  Panoptic segmentation in agriculture is an advanced computer vision technique
that provides a comprehensive understanding of field composition. It
facilitates various tasks such as crop and weed segmentation, plant panoptic
segmentation, and leaf instance segmentation, all aimed at addressing
challenges in agriculture. Exploring the application of panoptic segmentation
in agriculture, the 8th Workshop on Computer Vision in Plant Phenotyping and
Agriculture (CVPPA) hosted the challenge of hierarchical panoptic segmentation
of crops and weeds using the PhenoBench dataset. To tackle the tasks presented
in this competition, we propose an approach that combines the effectiveness of
the Segment AnyThing Model (SAM) for instance segmentation with prompt input
from object detection models. Specifically, we integrated two notable
approaches in object detection, namely DINO and YOLO-v8. Our best-performing
model achieved a PQ+ score of 81.33 based on the evaluation metrics of the
competition.
