Tassels boxed as male or female by position relative to the crop row, quantifying female tassels left after detasseling.
Sample frames for this project are available under NDA. Write to bilal@wortel.ai and we will share the relevant before and after pairs.
The Detasseling project involved annotating tassels in corn fields to identify male and female tassels based on their position relative to the corn rows. The main objective was to identify female tassels that remained after the detasseling process and to quantify the remaining tassels that needed to be removed.
Tassels located within the designated row were considered male tassels, while tassels appearing outside the row were considered female tassels. The annotation focused particularly on the remaining female tassels after detasseling, because these indicate tassels that were not successfully removed and may require further attention.
A high-quality annotated dataset was prepared to support detasseling quality assessment, with particular focus on identifying remaining female tassels and reducing missed tassels in corn fields.
The final dataset contained consistent annotations for male and female tassels, with emphasis on identifying remaining female tassels after detasseling. The dataset was prepared for computer vision model training and automated detasseling quality monitoring.

Green cover, residue and bare soil segmented inside survey quadrats, with the quadrat frame itself located per image.
Bounding boxes around individual almond trees in top-down drone imagery, where shadows and neighbouring vegetation mimicked real trees.
Bounding boxes around individual apples on tree branches, where dense leaves and occlusion hid much of each fruit.