Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning

Abstract

Background The morphological structure phenotype of maize tassel plays an important role in plant growth, reproduction, and yield formation. Plant organ segmentation can be achieved with high-precision and automated acquisition of maize tassel phenotype indicators because of the advances in the point cloud deep learning method. However, this method requires a large number of data sets and is not robust to automatic segmentation of highly adherent organ components; thus, it should be combined with point cloud processing technology. Results An innovative method of incomplete annotation of point cloud data was proposed for easy development of the dataset of maize tassels,and an automatic maize tassel phenotype analysis system: MaizeTasselSeg was developed. The top point cloud of tassel branch was automatically segmented based on PointNet + + network. Complete branch segmentation was realized based on the shortest path algorithm. The Intersection over Union(IoU), precision, and recall of the segmentation results were 96.29, 96.36, and 93.01, respectively. Six phenotypic indexes related to morphological structure (branch count, branch length, branch angle, branch curvature, tassel volume, and dispersion) were automatically extracted from the segmentation point cloud. The squared correlation coefficients (R2) for branch length, branch angle, and branch count were 0.9897, 0.9317, and 0.9587, respectively. The root mean squared error (RMSE) for branch length, branch angle, and branch count were 0.529 cm, 4.516°, and 0.875, respectively. Conclusion The proposed method provides an efficient scheme for high-throughput organ segmentation of maize tassels and can be used for the automatic extraction of phenotypic indicators of maize tassels. In addition, the incomplete annotation approach provides a new idea for morphology-based plant segmentation.