Segment Anything for Comprehensive Analysis of Grapevine Cluster Architecture and Berry Properties.

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 dataset, enables automated object segmentation without additional training. This study demonstrates out-of-the-box SAM's high accuracy in identifying individual berries in 2-dimensional (2D) cluster images. Using this model, we managed to segment

SUBMITTER: Torres-Lomas E 

PROVIDER: S-EPMC11208874 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature