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Panoptic segmentation in agriculture is an advanced computer vision techniquethat provides a comprehensive understanding of field composition. Itfacilitates various tasks such as crop and weed segmentation, plant panopticsegmentation, and leaf instance segmentation, all aimed at addressingchallenges in agriculture. Exploring the application of panoptic segmentationin agriculture, the 8th Workshop on Computer Vision in Plant Phenotyping andAgriculture (CVPPA) hosted the challenge of hierarchical panoptic segmentationof crops and weeds using the PhenoBench dataset. To tackle the tasks presentedin this competition, we propose an approach that combines the effectiveness ofthe Segment AnyThing Model (SAM) for instance segmentation with prompt inputfrom object detection models. Specifically, we integrated two notableapproaches in object detection, namely DINO and YOLO-v8. Our best-performingmodel achieved a PQ+ score of 81.33 based on the evaluation metrics of thecompetition.

A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition