Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.3
Journals
Agronomy
Special Issues
3D Phenotyping for Plant Breeding and Management
share announcement

3D Phenotyping for Plant Breeding and Management

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (26 February 2023) | Viewed by 8529

Special Issue Editors

Dr. Weiliang Wen

E-Mail Website
Guest Editor
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: digital plant; plant phenotyping; 3D modelling; 3D reconstruction; visual computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuntao Ma

E-Mail Website
Guest Editor
College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Interests: 3D phenotyping; precision agriculture; crop modeling; UAV proximity; image analysis; multi-source data fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Yufeng Ge

E-Mail Website
Guest Editor
Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
Interests: sensor-based plant phenotyping; optoelectronic sensor development in agriculture; VIS/NIR/MIR spectroscopy; agricultural remote sensing and image analysis; precision agriculture and spatial statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Plant genotyping and phenotyping technologies have significantly accelerated plant breeding and management; however, compared with the rapid development of genotyping technologies, the inability to efficiently and accurately capture complex phenotypic traits has become a bottleneck, limiting progress in breeding programs. Structural and morphological phenotypes are basic and intuitive means of evaluating crop growth and development, composing a group of important agronomist trait concerns. With the development of LiDAR, the three-dimensional (3D) scanner, depth camera, and multi-view stereo reconstruction algorithms, acquiring 3D data of plants has become easy and low-cost, 3D plant phenotyping having become an emerging research area in plant phenomics. This Special Issue plans to collect recent advances in 3D plant phenotyping promoting the development of plant breeding, cultivation, and management, aiming to provide selected contributions regarding advances in algorithms, platforms, and applications of 3D plant phenotyping.

Potential topics include, but are not limited to:

  • High-throughput 3D plant phenotyping platforms;
  • 3D data processing algorithms for plants;
  • Structural and morphological phenotypes extraction methods;
  • 3D reconstruction approaches of plants;
  • 3D modeling of plants;
  • Evaluating plant growth and development in 3D space. 

Dr. Weiliang Wen
Prof. Dr. Yuntao Ma
Prof. Dr. Yufeng Ge
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. Agronomy is an international peer-reviewed open access monthly 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 2600 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

  • structure and morphology
  • LiDAR
  • multi-view stereo reconstruction
  • 3D point cloud
  • point cloud segmentation
  • 3D phenotyping platform
  • high-throughput phenotyping
  • data fusion
  • 3D reconstruction
  • 3D modelling
  • deep learning
  • plant growth

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 5317 KiB  
Article
Three-Dimensional Reconstruction and Predictive Simulation Algorithm of Forest and Fruit Wood Borer Galleries Based on Two-Dimensional Images and Different Influencing Factors
by Xiao Li, Meng Yang and Wanlu Li
Agronomy 2022, 12(5), 1087; https://doi.org/10.3390/agronomy12051087 - 29 Apr 2022
Cited by 1 | Viewed by 1687
Abstract
Since forest and fruit wood borer insects are very harmful, and the formed galleries are complex and not easy to observe, the 3D reconstruction and visual prediction simulation of their galleries are of great importance in agricultural and forestry research. A single image-based [...] Read more.
Since forest and fruit wood borer insects are very harmful, and the formed galleries are complex and not easy to observe, the 3D reconstruction and visual prediction simulation of their galleries are of great importance in agricultural and forestry research. A single image-based 3D reconstruction and visualization method is proposed. The method is divided into two steps: (1) photographing the complete insect galleries on different sample wood segments, correcting the images to obtain the complete insect tract outline, and then redefining the height of model expansion based on the distance from the outline to the midline of the outline via the sketch-based reconstruction method to reconstruct the 3D geometric model of insect tracts; (2) setting the influencing factors, such as forest and fruit wood borer pest species, host plants and insect population density, and simultaneously judging the newly added sample points and updating the original skeleton points according to the category of sample points and the comprehensive consideration of influencing factors, so as to obtain the changes of insect gallery structure under different conditions and achieve the predictive simulation of insect tract structure. We found that modeling 3D wood borer galleries by different pests on different host plants can be achieved. Compared to the hand drawing method, our method can obtain 3D models in a very short time, and the experimental models are all reconstructed within 1.5 s. The predicted variation in the range of insect tracts indicate that it was inversely proportional to the population density and positively proportional to the moth-eating ability of the pests, indicating that the method reflects the relationship between the range of insect tracts and the influencing factors. The proposed method provides a new approach to the study and control of wood borer galleries in the forest and fruit industry. In conclusion, we provide a method to reconstruct and predict the wood borer galleries in three dimensions. Full article
(This article belongs to the Special Issue 3D Phenotyping for Plant Breeding and Management)
►▼ Show Figures

Figure 1

18 pages, 3662 KiB  
Article
Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud
by Yunling Liu, Guoli Zhang, Ke Shao, Shunfu Xiao, Qing Wang, Jinyu Zhu, Ruili Wang, Lei Meng and Yuntao Ma
Agronomy 2022, 12(4), 893; https://doi.org/10.3390/agronomy12040893 - 7 Apr 2022
Cited by 2 | Viewed by 2612
Abstract
Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we [...] Read more.
Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping leaves. Finally, we used the surface boundary filter (SBF) method to segment overlapping leaves and obtained all leaves of the whole plant. Segmentation results of plants with different complexities of leaf arrangement were evaluated using the manually segmented leaf point clouds as benchmarks. Our results suggested that the proposed method can effectively segment the 3D point cloud of individual leaves for field grown sugar beet plants. The leaf length and leaf area of the segmented leaf point clouds were calculated and compared with observations. The calculated leaf length and leaf area were highly correlated with the observations with R2 (0.80–0.82). It was concluded that the MSTVM-based region-growing algorithm combined with SBF can be used as a basic segmentation step for high-throughput plant phenotypic data extraction of field sugar beet plants. Full article
(This article belongs to the Special Issue 3D Phenotyping for Plant Breeding and Management)
►▼ Show Figures

Figure 1

21 pages, 34318 KiB  
Article
Three-Dimensional Wheat Modelling Based on Leaf Morphological Features and Mesh Deformation
by Chenxi Zheng, Weiliang Wen, Xianju Lu, Wushuai Chang, Bo Chen, Qiang Wu, Zhiwei Xiang, Xinyu Guo and Chunjiang Zhao
Agronomy 2022, 12(2), 414; https://doi.org/10.3390/agronomy12020414 - 7 Feb 2022
Cited by 8 | Viewed by 2859
Abstract
The three-dimensional (3D) morphological structure of wheat directly reflects the interrelationship among genetics, environments, and cropping systems. However, the morphological complexity of wheat limits its rapid and accurate 3D modelling. We have developed a 3D wheat modelling method that is based on the [...] Read more.
The three-dimensional (3D) morphological structure of wheat directly reflects the interrelationship among genetics, environments, and cropping systems. However, the morphological complexity of wheat limits its rapid and accurate 3D modelling. We have developed a 3D wheat modelling method that is based on the progression from skeletons to mesh models. Firstly, we identified five morphological parameters that describe the 3D leaf features of wheat from amounts of 3D leaf digitizing data at the grain filling stage. The template samples were selected based on the similarity between the input leaf skeleton and leaf templates in the constructed wheat leaf database. The leaf modelling was then performed using the as-rigid-as-possible (ARAP) mesh deformation method. We found that 3D wheat modelling at the individual leaf level, leaf group, and individual plant scales can be achieved. Compared with directly acquiring 3D digitizing data for 3D modelling, it saves 79.9% of the time. The minimum correlation R2 of the extracted morphological leaf parameters between using the measured data and 3D model by this method was 0.91 and the maximum RMSE was 0.03, implying that this method preserves the morphological leaf features. The proposed method provides a strong foundation for further morphological phenotype extraction, functional–structural analysis, and virtual reality applications in wheat plants. Overall, we provide a new 3D modelling method for complex plants. Full article
(This article belongs to the Special Issue 3D Phenotyping for Plant Breeding and Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop