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Open AccessArticle

Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

by Shunfu Xiao 1,2,3,†, Honghong Chai 1,†, Ke Shao 2, Mengyuan Shen 1, Qing Wang 1, Ruili Wang 2, Yang Sui 2 and Yuntao Ma 1,2,*
1
College of Land Science and Technology, China Agricultural University, Beijing 100193, China
2
Inner Mongolia Autonomous Region Biotechnology Research Institute, Huhehaote 010010, China
3
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2020, 12(2), 269; https://doi.org/10.3390/rs12020269
Received: 15 December 2019 / Revised: 6 January 2020 / Accepted: 10 January 2020 / Published: 14 January 2020
(This article belongs to the Special Issue Advanced Imaging for Plant Phenotyping)
Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding. View Full-Text
Keywords: field phenotyping; sugar beet; structure from motion; biomass prediction; leaf length field phenotyping; sugar beet; structure from motion; biomass prediction; leaf length
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MDPI and ACS Style

Xiao, S.; Chai, H.; Shao, K.; Shen, M.; Wang, Q.; Wang, R.; Sui, Y.; Ma, Y. Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field. Remote Sens. 2020, 12, 269.

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