Sunflower Plant Detection

Project overview

Agricultural field images containing sunflower plants were annotated for a computer vision dataset focused on sunflower plant detection. Visible plants were identified and enclosed in accurate bounding boxes to generate high-quality training data for an AI-based object detection model.

Work performed

• Reviewed and organised the agricultural image dataset before annotation.
• Identified visible sunflower plants in the field images.
• Drew accurate bounding boxes around individual sunflower plants.
• Applied the Sunflower Plant class consistently across the dataset.
• Annotated partially visible plants where applicable.
• Performed quality checks and corrected inaccurate or missed annotations.
• Ensured the bounding boxes were suitable for object detection model training.

What made it hard

• Detecting sunflower plants among dense vegetation and surrounding crops.
• Overlapping and partially visible plants complicated placement.
• Plants appeared at a wide range of distances and viewing angles.
• Keeping bounding boxes tight and consistent around individual plants required care.
• Lighting conditions varied and agricultural backgrounds were complex.

Result

A high-quality sunflower plant detection dataset was completed with accurate bounding box annotations. The dataset was prepared for training a computer vision model capable of automatically detecting sunflower plants in agricultural field images.

Final deliverable

Accurately annotated sunflower plant images with bounding boxes were delivered, with consistent labelling and annotation quality throughout the dataset. The final output was prepared to the project requirements and was ready for AI model training and agricultural computer vision applications.