Segment Anything for comprehensive analysis of grapevine cluster architecture and berry properties

arXiv:2403.12935v1 Announce Type: new
Abstract: Grape cluster architecture and compactness are complex traits influencing disease susceptibility, fruit quality, and yield. Evaluation methods for these traits include visual scoring, manual methodologies, and computer vision, with the latter being the most scalable approach. Most of the existing computer vision approaches for processing cluster images often rely on conventional segmentation or machine learning with extensive training and limited generalization. The Segment Anything Model (SAM), a novel foundation model trained on a massive image …

abstract analysis architecture arxiv cluster computer computer vision cs.cv disease evaluation quality scalable scoring segment segment anything type vision visual