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Remote Sens. 2019, 11(1), 63; https://doi.org/10.3390/rs11010063

Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates

1,2,†
,
2,3,†
,
2,3
,
2,3
,
2,3
,
2,3,* and 1,2,3,*
1
Agricultural College, Nanjing Agricultural University, Nanjing 210095, China
2
Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
3
Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 27 November 2018 / Revised: 27 December 2018 / Accepted: 28 December 2018 / Published: 31 December 2018
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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. View Full-Text
Keywords: maize plant; phenotyping; three-dimensional digitizing; multi-view stereo; three-dimensional scanning; point cloud maize plant; phenotyping; three-dimensional digitizing; multi-view stereo; three-dimensional scanning; point cloud
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, Y.; Wen, W.; Wu, S.; Wang, C.; Yu, Z.; Guo, X.; Zhao, C. Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates. Remote Sens. 2019, 11, 63.

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