The improved stratified transformer for organ segmentation of Arabidopsis

Yuhui Zheng; Dongwei Wang; Ning Jin; Xueguan Zhao; Fengmei Li; Fengbo Sun; Gang Dou; Haoran Bai; Yuhui Zheng; Dongwei Wang; Ning Jin; Xueguan Zhao; Fengmei Li; Fengbo Sun; Gang Dou; Haoran Bai

doi:10.3934/mbe.2024205

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 3: 4669-4697. doi: 10.3934/mbe.2024205

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The improved stratified transformer for organ segmentation of Arabidopsis

1.
College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
2.
Graduate School, Shenyang Jianzhu University, Shenyang 110168, China
3.
Beijing PAIDE Science and Technology Development Co., Ltd., Beijing 100097, China
4.
College of Food Science and Engineering, Qingdao Agricultural University, Qingdao 266109, China
5.
China Zhongxin Construction Engineering Co., Ltd., Qingdao 266205, China
6.
Weichai Lovol Intelligent Agricultural Technology Co., Ltd., Weifang 261000, China

Academic Editor: Guanqiu Qi

Received: 12 December 2023 Revised: 07 February 2024 Accepted: 08 February 2024 Published: 29 February 2024

Segmenting plant organs is a crucial step in extracting plant phenotypes. Despite the advancements in point-based neural networks, the field of plant point cloud segmentation suffers from a lack of adequate datasets. In this study, we addressed this issue by generating Arabidopsis models using L-system and proposing the surface-weighted sampling method. This approach enables automated point sampling and annotation, resulting in fully annotated point clouds. To create the Arabidopsis dataset, we employed Voxel Centroid Sampling and Random Sampling as point cloud downsampling methods, effectively reducing the number of points. To enhance the efficiency of semantic segmentation in plant point clouds, we introduced the Plant Stratified Transformer. This network is an improved version of the Stratified Transformer, incorporating the Fast Downsample Layer. Our improved network underwent training and testing on our dataset, and we compared its performance with PointNet++, PAConv, and the original Stratified Transformer network. For semantic segmentation, our improved network achieved mean Precision, Recall, F1-score and IoU of 84.20, 83.03, 83.61 and 73.11%, respectively. It outperformed PointNet++ and PAConv and performed similarly to the original network. Regarding efficiency, the training time and inference time were 714.3 and 597.9 ms, respectively, which were reduced by 320.9 and 271.8 ms, respectively, compared to the original network. The improved network significantly accelerated the speed of feeding point clouds into the network while maintaining segmentation performance. We demonstrated the potential of virtual plants and deep learning methods in rapidly extracting plant phenotypes, contributing to the advancement of plant phenotype research.
- virtual plant,
- plant phenotyping,
- deep learning,
- point cloud,
- plant segmentation
Citation: Yuhui Zheng, Dongwei Wang, Ning Jin, Xueguan Zhao, Fengmei Li, Fengbo Sun, Gang Dou, Haoran Bai. The improved stratified transformer for organ segmentation of Arabidopsis[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 4669-4697. doi: 10.3934/mbe.2024205

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Abstract

Segmenting plant organs is a crucial step in extracting plant phenotypes. Despite the advancements in point-based neural networks, the field of plant point cloud segmentation suffers from a lack of adequate datasets. In this study, we addressed this issue by generating Arabidopsis models using L-system and proposing the surface-weighted sampling method. This approach enables automated point sampling and annotation, resulting in fully annotated point clouds. To create the Arabidopsis dataset, we employed Voxel Centroid Sampling and Random Sampling as point cloud downsampling methods, effectively reducing the number of points. To enhance the efficiency of semantic segmentation in plant point clouds, we introduced the Plant Stratified Transformer. This network is an improved version of the Stratified Transformer, incorporating the Fast Downsample Layer. Our improved network underwent training and testing on our dataset, and we compared its performance with PointNet++, PAConv, and the original Stratified Transformer network. For semantic segmentation, our improved network achieved mean Precision, Recall, F1-score and IoU of 84.20, 83.03, 83.61 and 73.11%, respectively. It outperformed PointNet++ and PAConv and performed similarly to the original network. Regarding efficiency, the training time and inference time were 714.3 and 597.9 ms, respectively, which were reduced by 320.9 and 271.8 ms, respectively, compared to the original network. The improved network significantly accelerated the speed of feeding point clouds into the network while maintaining segmentation performance. We demonstrated the potential of virtual plants and deep learning methods in rapidly extracting plant phenotypes, contributing to the advancement of plant phenotype research.

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