Phenotyping of Silique Morphology in Oilseed Rape Using Skeletonization with Hierarchical Segmentation

Abstract

Silique morphology is an important trait that determines the yield output of oilseed rape ( Brassica napus L .). Segmenting siliques and quantifying traits are challenging because of the complicated structure of an oilseed rape plant at the reproductive stage. This study aims to develop an accurate method in which a skeletonization algorithm was combined with the hierarchical segmentation (SHS) algorithm to separate siliques from the whole plant using 3-dimensional (3D) point clouds. We combined the L1-median skeleton with the random sample consensus for iteratively extracting skeleton points and optimized the skeleton based on information such as distance, angle, and direction from neighborhood points. Density-based spatial clustering of applications with noise and weighted unidirectional graph were used to achieve hierarchical segmentation of siliques. Using the SHS, we quantified the silique number (SN), silique length (SL), and silique volume (SV) automatically based on the geometric rules. The proposed method was tested with the oilseed rape plants at the mature stage grown in a greenhouse and field. We found that our method showed good performance in silique segmentation and phenotypic extraction with R 2 values of 0.922 and 0.934 for SN and total SL, respectively. Additionally, SN, total SL, and total SV had the statistical significance of correlations with the yield of a plant, with R values of 0.935, 0.916, and 0.897, respectively. Overall, the SHS algorithm is accurate, efficient, and robust for the segmentation of siliques and extraction of silique morphological parameters, which is promising for high-throughput silique phenotyping in oilseed rape breeding.

Plant phenotyping relevance

3D点群からシリクを分離し、形態形質を自動抽出する手法の開発と性能評価が研究の中心であるため。

Code and data availability

The supplied blocks describe the SHS algorithm, plant materials, and point cloud acquisition, but contain no data availability statement, no public dataset or code repository URL for the authors' phenotyping data or analysis code, and no trained model checkpoints. The only URLs present are the CC BY license link and a

No evidence-backed public reproduction asset is currently recorded.