3D Point Cloud on Semantic Information for Wheat Reconstruction

Agriculture

Jinqian Zhang¹  Shuaibo Peng¹  Kangjie Wu¹  Jun Sun¹  Yuhang Yang¹  Xixin Zhang¹  Jun Li²  Mantao Wang² 

[1] College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China;Sichuan Key Laboratory of Agricultural Information Engineering, Ya’an 625000, China;

关键词: wheat phenotype;    object detection;    text-to-image;    3D point cloud;   

DOI  :  10.3390/agriculture11050450

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown