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Article ; Online: Phenotypic Traits Extraction of Wheat Plants Using 3D Digitization

ZHENG Chenxi / WEN Weiliang / LU Xianju / GUO Xinyu / ZHAO Chunjiang

智慧农业, Vol 4, Iss 2, Pp 150-

2022 Volume 162

Abstract: Aiming at the difficulty of accurately extract the phenotypic traits of plants and organs from images or point clouds caused by the multiple tillers and serious cross-occlusion among organs of wheat plants, to meet the needs of accurate phenotypic ... ...

Abstract	Aiming at the difficulty of accurately extract the phenotypic traits of plants and organs from images or point clouds caused by the multiple tillers and serious cross-occlusion among organs of wheat plants, to meet the needs of accurate phenotypic analysis of wheat plants, three-dimensional (3D) digitization was used to extract phenotypic parameters of wheat plants. Firstly, digital representation method of wheat organs was given and a 3D digital data acquisition standard suitable for the whole growth period of wheat was formulated. According to this standard, data acquisition was carried out using a 3D digitizer. Based on the definition of phenotypic parameters and semantic coordinates information contained in the 3D digitizing data, eleven conventional measurable phenotypic parameters in three categories were quantitative extracted, including lengths, thicknesses, and angles of wheat plants and organs. Furthermore, two types of new parameters for shoot architecture and 3D leaf shape were defined. Plant girth was defined to quantitatively describe the looseness or compactness by fitting 3D discrete coordinates based on the least square method. For leaf shape, wheat leaf curling and twisting were defined and quantified according to the direction change of leaf surface normal vector. Three wheat cultivars including FK13, XN979, and JM44 at three stages (rising stage, jointing stage, and heading stage) were used for method validation. The Open3D library was used to process and visualize wheat plant data. Visualization results showed that the acquired 3D digitization data of maize plants were realistic, and the data acquisition approach was capable to present morphological differences among different cultivars and growth stages. Validation results showed that the errors of stem length, leaf length, stem thickness, stem and leaf angle were relatively small. The R2 were 0.93, 0.98, 0.93, and 0.85, respectively. The error of the leaf width and leaf inclination angle were also satisfactory, the R2 were 0.75 and 0.73. ...
Keywords	wheat ; three-dimensional digitization ; visualization ; phenotypic traits extraction ; Agriculture (General) ; S1-972 ; Technology (General) ; T1-995
Subject code	580
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	Editorial Office of Smart Agriculture
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: CT-Based Phenotyping and Genome-Wide Association Analysis of the Internal Structure and Components of Maize Kernels

Li, Dazhuang / Wang, Jinglu / Zhang, Ying / Lu, Xianju / Du, Jianjun / Guo, Xinyu

Agronomy. 2023 Apr. 07, v. 13, no. 4

2023

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 ... ...

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 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.
Keywords	agronomy ; corn ; endosperm ; genome-wide association study ; growth and development ; image analysis ; micro-computed tomography ; phenotype ; plant growth ; stress tolerance
Language	English
Dates of publication	2023-0407
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2607043-1
ISSN	2073-4395
ISSN	2073-4395
DOI	10.3390/agronomy13041078
Database	NAL-Catalogue (AGRICOLA)

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Article: Disentangling the Heterosis in Biomass Production and Radiation Use Efficiency in Maize: A Phytomer-Based 3D Modelling Approach.

Liu, Xiang / Gu, Shenghao / Wen, Weiliang / Lu, Xianju / Jin, Yu / Zhang, Yongjiang / Guo, Xinyu

Plants (Basel, Switzerland)

2023 Volume 12, Issue 6

Abstract: Maize ( ...

Abstract	Maize (
Language	English
Publishing date	2023-03-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2704341-1
ISSN	2223-7747
ISSN	2223-7747
DOI	10.3390/plants12061229
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Design and Development of a Low-Cost UGV 3D Phenotyping Platform with Integrated LiDAR and Electric Slide Rail.

Cai, Shuangze / Gou, Wenbo / Wen, Weiliang / Lu, Xianju / Fan, Jiangchuan / Guo, Xinyu

Plants (Basel, Switzerland)

2023 Volume 12, Issue 3

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 ... ...

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 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.
Language	English
Publishing date	2023-01-20
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2704341-1
ISSN	2223-7747
ISSN	2223-7747
DOI	10.3390/plants12030483
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Geometric Wheat Modeling and Quantitative Plant Architecture Analysis Using Three-Dimensional Phytomers.

Chang, Wushuai / Wen, Weiliang / Zheng, Chenxi / Lu, Xianju / Chen, Bo / Li, Ruiqi / Guo, Xinyu

Plants (Basel, Switzerland)

2023 Volume 12, Issue 3

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 ... ...

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, 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 (
Language	English
Publishing date	2023-01-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2704341-1
ISSN	2223-7747
ISSN	2223-7747
DOI	10.3390/plants12030445
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network.

Luo, Zhizeng / Jin, Ronghang / Shi, Hongfei / Lu, Xianju

Neural plasticity

2021 Volume 2021, Page(s) 6655430

Abstract: Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain ...

Abstract	Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, the BFN is constructed on the basis of the threshold matrix of the Pearson correlation coefficient of the mu rhythm among the channels. In addition, a weighted BFN is constructed and expressed by the sum of the existing edge weights to characterize the cerebral cortex activation degree in different movement patterns. Then, on the basis of the topological structures of seven mental tasks, three regional networks centered on the C3, C4, and Cz channels are constructed, which are consistent with correspondence between limb movement patterns and cerebral cortex in neurophysiology. Furthermore, the CIR of each regional functional network is calculated to form three-dimensional vectors. Finally, we use the support vector machine to learn a classifier for multiclass MI tasks. Experimental results show a significant improvement and demonstrate the success of the extracted feature CIR in dealing with MI classification. Specifically, the average classification performance reaches 88.67% which is higher than other competing methods, indicating that the extracted CIR is effective for MI classification.
MeSH term(s)	Algorithms ; Brain/physiology ; Electroencephalography ; Humans ; Imagination/physiology ; Models, Neurological ; Nerve Net/physiology ; Recognition, Psychology/physiology
Language	English
Publishing date	2021-02-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1454938-4
ISSN	1687-5443 ; 2090-5904 ; 0792-8483
ISSN (online)	1687-5443
ISSN	2090-5904 ; 0792-8483
DOI	10.1155/2021/6655430
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence.

Wu, Yandong / Wen, Weiliang / Gu, Shenghao / Huang, Guanmin / Wang, Chuanyu / Lu, Xianju / Xiao, Pengliang / Guo, Xinyu / Huang, Linsheng

Plant phenomics (Washington, D.C.)

2024 Volume 6, Page(s) 160

Abstract: The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource competition. ... ...

Abstract	The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource competition. To address this issue, we propose a 3D maize modeling method based on computational intelligence. An initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture. The subsequent model considers the 3D phytomers of maize as intelligent agents. The aim is to maximize the ratio of sunlit leaf area, and by iteratively modifying the azimuth angle of the 3D phytomers, a 3D maize canopy model that maximizes light resource interception can be constructed. Additionally, the method incorporates a reflective approach to optimize the canopy and utilizes a mesh deformation technique for detecting and responding to leaf collisions within the canopy. Six canopy models of 2 varieties plus 3 planting densities was constructed for validation. The average
Language	English
Publishing date	2024-03-20
Publishing country	United States
Document type	Journal Article
ISSN	2643-6515
ISSN (online)	2643-6515
DOI	10.34133/plantphenomics.0160
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Plant microphenotype: from innovative imaging to computational analysis.

Zhang, Ying / Gu, Shenghao / Du, Jianjun / Huang, Guanmin / Shi, Jiawei / Lu, Xianju / Wang, Jinglu / Yang, Wanneng / Guo, Xinyu / Zhao, Chunjiang

Plant biotechnology journal

2024 Volume 22, Issue 4, Page(s) 802–818

Abstract: The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of ... ...

Abstract	The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.
MeSH term(s)	Artificial Intelligence ; Phenotype ; Genomics/methods ; Genotype ; Plants/genetics
Language	English
Publishing date	2024-01-13
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2136367-5
ISSN	1467-7652 ; 1467-7652
ISSN (online)	1467-7652
ISSN	1467-7652
DOI	10.1111/pbi.14244
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat.

Li, Wenrui / Wu, Sheng / Wen, Weiliang / Lu, Xianju / Liu, Haishen / Zhang, Minggang / Xiao, Pengliang / Guo, Xinyu / Zhao, Chunjiang

AoB PLANTS

2024 Volume 16, Issue 2, Page(s) plae019

Abstract: It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of ... ...

Abstract	It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research. Taking wheat as the research object, we propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale. Specifically, we use the MVS-Pheno platform to reconstruct the point cloud of wheat plants and segment organs through the deep learning algorithm. On this basis, we automatically reconstructed the 3D structure of leaves and tillers and extracted the morphological parameters of wheat. The results show that the semantic segmentation accuracy of organs is 95.2%, and the instance segmentation accuracy AP
Language	English
Publishing date	2024-03-29
Publishing country	England
Document type	Journal Article
ZDB-ID	2555823-7
ISSN	2041-2851
ISSN	2041-2851
DOI	10.1093/aobpla/plae019
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Accurate Phenotypic Identification and Genetic Analysis of the Ear Leaf Veins in Maize (Zea mays L.)

Guo, Shangjing / Zhu, Mingyi / Du, Jianjun / Wang, Jinglu / Lu, Xianju / Jin, Yu / Zhang, Minggang / Guo, Xinyu / Zhang, Ying

Agronomy. 2023 Mar. 04, v. 13, no. 3

2023

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 ... ...

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 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.
Keywords	Zea mays ; agronomy ; corn ; ears ; genes ; genetic analysis ; genome-wide association study ; leaf width ; leaves ; micro-computed tomography ; phenotype ; single nucleotide polymorphism
Language	English
Dates of publication	2023-0304
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2607043-1
ISSN	2073-4395
ISSN	2073-4395
DOI	10.3390/agronomy13030753
Database	NAL-Catalogue (AGRICOLA)

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