Paper Info
Title
Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates.
Abstract
High-throughput phenotyping technologies have become an increasingly important topic of crop science in recent years. Various sensors and data acquisition approaches have been applied to acquire the phenotyping traits. It is quite confusing for crop phenotyping researchers to determine an appropriate way for their application. In this study, three representative three-dimensional (3D) data acquisition approaches, including 3D laser scanning, multi-view stereo (MVS) reconstruction, and 3D digitizing, were evaluated for maize plant phenotyping in multi growth stages. Phenotyping traits accuracy, post-processing difficulty, device cost, data acquisition efficiency, and automation were considered during the evaluation process. 3D scanning provided satisfactory point clouds for medium and high maize plants with acceptable efficiency, while the results were not satisfactory for small maize plants. The equipment used in 3D scanning is expensive, but is highly automatic. MVS reconstruction provided satisfactory point clouds for small and medium plants, and point deviations were observed in upper parts of higher plants. MVS data acquisition, using low-cost cameras, exhibited the highest efficiency among the three evaluated approaches. The one-by-one pipeline data acquisition pattern allows the use of MVS high-throughput in further phenotyping platforms. Undoubtedly, enhancement of point cloud processing technologies is required to improve the extracted phenotyping traits accuracy for both 3D scanning and MVS reconstruction. Finally, 3D digitizing was time-consuming and labor intensive. However, it does not depend on any post-processing algorithms to extract phenotyping parameters and reliable phenotyping traits could be derived. The promising accuracy of 3D digitizing is a better verification choice for other 3D phenotyping approaches. Our study provides clear reference about phenotyping data acquisition of maize plants, especially for the affordable and portable field phenotyping platforms to be developed.
Year
DOI
Venue
2019
10.3390/rs11010063
REMOTE SENSING
Keywords
Field
DocType
maize plant,phenotyping,three-dimensional digitizing,multi-view stereo,three-dimensional scanning,point cloud
Computer vision,Plant phenotyping,Point cloud processing,Laser scanning,Data acquisition,Automation,Stereo reconstruction,Artificial intelligence,Point cloud,Geology
Journal
Volume
Issue
Citations 
11
1
0
PageRank 
References 
Authors
0.34
8
7
Name
Order
Citations
PageRank
Yongjian Wang100.34
Weiliang Wen222.15
Sheng Wu36111.46
Chuan-Yu Wang47310.68
Zetao Yu500.34
Xinyu Guo677.29
J.-C. Zhao713552.42