Automated extraction of pod phenotype data from micro-computed tomography.

ABSTRACT:

Introduction

Plant image datasets have the potential to greatly improve our understanding of the phenotypic response of plants to environmental and genetic factors. However, manual data extraction from such datasets are known to be time-consuming and resource intensive. Therefore, the development of efficient and reliable machine learning methods for extracting phenotype data from plant imagery is crucial.

Methods

In this paper, a current gold standard computed vision method for detecting and segmenting objects in three-dimensional imagery (StartDist-3D) is applied to X-ray micro-computed tomography scans of oilseed rape (Brassica napus) mature pods.

Results

With a relatively minimal training effort, this fine-tuned StarDist-3D model accurately detected (Valida

SUBMITTER: Corcoran E 

PROVIDER: S-EPMC9998914 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature