Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping.

ABSTRACT: The rise of self-supervised learning (SSL) methods in recent years presents an opportunity to leverage unlabeled and domain-specific datasets generated by image-based plant phenotyping platforms to accelerate plant breeding programs. Despite the surge of research on SSL, there has been a scarcity of research exploring the applications of SSL to image-based plant phenotyping tasks, particularly detection and counting tasks. We address this gap by benchmarking the performance of 2 SSL methods-momentum contrast (MoCo) v2 and dense contrastive learning (DenseCL)-against the conventional supervised learning method when transferring learned representations to 4 downstream (target) image-based plant phenotyping tasks: wheat head detection, plant instance detection, wheat spikelet counting, and le

SUBMITTER: Ogidi FC 

PROVIDER: S-EPMC10079263 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature