Automated extraction of pod phenotype data from micro-computed tomograph

A - Papers appearing in refereed journals

Corcoran, E., Siles-Suarez, L., Kurup, S. and Ahnert, S. 2023. Automated extraction of pod phenotype data from micro-computed tomograph. Frontiers in Plant Science. 14, p. 1120182.

AuthorsCorcoran, E., Siles-Suarez, L., Kurup, S. and Ahnert, S.

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 (Validation F1-score = 96.3%,Testing F1-score = 99.3%) and predicted the shape (mean matched score = 90%) of seeds.
Discussion: This method then allowed rapid extraction of data on the number, size, shape, seed spacing and seed location in specific valves that can be integrated into models of plant development or crop yield. Additionally, the fine-tuned StarDist-3D provides an efficient way to create a dataset of segmented images of individual seeds that could be used to further explore the factors affecting seed development, abortion and maturation synchrony within the pod. There is also potential for the fine-tuned Stardist-3D method to be applied to imagery of seeds from other plant species, as well as imagery of similarly shaped plant structures such as beans or wheat grains, provided the structures targeted for detection and segmentation can be described as star-convex polygons.

KeywordsPhenotyping; Plant development; Machine learning; Computer vision; Microcompute tomography
Year of Publication2023
JournalFrontiers in Plant Science
Journal citation14, p. 1120182
Digital Object Identifier (DOI)
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeTPM - Tailoring Plant Metabolism - Work package 1 (WP1) - High value lipids for health and industry
Brassica Rapeseed And Vegetable Optimisation (BRAVO)
Publisher's version
Output statusPublished
Publication dates
Online24 Feb 2023
Publication process dates
Accepted13 Feb 2023
PublisherFrontiers Media SA

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