Organ-Level Instance Segmentation of Oilseed Rape at Seedling Stage Based on 3D Point Cloud

Abstract

Highlights Provides a low-cost, fast, accurate, and non-destructive method for segmenting stems and leaves of oilseed rape at the seedling stage. Different varieties and leaf shapes of oilseed rape can all be accurately and efficiently segmented at organ level. The results of accurate organ instance segmentation are used for further organ morphological structure analysis and organ-level phenotype extraction. The calculated phenotypic data is highly correlated with the true phenotypic data. Abstract. Organ-level plant point cloud instance segmentation is crucial for three-dimensional (3D) plant structure investigations and plant phenotypic formations. Due to the complexity of 3D plant structures, accurate plant organ segmentation methods remain a bottleneck in current development of plant organ phenotypes. In this work, we present a fast, accurate, low-cost, and non-destructive method of stem and leaf instance segmentation at the organ level for oilseed rape point cloud. Firstly, we use a 3D scanner to obtain the point cloud of oilseed rape plants. Then, principal component analysis (PCA) is used to perform surface feature extraction on the point cloud after preprocessing. Subsequently, the stems and leaves in different features are segmented by region growing. The irrational segmentation results are finally optimized. Segmentation results of stems and leaves of oilseed rape of different sizes and leaf shapes are evaluated using manually segmented oilseed rape point clouds as a benchmark. The precision, recall, F-score, and average accuracy of the proposed stem and leave segmentation method are 0.983, 0.897, 0.937, and 0.883, respectively. The experimental results suggest that the proposed method can accurately segment an oilseed rape 3D point cloud into instances corresponding to the organs of the plant. Additionally, the separated leaf and stem organs of oilseed rape can be further used for plant structure studies and organ phenotype extraction. The organ phenotypic parameters of oilseed rape are calculated based on the results of stem and leaf instances segmentation. The leaf area, stem length and stem angle of oilseed rape calculated from the point cloud are also compared with the corresponding manual measurements, which are highly correlated with coefficients of determination R2 (0.82-0.97). In conclusion, the proposed segmentation method can be applied as a fundamental segmentation step to extract organ phenotype data from oilseed rape plants. Namely, our research is valuable for precision breeding and basic plant research. Keywords: 3D point cloud, Oilseed rape plant, Phenotypic traits, Stem-leaf segmentation.