Data Description
- Total Images: 2,822
- Annotations: YOLO v5 PyTorch format
- Pre-processing:
- Auto-orientation of pixel data (EXIF-orientation stripping)
- Augmentation:
- 90-degree rotations (none, clockwise, counter-clockwise) with equal probability
- Random shear: -15° to +15° horizontally and vertically
- Random brightness adjustment: -25% to +25%
Classes
Augmentation Details
To enhance the dataset and improve model robustness, each source image undergoes the following augmentations to create three versions:
- Rotation: Images are rotated by 0°, 90°, or 270°.
- Shear: Random horizontal and vertical shear between -15° and +15°.
- Brightness: Random adjustment between -25% and +25%.
Inspiration
Accurate identification and differentiation of weeds from crops are crucial for effective farming practices, enabling better crop management and yield optimization.
Applications:
- Precision Agriculture: Helps in the development of automated systems for weed detection and removal, reducing the reliance on manual labor and herbicides.
- Research and Development: Facilitates research in machine learning and computer vision for agricultural applications, fostering innovation in the sector.
- Educational Purposes: Serves as a valuable resource for academic projects and training machine learning models in agricultural studies.