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Related Experiment Video

Updated: Aug 7, 2025

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Micron-scale Phenotyping Techniques of Maize Vascular Bundles Based on X-ray Microcomputed Tomography

Published on: October 9, 2018

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

Evangeline Corcoran¹, Laura Siles², Smita Kurup²

¹Environment and Sustainability Theme, AI for Science and Government Programme, The Alan Turing Institute, London, United Kingdom.

Frontiers in Plant Science

|March 13, 2023

Summary

A new machine learning method accurately detects and segments seeds in oilseed rape pods using StarDist-3D. This advances plant phenotyping by enabling rapid data extraction for crop yield and development models.

Keywords:

computer vision machine learning micro-compute tomography phenotyping plant development

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Area of Science:

Plant science
Computer vision
Machine learning

Background:

Manual extraction of plant phenotype data from imagery is time-consuming and resource-intensive.
Efficient and reliable machine learning methods are crucial for plant image analysis.

Purpose of the Study:

To apply and evaluate the StarDist-3D computed vision method for detecting and segmenting seeds in oilseed rape (Brassica napus) mature pods.
To assess the accuracy and efficiency of StarDist-3D for extracting quantitative seed data.

Main Methods:

The StarDist-3D method, a gold standard for 3D object detection and segmentation, was fine-tuned.
The method was applied to X-ray micro-computed tomography scans of oilseed rape mature pods.

Main Results:

The fine-tuned StarDist-3D model achieved high accuracy in seed detection (Validation F1-score = 96.3%, Testing F1-score = 99.3%) and shape prediction (mean matched score = 90%) with minimal training.
Rapid extraction of data on seed number, size, shape, spacing, and location within pods was enabled.

Conclusions:

The fine-tuned StarDist-3D method offers an efficient approach for plant phenotyping from 3D imagery.
This method facilitates the creation of segmented seed datasets for further research and has potential applications for other plant species and structures.