path/to/your/data.yaml epochs=100 imgsz=640

5. Make Predictions:

   !yolo predict model=/path/to/your/final/weights/best.pt source=/path/to/your/image.jpg conf=0.5

Conclusion

The project successfully demonstrates the application of YOLOv8 for weed detection, providing a comprehensive workflow from dataset acquisition to making predictions on new images.