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.
Copyright © 2023 Zhihong Ma et al.