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Plants
Special Issues
3D Imaging Techniques Adapted to Plant Phenomics
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3D Imaging Techniques Adapted to Plant Phenomics

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 5474

Special Issue Editors

Dr. Shichao Jin

E-Mail Website
Guest Editor
Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China
Interests: plant phenomics; remote sensing; deep learning; image analysis; LiDAR; 3D phenotyping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wanneng Yang

E-Mail Website
Guest Editor
College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
Interests: CT; plant phenomics
Special Issues, Collections and Topics in MDPI journals
Dr. Xinyu Guo

E-Mail Website
Guest Editor
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Interests: 3D imaging; 3D modeling; plant phenomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plant phenomics is the bridge for linking plant genomics and environmental studies, thereby improving plant breeding and management. Imaging techniques have improved high-throughput plant phenotyping due to their advantages in multi-dimensional data acquisition and analysis. Among them, 3D imaging techniques, such as LiDAR (light detection and ranging), CT (computed tomography), structured light, and multi-view images, provide powerful new tools for characterizing 3D traits that are unavailable from a single 2D perspective. Currently, the development of 3D imaging in plant phenotyping includes both facilities (sensors and platforms) and algorithms. This progress also improves 3D plant modeling across different spatial–temporal scales and disciplines, providing easier and less expensive association with genes and analysis of environmental practices. Although 3D imaging has been favored in plant phenotyping and modeling, its progress lags far behind 2D image-based plant phenotyping. Low-cost, high-throughput, and accurate 3D imaging phenotypic facilities and intelligent algorithms are urgently needed in order to boost 3D image-based plant phenomics applications.

Given the above context, this special issue invites submissions broadly contributing to 3D imaging-based plant phenomics. Specific topics of interest include:

  1. Development of low-cost, high-throughput, and accurate 3D imaging phenotypic facilities.
  2. Algorithms for processing 3D imaging data, including data fusion, reconstruction, segmentation, and 3D trait mining.
  3. Application of 3D traits to plant breeding, cultivation, and management.

Dr. Shichao Jin
Prof. Dr. Wanneng Yang
Dr. Xinyu Guo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 3D imaging
  • LiDAR
  • CT
  • structured light
  • plant phenomics

Published Papers (3 papers)

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Research

18 pages, 4062 KiB  
Article
Disentangling the Heterosis in Biomass Production and Radiation Use Efficiency in Maize: A Phytomer-Based 3D Modelling Approach
by Xiang Liu, Shenghao Gu, Weiliang Wen, Xianju Lu, Yu Jin, Yongjiang Zhang and Xinyu Guo
Plants 2023, 12(6), 1229; https://doi.org/10.3390/plants12061229 - 08 Mar 2023
Cited by 3 | Viewed by 1257
Abstract
Maize (Zea mays L.) benefits from heterosis in-yield formation and photosynthetic efficiency through optimizing canopy structure and improving leaf photosynthesis. However, the role of canopy structure and photosynthetic capacity in determining heterosis in biomass production and radiation use efficiency has not been [...] Read more.
Maize (Zea mays L.) benefits from heterosis in-yield formation and photosynthetic efficiency through optimizing canopy structure and improving leaf photosynthesis. However, the role of canopy structure and photosynthetic capacity in determining heterosis in biomass production and radiation use efficiency has not been separately clarified. We developed a quantitative framework based on a phytomer-based three-dimensional canopy photosynthesis model and simulated light capture and canopy photosynthetic production in scenarios with and without heterosis in either canopy structure or leaf photosynthetic capacity. The accumulated above-ground biomass of Jingnongke728 was 39% and 31% higher than its male parent, Jing2416, and female parent, JingMC01, while accumulated photosynthetically active radiation was 23% and 14% higher, correspondingly, leading to an increase of 13% and 17% in radiation use efficiency. The increasing post-silking radiation use efficiency was mainly attributed to leaf photosynthetic improvement, while the dominant contributing factor differs for male and female parents for heterosis in post-silking yield formation. This quantitative framework illustrates the potential to identify the key traits related to yield and radiation use efficiency and helps breeders to make selections for higher yield and photosynthetic efficiency. Full article
(This article belongs to the Special Issue 3D Imaging Techniques Adapted to Plant Phenomics)
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18 pages, 5303 KiB  
Article
Design and Development of a Low-Cost UGV 3D Phenotyping Platform with Integrated LiDAR and Electric Slide Rail
by Shuangze Cai, Wenbo Gou, Weiliang Wen, Xianju Lu, Jiangchuan Fan and Xinyu Guo
Plants 2023, 12(3), 483; https://doi.org/10.3390/plants12030483 - 20 Jan 2023
Cited by 2 | Viewed by 1913
Abstract
Unmanned ground vehicles (UGV) have attracted much attention in crop phenotype monitoring due to their lightweight and flexibility. This paper describes a new UGV equipped with an electric slide rail and point cloud high-throughput acquisition and phenotype extraction system. The designed UGV is [...] Read more.
Unmanned ground vehicles (UGV) have attracted much attention in crop phenotype monitoring due to their lightweight and flexibility. This paper describes a new UGV equipped with an electric slide rail and point cloud high-throughput acquisition and phenotype extraction system. The designed UGV is equipped with an autopilot system, a small electric slide rail, and Light Detection and Ranging (LiDAR) to achieve high-throughput, high-precision automatic crop point cloud acquisition and map building. The phenotype analysis system realized single plant segmentation and pipeline extraction of plant height and maximum crown width of the crop point cloud using the Random sampling consistency (RANSAC), Euclidean clustering, and k-means clustering algorithm. This phenotyping system was used to collect point cloud data and extract plant height and maximum crown width for 54 greenhouse-potted lettuce plants. The results showed that the correlation coefficient (R2) between the collected data and manual measurements were 0.97996 and 0.90975, respectively, while the root mean square error (RMSE) was 1.51 cm and 4.99 cm, respectively. At less than a tenth of the cost of the PlantEye F500, UGV achieves phenotypic data acquisition with less error and detects morphological trait differences between lettuce types. Thus, it could be suitable for actual 3D phenotypic measurements of greenhouse crops. Full article
(This article belongs to the Special Issue 3D Imaging Techniques Adapted to Plant Phenomics)
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17 pages, 3835 KiB  
Article
Geometric Wheat Modeling and Quantitative Plant Architecture Analysis Using Three-Dimensional Phytomers
by Wushuai Chang, Weiliang Wen, Chenxi Zheng, Xianju Lu, Bo Chen, Ruiqi Li and Xinyu Guo
Plants 2023, 12(3), 445; https://doi.org/10.3390/plants12030445 - 18 Jan 2023
Cited by 2 | Viewed by 1494
Abstract
The characterization, analysis, and evaluation of morphology and structure are crucial in wheat research. Quantitative and fine characterization of wheat morphology and structure from a three-dimensional (3D) perspective has great theoretical significance and application value in plant architecture identification, high light efficiency breeding, [...] Read more.
The characterization, analysis, and evaluation of morphology and structure are crucial in wheat research. Quantitative and fine characterization of wheat morphology and structure from a three-dimensional (3D) perspective has great theoretical significance and application value in plant architecture identification, high light efficiency breeding, and cultivation. This study proposes a geometric modeling method of wheat plants based on the 3D phytomer concept. Specifically, 3D plant architecture parameters at the organ, phytomer, single stem, and individual plant scales were extracted based on the geometric models. Furthermore, plant architecture vector (PA) was proposed to comprehensively evaluate wheat plant architecture, including convergence index (C), leaf structure index (L), phytomer structure index (PHY), and stem structure index (S). The proposed method could quickly and efficiently achieve 3D wheat plant modeling by assembling 3D phytomers. In addition, the extracted PA quantifies the plant architecture differences in multi-scales among different cultivars, thus, realizing a shift from the traditional qualitative to quantitative analysis of plant architecture. Overall, this study promotes the application of the 3D phytomer concept to multi-tiller crops, thereby providing a theoretical and technical basis for 3D plant modeling and plant architecture quantification in wheat. Full article
(This article belongs to the Special Issue 3D Imaging Techniques Adapted to Plant Phenomics)
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