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Agronomy
Special Issues
Micro Phenotyping for Plant Breeding
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Micro Phenotyping for Plant Breeding

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5800

Special Issue Editors

Dr. Ying Zhang

E-Mail Website
Guest Editor
Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Interests: micro-phenotyping detection; multi-omics studies
Dr. Peng Song

E-Mail Website
Guest Editor
College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
Interests: crop nondestructive phenotyping techniques; crop phenotype information acquisition and analysis
Dr. Jianjun Du

E-Mail Website
Guest Editor
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing 100097, China
Interests: multiscale phenomics of crops; image understanding and 3D modeling; biomechanics and numerical simulation
Dr. Ruifang Zhai

E-Mail Website
Guest Editor
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
Interests: crop phenomics and computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A fundamental feature of plants’ form and function is the structure, organization, and biochemical composition of cells and tissues. Many important biological processes within an organism, including photosynthesis, the acquisition and transport of water and nutrients, response to stress, tissue biomechanics, and interactions with other organisms, directly occur in cells and tissues; thus, many important qualitative and developmental traits cannot be assessed by macroscopic approaches. To better understand how these processes proceed at the tissue and cellular scales and that interaction relationship, innovative micro-phenotyping modalities ranging from the intercellular scale to the mesoscopic tissue and organ level have to be introduced into the plant. However, the lack of precise plant phenotyping methods at tissue and cellular resolution limits our ability to dissect the genotype–environment interactions that are critical for understanding plant adaptation to different types of stresses and for linking genes with their function, expression, and localization.

This Special Issue will highlight the innovative imaging modalities of plant micro-phenotype information acquisition, artificial-intelligence-based computational tools for microscopic phenotyping extraction, studies on accurate identification of micro-phenotypes, genetic analysis, and structural–functional models based on micro-phenotypes.

Dr. Ying Zhang
Dr. Peng Song
Dr. Jianjun Du
Dr. Ruifang Zhai
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

  • micro-phenotype
  • artificial intelligence
  • genetic analysis
  • structural–functional model

Published Papers (3 papers)

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Research

14 pages, 3995 KiB  
Article
A Methodology Study on the Optimal Detection of Oil and Moisture Content in Soybeans Using LF-NMR and Its 2D T1-T2 Nuclear Magnetic Technology
by Yu Zhang, Jianxiang Zhao, Ying Gu, Yu Zhang, Yi Chen, Ping Song and Tao Yang
Agronomy 2023, 13(4), 1102; https://doi.org/10.3390/agronomy13041102 - 12 Apr 2023
Cited by 4 | Viewed by 2012
Abstract
In this study, we aimed to provide an accurate method for the detection of oil and moisture content in soybeans. Introducing two-dimensional low-field nuclear magnetic resonance (LF-2D-NMR) qualitatively solved the problem of overlapping component signals that one-dimensional (1D) LF-NMR techniques cannot distinguish in [...] Read more.
In this study, we aimed to provide an accurate method for the detection of oil and moisture content in soybeans. Introducing two-dimensional low-field nuclear magnetic resonance (LF-2D-NMR) qualitatively solved the problem of overlapping component signals that one-dimensional (1D) LF-NMR techniques cannot distinguish in soybean detection research. Soxhlet extraction, oven drying, LF-NMR spectrum, and LF-NMR oil and moisture content software were used to detect soybean oil and moisture content. The comparison showed that the LF-NMR oil and moisture content software was faster and more accurate than the other methods. The specific identification of the oil and moisture signals of soybean seeds using longitudinal relaxation time (T1) and transverse relaxation time (T2) successfully solved the problems of less mobile water, overlapping free water, and oil signals. Therefore, LF-2D-NMR can complement conventional LF-NMR assays, and this study provides a new method for the analysis and detection of moisture and oil in soybeans. Full article
(This article belongs to the Special Issue Micro Phenotyping for Plant Breeding)
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12 pages, 2473 KiB  
Article
CT-Based Phenotyping and Genome-Wide Association Analysis of the Internal Structure and Components of Maize Kernels
by Dazhuang Li, Jinglu Wang, Ying Zhang, Xianju Lu, Jianjun Du and Xinyu Guo
Agronomy 2023, 13(4), 1078; https://doi.org/10.3390/agronomy13041078 - 7 Apr 2023
Viewed by 1352
Abstract
The structure of the maize kernels plays a critical role in determining maize yield and quality, and high-throughput, non-destructive microscope phenotypic characteristics acquisition and analysis are of great importance. In this study, Micro-CT technology was used to obtain images of maize kernels. An [...] Read more.
The structure of the maize kernels plays a critical role in determining maize yield and quality, and high-throughput, non-destructive microscope phenotypic characteristics acquisition and analysis are of great importance. In this study, Micro-CT technology was used to obtain images of maize kernels. An automatic CT image analysis pipeline was then developed to extract 20 traits related to the three-dimensional structure of kernel, embryo, endosperm, and cavity. The determination coefficients for five volume-related traits (embryo, endosperm, silty endosperm, embryo cavity, and endosperm cavity) were 0.95, 0.95, 0.77, 0.73, and 0.94, respectively. Further, we analyzed the phenotypic variations among a group of 303 inbred lines and conducted genome-wide association studies (GWAS). A total of 26 significant SNP loci were associated with these traits that are closely related to kernel volume, and 62 candidate genes were identified. Functional analysis revealed that most candidate genes corresponding to cavity traits encoded stress resistance proteins, while those corresponding to embryo and endosperm traits encoded proteins involved in regulating plant growth and development. These results will improve the understanding of the phenotypic traits of maize kernels and will provide new theoretical support for in-depth analysis of the genetic mechanism of kernel structure traits. Full article
(This article belongs to the Special Issue Micro Phenotyping for Plant Breeding)
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20 pages, 4382 KiB  
Article
Accurate Phenotypic Identification and Genetic Analysis of the Ear Leaf Veins in Maize (Zea mays L.)
by Shangjing Guo, Mingyi Zhu, Jianjun Du, Jinglu Wang, Xianju Lu, Yu Jin, Minggang Zhang, Xinyu Guo and Ying Zhang
Agronomy 2023, 13(3), 753; https://doi.org/10.3390/agronomy13030753 - 4 Mar 2023
Cited by 1 | Viewed by 1992
Abstract
The ear leaf veins are an important transport structure in the maize "source" organ; therefore, the microscopic phenotypic characteristics and genetic analysis of the leaf veins are particularly essential for promoting the breeding of ideal maize varieties with high yield and quality. In [...] Read more.
The ear leaf veins are an important transport structure in the maize "source" organ; therefore, the microscopic phenotypic characteristics and genetic analysis of the leaf veins are particularly essential for promoting the breeding of ideal maize varieties with high yield and quality. In this study, the microscopic image of the complete blade cross section was realized using X-ray micro-computed tomography (micro-CT) technology with a resolution of 13.5 µm. Moreover, the veins’ phenotypic traits in the cross section of the complete maize leaf, including the number of leaf veins, midvein area, leaf width, and density of leaf veins, were automatically and accurately detected by a deep-learning-integrated phenotyping pipeline. Then, we systematically collected vein phenotypes of 300 inbred lines at the silking stage of the ear leaves. It was found that the leaf veins’ microscopic characteristics varied among the different subgroups. The number of leaf veins, the density of leaf veins, and the midvein area in the stiff-stalk (SS) subgroup were significantly higher than those of the other three subgroups, but the leaf width was the smallest. The leaf width in the tropical/subtropical (TST) subgroup was the largest, but there was no significant difference in the number of leaf veins between the TST subgroup and other subgroups. Combined with a genome-wide association study (GWAS), 61 significant single-nucleotide polymorphism markers (SNPs) and 29 candidate genes were identified. Among them, the candidate gene Zm00001d018081 regulating the number of leaf veins and Zm00001d027998 regulating the midvein area will provide new theoretical support for in-depth analysis of the genetic mechanism of maize leaf veins. Full article
(This article belongs to the Special Issue Micro Phenotyping for Plant Breeding)
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