Larry M. York

Vision Transformers Enable Advanced Plant Phenotyping in Controlled Environments

This paper compares segmentation methods for plant phenotyping, finding that pretrained vision transformers outperform color thresholding and supervised U-Nets in generalization across species and conditions.

Transformers Outperform ConvNets for Root Segmentation: A Systematic Comparison Across Nine Datasets

The paper compares Transformer and ConvNet models for root segmentation across nine datasets, finding Transformers significantly outperform ConvNets in accuracy and root-diameter agreement.

Divide and conquer: Using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods

The paper introduces two methods to standardize root measurement aggregation across multiple scans using image concatenation and statistical analysis, showing they produce consistent results except for median diameter.

A novel multi-perspective imaging platform (M-PIP) for phenotyping soybean root crowns in the field increases throughput and separation ability of genotype root properties