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 (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.

Plant phenotyping relevance

植物のX線マイクロCT画像から種子を検出・分割し、個数・サイズ・形状・間隔・位置を抽出する機械学習手法の開発と検証が中心であるため。

Code and data availability

The supplied blocks describe a StarDist-3D pipeline for extracting seed phenotypes from µCT scans of Brassica napus pods, but contain no data availability statement, no public repository deposit, no authors' code/model URL, and no explicit statement that the supplementary material contains datasets, images, code, or a

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