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 Vox… Show more
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