Grape Segmentation

Project overview

Grape images were annotated for a computer vision dataset focused on detecting and segmenting grapes in agricultural environments. Precise polygon annotations were created around visible grapes and grape clusters to generate high-quality training data for an AI-based segmentation model.

Work performed

• Reviewed and organised the grape image dataset before annotation.
• Visible grapes and grape clusters were identified in each image.
• Precise polygon annotations were created along the visible boundaries of the grapes.
• Labelling remained consistent throughout using the Grape class.
• Partially visible grapes were annotated where applicable.
• Quality checks were performed, and inaccurate or incomplete polygons were corrected.
• Annotations were confirmed suitable for computer vision segmentation model training.

What made it hard

• Annotating small grapes with precise boundaries.
• Tightly packed and overlapping grapes within clusters had to be separated.
• Partially visible grapes hidden behind leaves or other grapes needed careful judgement.
• Accurate polygon boundaries around rounded objects were difficult to maintain.
• Varying lighting conditions, shadows and complex vineyard backgrounds reduced clarity.

Result

A high-quality grape segmentation dataset was completed with precise polygon annotations. The dataset was prepared for training a computer vision model capable of automatically detecting and segmenting grapes in agricultural images.

Final deliverable

Accurate polygon annotations for grapes were delivered with consistent labelling and annotation quality maintained throughout the dataset. The final output was prepared according to project requirements and was ready for AI model training and agricultural computer vision applications.