Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots.

Abstract

Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4-10 minutes to segment a maize shoot and consumes 10-20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

Plant phenotyping relevance

トウモロコシの3D点群を器官レベルに分割・注釈するツールを開発し、植物形態の表現型推定と深層学習用データ構築を技術的に支援するため、フェノタイピング手法が中心です。

Code and data availability

The paper's authors publicly released the Label3DMaize toolkit (MATLAB source code and executable) on GitHub, which implements the paper's point cloud segmentation/annotation analysis for maize shoots. A supporting data deposit (GigaScience Database, 10.5524/100884) is cited but its URL is not among the allowed URLs,so