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.

农业中的全景分割是一种先进的计算机视觉技术，提供了对农田组成的全面理解，旨在应对农业中的各种任务，如作物和杂草分割、植物全景分割和叶子实例分割。本研究提出了一种方法，将实例分割模型（SAM）的有效性与目标检测模型（DINO 和 YOLO-v8）的输入相结合，以解决 PhenoBench 数据集上作物和杂草层次全景分割的挑战。在比赛的评估指标下，我们的最佳模型获得了 81.33 的 PQ + 得分。