3D Annotation and deep learning for cotton plant part segmentation and architectural trait extraction

Abstract

Background: Plant architecture can influence crop yield and quality. Manual extraction of architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation from 3D data allows for highly accurate results with the availability of depth information. The goal of this study was to allow 3D annotation and apply 3D deep learning model using both point and voxel representations of the 3D data to segment cotton plant parts and derive important architectural traits. Results The Point Voxel Convolutional Neural Network (PVCNN) combining both point- and voxel-based representations of data shows less time consumption and better segmentation performance than point-based networks. The segmented plants were postprocessed using correction algorithms for the main stem and branch. From the postprocessed results, seven architectural traits were extracted including main stem height, main stem diameter, number of branches, number of nodes, branch inclination angle, branch diameter and number of bolls. Results indicate that the best mIoU (89.12%) and accuracy (96.19%) with average inference time of 0.88 seconds were achieved through PVCNN, compared to Pointnet and Pointnet++. On the seven derived architectural traits from segmented parts, an R 2 value of more than 0.8 and mean absolute percentage error of less than 10% were attained. Conclusion This plant part segmentation method based on 3D deep learning enables effective and efficient architectural trait measurement from point clouds, which could be useful to advance plant breeding programs and characterization of in-season developmental traits.

Plant phenotyping relevance

3D深層学習による綿花の部位分割と建築形質抽出が研究の中心であり、精度・推論時間・形質推定性能も検証しているため。

Code and data availability

The preprint describes a custom 3D annotation tool (PlantCloud), a manually annotated cotton point cloud dataset (30 plants), and PVCNN/Pointnet/Pointnet++ models, but no block contains an availability statement, deposit, or authors' public URL for the dataset, code, tool, or trained models. The only URLs present (Hit치

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