Abraham George Smith

The paper introduces a domain-specific foundation model for root segmentation, showing strong zero-shot and few-shot performance across multiple 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.

This paper develops a method to train deep-learning models for multi-species canopy segmentation using FCNNs and pseudo-labeling, achieving high accuracy with reduced human effort.

The paper describes a field facility for deep root research, showing significant differences in root density and activity among crop species at depths of 2.5 to 4.5 meters.

The paper introduces an automated method for measuring seminal root angles from images and validates its accuracy against manual measurements.