Automatic Root Length Estimation from Images Acquired In Situ without Segmentation.

ABSTRACT: Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examinin

SUBMITTER: Khoroshevsky F 

PROVIDER: S-EPMC10790720 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature