Hierarchical Panoptic Segmentation of Crops and Leaves with Boundary Loss
A computer-vision method that adapts the Mask2Former panoptic-segmentation architecture with a second transformer decoder so it segments whole plants and their individual leaves at once, while adding focal loss and boundary loss to better capture the tiny, class-imbalanced regions of leaves and weeds. Students learn how boundary-based loss integrals over region interfaces and focal down-weighting of easy pixels improve segmentation and leaf-counting accuracy in precision agriculture without increasing inference cost.
Exploiting Boundary Loss for the Hierarchical Panoptic Segmentation of Plants and Leaves
Precision agriculture leverages data and machine learning so that farmers can monitor their crops and target interventions precisely. This enables the precision application of herbicide only to weeds…