St. John's Wort Segmentation - Wortel AI annotation portfolio

Sample frames for this project are available under NDA. Write to bilal@wortel.ai and we will share the relevant before and after pairs.

Challenges

Differentiating St. John's Wort from visually similar weeds and surrounding vegetation.
Small plants and thin plant structures made precise segmentation difficult.
Dense field vegetation often left plant boundaries unclear.
Overlapping leaves and plants required careful polygon placement.
Partially visible plants had to have their visible boundaries estimated accurately.
Different growth stages produced wide variation in plant size and appearance.
Shadows and changing lighting conditions affected plant visibility.
Holding polygon boundaries precise while excluding surrounding vegetation took significant attention to detail.

Project overview

Agricultural field images were annotated for a computer vision dataset focused on segmenting St. John's Wort plants in real-world field environments. Precise polygon segmentation masks were created around visible plants to generate high-quality training data for an AI-based segmentation model.

The main objective was to prepare a reliable dataset that would allow an AI model to identify and segment St. John's Wort accurately while distinguishing it from surrounding crops, grass, weeds and other vegetation.

Dataset