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

Wenqi Zhang; Sheng Wu; Weiliang Wen; Xianju Lu; Chuanyu Wang; Wenbo Gou; Yuankun Li; Xinyu Guo; Chunjiang Zhao

doi:10.1186/s13007-023-01051-9

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

Plant Methods. 2023 Aug 1;19(1):76. doi: 10.1186/s13007-023-01051-9.

Authors

Wenqi Zhang^#^{1

2

3}, Sheng Wu^#^{1

2}, Weiliang Wen^{1

2}, Xianju Lu^{1

2}, Chuanyu Wang^{1

2}, Wenbo Gou^{1

2}, Yuankun Li^{1

2}, Xinyu Guo^{4

5}, Chunjiang Zhao^{6

7}

Affiliations

¹ Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
² Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
³ College of Information Technology, Shanghai Ocean University, Shanghai, 201306, China.
⁴ Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China. guoxy73@163.com.
⁵ Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China. guoxy73@163.com.
⁶ Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China. zhaocj@nercita.org.cn.
⁷ Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China. zhaocj@nercita.org.cn.

^# Contributed equally.

Abstract

Background: The morphological structure phenotype of maize tassel plays an important role in plant growth, reproduction, and yield formation. It is an important step in the distinctness, uniformity, and stability (DUS) testing to obtain maize tassel phenotype traits. Plant organ segmentation can be achieved with high-precision and automated acquisition of maize tassel phenotype traits 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 tip feature of point cloud is trained and learned based on PointNet + + network, and the tip point cloud of tassel branch was automatically segmented. 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 traits 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 traits of maize tassel. In addition, the incomplete annotation approach provides a new idea for morphology-based plant segmentation.

Keywords: DUS testing; Deep learning; Maize tassel; Organ segmentation; Point cloud; Tassel phenotype.

Abstract

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