Waldersey Black Grass Segmentation

Project overview

The Waldersey Black Grass Segmentation project involved annotating agricultural field images to identify and segment black grass plants accurately. The dataset was prepared for computer vision model training focused on precise weed segmentation and agricultural field monitoring.

Work performed

• Reviewed the agricultural field images and identified black grass plants.
• Created precise polygon segmentation masks around individual black grass plants.
• Visible boundaries of leaves and plant structures were followed closely.
• Differentiated black grass from crops, other weeds, soil and surrounding vegetation.
• Overlapping and partially visible plants were handled case by case.
• Segmentation quality was kept consistent across the dataset.
• Reviewed and corrected inaccurate or incomplete masks.
• Prepared the dataset for weed segmentation model training.

What made it hard

• Distinguishing black grass from visually similar grasses and vegetation.
• Creating precise boundaries within dense agricultural fields.
• Overlapping leaves and plants complicated individual masks.
• Small and partially visible plants still had to be segmented accurately.
• Complex backgrounds and varying lighting conditions added difficulty.
• Maintaining consistent polygon boundaries throughout the dataset.

Result

A high-quality annotated dataset was prepared for black grass segmentation, suitable for training computer vision models for agricultural weed identification, segmentation and monitoring.

Final deliverable

The final dataset contained accurate and consistent polygon segmentation masks for the black_grass class, prepared for computer vision model training and agricultural weed monitoring applications.