3D Point Cloud on Semantic Information for Wheat Reconstruction

Yang, Yuhang and Zhang, Jinqian and Wu, Kangjie and Zhang, Xixin and Sun, Jun and Peng, Shuaibo and Li, Jun and Wang, Mantao (2021) 3D Point Cloud on Semantic Information for Wheat Reconstruction. Agriculture, 11 (5). p. 450. ISSN 2077-0472

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Abstract

Phenotypic analysis has always played an important role in breeding research. At present, wheat phenotypic analysis research mostly relies on high-precision instruments, which make the cost higher. Thanks to the development of 3D reconstruction technology, the reconstructed wheat 3D model can also be used for phenotypic analysis. In this paper, a method is proposed to reconstruct wheat 3D model based on semantic information. The method can generate the corresponding 3D point cloud model of wheat according to the semantic description. First, an object detection algorithm is used to detect the characteristics of some wheat phenotypes during the growth process. Second, the growth environment information and some phenotypic features of wheat are combined into semantic information. Third, text-to-image algorithm is used to generate the 2D image of wheat. Finally, the wheat in the 2D image is transformed into an abstract 3D point cloud and obtained a higher precision point cloud model using a deep learning algorithm. Extensive experiments indicate that the method reconstructs 3D models and has a heuristic effect on phenotypic analysis and breeding research by deep learning.

Item Type: Article
Subjects: Oalibrary Press > Agricultural and Food Science
Depositing User: Managing Editor
Date Deposited: 01 Dec 2022 05:30
Last Modified: 24 Jun 2024 04:15
URI: http://asian.go4publish.com/id/eprint/458

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