3D Point Cloud on Semantic Information for Wheat Reconstruction

Yuhang Yang, Jinqian Zhang, Kangjie Wu, Xixin Zhang, Jun Sun, Shuaibo Peng, Jun Li and Mantao Wang
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Yuhang Yang: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Jinqian Zhang: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Kangjie Wu: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Xixin Zhang: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Jun Sun: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Shuaibo Peng: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Jun Li: Sichuan Key Laboratory of Agricultural Information Engineering, Ya’an 625000, China
Mantao Wang: Sichuan Key Laboratory of Agricultural Information Engineering, Ya’an 625000, China

Agriculture, 2021, vol. 11, issue 5, 1-16

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.

Keywords: wheat phenotype; object detection; text-to-image; 3D point cloud (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
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