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

Teng, Miao; Wen, Weiliang; Li, Yinglun; Wu, Sheng Nan; Zhu, Chao; Guo, Xinyu

doi:10.1093/gigascience/giab031

Cited by 26 publications

(13 citation statements)

References 54 publications

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“…For instance, the hardware should be made lighter and easier to disassemble to make the platform easier to deploy and save labor costs. Point cloud segmentation (Miao et al, 2021 ) and phenotype extraction algorithms for specific plant species should be developed.…”

Section: Discussionmentioning

confidence: 99%

A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction

Wen

Gou

et al. 2022

Front. Plant Sci.

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Plant phenotyping is essential in plant breeding and management. High-throughput data acquisition and automatic phenotypes extraction are common concerns in plant phenotyping. Despite the development of phenotyping platforms and the realization of high-throughput three-dimensional (3D) data acquisition in tall plants, such as maize, handling small-size plants with complex structural features remains a challenge. This study developed a miniaturized shoot phenotyping platform MVS-Pheno V2 focusing on low plant shoots. The platform is an improvement of MVS-Pheno V1 and was developed based on multi-view stereo 3D reconstruction. It has the following four components: Hardware, wireless communication and control, data acquisition system, and data processing system. The hardware sets the rotation on top of the platform, separating plants to be static while rotating. A novel local network was established to realize wireless communication and control; thus, preventing cable twining. The data processing system was developed to calibrate point clouds and extract phenotypes, including plant height, leaf area, projected area, shoot volume, and compactness. This study used three cultivars of wheat shoots at four growth stages to test the performance of the platform. The mean absolute percentage error of point cloud calibration was 0.585%. The squared correlation coefficient R2 was 0.9991, 0.9949, and 0.9693 for plant height, leaf length, and leaf width, respectively. The root mean squared error (RMSE) was 0.6996, 0.4531, and 0.1174 cm for plant height, leaf length, and leaf width. The MVS-Pheno V2 platform provides an alternative solution for high-throughput phenotyping of low individual plants and is especially suitable for shoot architecture-related plant breeding and management studies.

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Section: Discussionmentioning

confidence: 99%

A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction

Wen

Gou

et al. 2022

Front. Plant Sci.

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Section: Methodsmentioning

confidence: 99%

“…Too much data significantly reduces the segmentation efficiency when performing point-cloud segmentation. In this study, the segmentation process was improved using the method described by Miao et al (2021a). The number of point clouds was first down-sampled to about 15,000 points while maintaining the local geometric characteristics of maize.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot

et al. 2023

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The 3D point cloud data are used to analyze plant morphological structure. Organ segmentation of a single plant can be directly used to determine the accuracy and reliability of organ-level phenotypic estimation in a point-cloud study. However, it is difficult to achieve a high-precision, automatic, and fast plant point cloud segmentation. Besides, a few methods can easily integrate the global structural features and local morphological features of point clouds relatively at a reduced cost. In this paper, a distance field-based segmentation pipeline (DFSP) which could code the global spatial structure and local connection of a plant was developed to realize rapid organ location and segmentation. The terminal point clouds of different plant organs were first extracted via DFSP during the stem-leaf segmentation, followed by the identification of the low-end point cloud of maize stem based on the local geometric features. The regional growth was then combined to obtain a stem point cloud. Finally, the instance segmentation of the leaf point cloud was realized using DFSP. The segmentation method was tested on 420 maize and compared with the manually obtained ground truth. Notably, DFSP had an average processing time of 1.52 s for about 15,000 points of maize plant data. The mean precision, recall, and micro F1 score of the DFSP segmentation algorithm were 0.905, 0.899, and 0.902, respectively. These findings suggest that DFSP can accurately, rapidly, and automatically achieve maize stem-leaf segmentation tasks and could be effective in maize phenotype research. The source code can be found at https://github.com/syau-miao/DFSP.git.

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“…References [42,43] carried out 3D reconstruction and phenotypic analysis on crops by using the multiview stereo (MVS) technique. Miao et al designed a toolkit-Label3DMaize [44], for annotating 3D point cloud data of maize shoots; the toolkit facilitates the preparation of manually labeled maize 3D data for training and testing on machine learning models.…”

Section: Introductionmentioning

confidence: 99%

PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants

Xiang

et al. 2022

Plant Phenomics

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Phenotyping of plant growth improves the understanding of complex genetic traits and eventually expedites the development of modern breeding and intelligent agriculture. In phenotyping, segmentation of 3D point clouds of plant organs such as leaves and stems contributes to automatic growth monitoring and reflects the extent of stress received by the plant. In this work, we first proposed the Voxelized Farthest Point Sampling (VFPS), a novel point cloud downsampling strategy, to prepare our plant dataset for training of deep neural networks. Then, a deep learning network—PSegNet, was specially designed for segmenting point clouds of several species of plants. The effectiveness of PSegNet originates from three new modules including the Double-Neighborhood Feature Extraction Block (DNFEB), the Double-Granularity Feature Fusion Module (DGFFM), and the Attention Module (AM). After training on the plant dataset prepared with VFPS, the network can simultaneously realize the semantic segmentation and the leaf instance segmentation for three plant species. Comparing to several mainstream networks such as PointNet++, ASIS, SGPN, and PlantNet, the PSegNet obtained the best segmentation results quantitatively and qualitatively. In semantic segmentation, PSegNet achieved 95.23%, 93.85%, 94.52%, and 89.90% for the mean Prec, Rec, F1, and IoU, respectively. In instance segmentation, PSegNet achieved 88.13%, 79.28%, 83.35%, and 89.54% for the mPrec, mRec, mCov, and mWCov, respectively.

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

Cited by 26 publications

References 54 publications

A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction

A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction

DFSP: A fast and automatic distance field-based stem-leaf segmentation pipeline for point cloud of maize shoot

PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants

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