Crop phenomics has rapidly progressed in recent years due to the growing need for crop functional geno-mics,digital breeding,and smart cultivation.Despite this advancement,the lack of standards for the cre-ation and u...Crop phenomics has rapidly progressed in recent years due to the growing need for crop functional geno-mics,digital breeding,and smart cultivation.Despite this advancement,the lack of standards for the cre-ation and usage of crop phenomics technology and equipment has become a bottleneck,limiting the industry’s high-quality development.This paper begins with an overview of the crop phenotyping indus-try and presents an industrial mapping of technology and equipment for big data in crop phenomics.It analyzes the necessity and current state of constructing a standard framework for crop phenotyping.Furthermore,this paper proposes the intended organizational structure and goals of the standard frame-work.It details the essentials of the standard framework in the research and development of hardware and equipment,data acquisition,and the storage and management of crop phenotyping data.Finally,it discusses promoting the construction and evaluation of the standard framework,aiming to provide ideas for developing a high-quality standard framework for crop phenotyping.展开更多
Maize(Zea mays L.) yield depends not only on the conversion and accumulation of carbohydrates in kernels, but also on the supply of carbohydrates by leaves. However, the carbon metabolism process in leaves can vary ac...Maize(Zea mays L.) yield depends not only on the conversion and accumulation of carbohydrates in kernels, but also on the supply of carbohydrates by leaves. However, the carbon metabolism process in leaves can vary across different leaf regions and during the day and night. Hence, we used Weighted Gene Co-expression Network analysis(WGCNA) with the gene expression profiles of carbon metabolism to identify the modules and genes that may associate with particular regions in a leaf and time of day. There were a total of 45 samples of maize leaves that were taken from three different regions of a growing maize leaf at five time points. Robust Multi-array Average analysis was used to pre-process the raw data of GSE85963(accession number), and quality control of data was based on Pearson correlation coefficients. We obtained eight co-expression network modules. The modules with the highest significance of association with LeafRegion and TimePoint were selected. Functional enrichment and gene-gene interaction analyses were conducted to acquire the hub genes and pathways in these significant modules. These results can support the findings of similar studies by providing evidence of potential module genes and enriched pathways associated with leaf development in maize.展开更多
Plant vascular bundles are responsible for water and material transportation, and their quantitative and functional evaluation is desirable in plant research. At the single-plant level, the number, size, and distribut...Plant vascular bundles are responsible for water and material transportation, and their quantitative and functional evaluation is desirable in plant research. At the single-plant level, the number, size, and distribution of vascular bundles vary widely, posing a challenge to automatically and accurately identifying and quantifying them. In this study, a deep learning-integrated phenotyping pipeline was developed to robustly and accurately detect vascular bundles in Computed Tomography(CT) images of stem internodes. Two semantic indicators were used to evaluate and identify a suitable feature extraction network for semantic segmentation models. The epidermis thickness of maize stem was evaluated for the first time and adjacent vascular bundles were improved using an adaptive watershed-based approach. The counting accuracy(R^(2)) of vascular bundles was 0.997 for all types of stem internodes, and the measured accuracy of size traits was over 0.98. Combining sap flow experiments, multiscale traits of vascular bundles were evaluated at the single-plant level, which provided an insight into the water use efficiency of the maize plant.展开更多
Segmentation of three-dimensional(3D)point clouds is fundamental in comprehending unstructured structural and morphological data.It plays a critical role in research related to plant phenomics,3D plant modeling,and fu...Segmentation of three-dimensional(3D)point clouds is fundamental in comprehending unstructured structural and morphological data.It plays a critical role in research related to plant phenomics,3D plant modeling,and functional-structural plant modeling.Although technologies for plant point cloud segmentation(PPCS)have advanced rapidly,there has been a lack of a systematic overview of the development process.This paper presents an overview of the progress made in 3D point cloud segmentation research in plants.It starts by discussing the methods used to acquire point clouds in plants,and analyzes the impact of point cloud resolution and quality on the segmentation task.It then introduces multi-scale point cloud segmentation in plants.The paper summarizes and analyzes traditional methods for PPCS,including the global and local features.This paper discusses the progress of machine learning-based segmentation on plant point clouds through supervised,unsupervised,and integrated approaches.It also summarizes the datasets that for PPCS using deep learning-oriented methods and explains the advantages and disadvantages of deep learning-based methods for projection-based,voxel-based,and point-based approaches respectively.Finally,the development of PPCS is discussed and prospected.Deep learning methods are predicted to become dominant in the field of PPCS,and 3D point cloud segmentation would develop towards more automated with higher resolution and precision.展开更多
Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture analysis.However,the complex internal structure of maize kernels presents several challenges.In CT(computed tomog...Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture analysis.However,the complex internal structure of maize kernels presents several challenges.In CT(computed tomog-raphy)images,the pixel intensity differences between the vitreous and starchy endosperm regions in maize kernel CT images are not distinct,potentially leading to low segmentation accuracy or oversegmentation.Moreover,the blurred edges between the vitreous and starchy endosperm make segmentation difficult,often resulting in jagged segmentation outcomes.We propose a deep learning-based CT image analysis pipeline to examine the internal structure of maize seeds.First,CT images are acquired using a multislice CT scanner.To improve the efficiency of maize kernel CT imaging,a batch scanning method is used.Individual kernels are accurately segmented from batch-scanned CT images using the Canny algorithm.Second,we modify the conventional architecture for high-quality segmentation of the vitreous and starchy endosperm in maize kernels.The conventional U-Net is modified by integrating the CBAM(convolutional block attention module)mechanism in the encoder and the SE(squeeze-and-excitation attention)mechanism in the decoder,as well as by using the focal-Tversky loss function instead of the Dice loss,and the boundary smoothing term is weighted as an additional loss term,named CSFTU-Net.The experimental results show that the CSFTU-Net model significantly improves the ability of segmenting vitreous and starchy endosperm.Finally,a segmented mask-based method is proposed to extract phenotype parameters of maize kernel texture,including the volume of the kernel(V),volume of the vitreous endosperm(VV),volume of starchy endosperm(SV),and ratios over their respective total kernel volumes(W/V and SV/V).The proposed pipeline facilitates the nondestructive quantification of the internal structure of maize kernels,offering valuable insights for maize breeding and processing.展开更多
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 overla...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.展开更多
Marked variations in the 3-dimensional(3D)shape of corn leaves can be discerned as a function of various influences,including genetics,environmental factors,and the management of cultivation processes.However,the caus...Marked variations in the 3-dimensional(3D)shape of corn leaves can be discerned as a function of various influences,including genetics,environmental factors,and the management of cultivation processes.However,the causes of these variations remain unclear,primarily due to the absence of quantitative methods to describe the 3D spatial morphology of leaves.To address this issue,this study acquired 3D digitized data of ear-position leaves from 478 corn inbred lines during the grain-filling stage.We propose quantitative calculation methods for 13 3D leaf shape features,such as the leaf length,3D leaf area,leaf inclination angle,blade-included angle,blade self-twisting,blade planarity,and margin amplitude.Correlation analysis,cluster analysis,and heritability analysis were conducted among the 13 leaf traits.Leaf morphology differences among subpopulations of the inbred lines were also analyzed.The results revealed that the 3D leaf traits are capable of revealing the morphological differences among different leaf surfaces,and the genetic analysis revealed that 84.62%of the 3D phenotypic traits of ear-position leaves had a heritability greater than 0.3.However,the majority of 3D leaf shape traits were strongly affected by environmental conditions.Overall,this study quantitatively investigated 3D leaf shape in corn,providing a reliable basis for further research on the genetic regulation of corn leaf morphology and advancing the understanding of the complex interplay among crop genetics,phenotypes,and the environment.展开更多
The tassel state in maize hybridization fields not only reflects the growth stage of the maize but also reflects the performance of the detasseling operation.Existing tassel detection models are primarily used to iden...The tassel state in maize hybridization fields not only reflects the growth stage of the maize but also reflects the performance of the detasseling operation.Existing tassel detection models are primarily used to identify mature tassels with obvious features,making it difficult to accurately identify small tassels or detasseled plants.This study presents a novel approach that utilizes unmanned aerial vehicles(UAVs)and deep learning techniques to accurately identify and assess tassel states,before and after manually detasseling in maize hybridization fields.The proposed method suggests that a specific tassel annotation and data augmentation strategy is valuable for substantial enhancing the quality of the tassel training data.This study also evaluates mainstream object detection models and proposes a series of highly accurate tassel detection models based on tassel categories with strong data adaptability.In addition,a strategy for blocking large UAV images,as well as improving tassel detection accuracy,is proposed to balance UAV image acquisition and computational cost.The experimental results demonstrate that the proposed method can accurately identify and classify tassels at various stages of detasseling.The tassel detection model optimized with the enhanced data achieves an average precision of 94.5%across all categories.An optimal model combination that uses blocking strategies for different development stages can improve the tassel detection accuracy to 98%.This could be useful in addressing the issue of missed tassel detections in maize hybridization fields.The data annotation strategy and image blocking strategy may also have broad applications in object detection and recognition in other agricultural scenarios.展开更多
Accurate structural phenotyping analysis is essential to understand plant architectural adaptation strategy to environment change.The aim of this study was to analyze leaf arrangement and geometry influenced by azimut...Accurate structural phenotyping analysis is essential to understand plant architectural adaptation strategy to environment change.The aim of this study was to analyze leaf arrangement and geometry influenced by azimuthally generated light gradient;and to simulate static and heterogeneous cucumber canopies using regression equations by considering more geometric parameters.Three continuous measurements of structural organ parameters were obtained to fit the organ initiation and expansion curves.Four measurements with three density treatments were obtained to validate model accuracy.To describe leaf distribution and orientation characteristics in more detail,azimuth and elevation models were introduced into canopy structure modelling.Leaf distribution frequency was simulated based on leaf area index and solar elevation angle while leaf elevation was simulated based on leaf azimuth and acropetal phytomer number.This study provides an important basis for structural phenotyping analysis of cucumber canopy,which is essential for more accurate functional-structural modelling in the future.展开更多
Agronomic traits in maize(Zea mays L.)are complex and modulated by pleiotropic loci and interconnected genetic networks.However,the traditional single-trait genome-wide association study(GWAS)method often misses genet...Agronomic traits in maize(Zea mays L.)are complex and modulated by pleiotropic loci and interconnected genetic networks.However,the traditional single-trait genome-wide association study(GWAS)method often misses genetic associations among traits,overlooks pleiotropic effects,and underestimates shared regulatory mechanisms.In the current study,we employed multi-trait analysis of GWAS(MTAG)and constructed a genetic network to dissect the genetic architecture of 18 agronomic traits across a genetically diverse panel of 2,448 maize inbred lines.Incorporating MTAG significantly improved the detection of pleiotropic loci that had not been detected by single-trait GWAS.Using a genetic network,we uncovered numerous previously unrecognized connections among traits related to plant architecture,yield,and flowering time.The 49 detected hub nodes,including Zm00001d028840 and Zm00001d033859(knotted1),influence multiple traits.Co-expression analysis of candidate genes across two developmental stages validated their distinct yet complementary roles,with Zm00001d028840 linked to early cell wall remodeling and Zm00001d033849 involved in chromatin remodeling during tasseling.Moreover,we integrated results from GWAS,MTAG,and genetic network analyses to prioritize pleiotropic loci and hub genes that regulate multiple agronomic traits.This integrative approach offers a practical framework for selecting stable,multi-trait-associated targets,thereby supporting more precise and efficient crop improvement strategies.展开更多
Plant phenotyping technologies play important roles in plant research and agriculture.Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to anal...Plant phenotyping technologies play important roles in plant research and agriculture.Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation.Accordingly,high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed,and MVS-Pheno,a portable and low-cost phenotyping platform for individual plants,was developed.The platform is composed of four major components:a semiautomatic multiview stereo(MVS)image acquisition device,a data acquisition console,data processing and phenotype extraction software for maize shoots,and a data management system.The platform’s device is detachable and adjustable according to the size of the target shoot.Image sequences for each maize shoot can be captured within 60-120 seconds,yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software,and the phenotypic traits at the organ and individual plant levels are then extracted by the software.The correlation coefficient(R^(2))between the extracted and manually measured plant height,leaf width,and leaf area values are 0.99,0.87,and 0.93,respectively.A data management system has also been developed to store and manage the acquired raw data,reconstructed point clouds,agronomic information,and resulting phenotypic traits.The platform offers an optional solution for high-throughput phenotyping of field-grown plants,which is especially useful for large populations or experiments across many different ecological regions.展开更多
As a globally popular leafy vegetable and a representative plant of the Asteraceae family,lettuce has great economic and academic significance.In the last decade,high-throughput sequencing,phenotyping,and other multi-...As a globally popular leafy vegetable and a representative plant of the Asteraceae family,lettuce has great economic and academic significance.In the last decade,high-throughput sequencing,phenotyping,and other multi-omics data in lettuce have accumulated on a large scale,thus increasing the demand for an integrative lettuce database.Here,we report the establishment of a comprehensive lettuce database,LettuceGDB(https://www.lettucegdb.com/).As an omics data hub,the current LettuceGDB includes two reference genomes with detailed annotations;re-sequencing data from over 1000 lettuce varieties;a collection of more than 1300 worldwide germplasms and millions of accompanying phenotypic records obtained with manual and cutting-edge phenomics technologies;re-analyses of 256 RNA sequencing datasets;a complete miRNAome;extensive metabolite information for representative varieties and wild relatives;epigenetic data on the genome-wide chromatin accessibility landscape;and various lettuce research papers published in the last decade.Five hierarchically accessible functions(Genome,Genotype,Germplasm,Phenotype,and O-Omics)have been developed with a user-friendly interface to enable convenient data access.Eight built-in tools(Assembly Converter,Search Gene,BLAST,JBrowse,Primer Design,Gene Annotation,Tissue Expression,Literature,and Data)are available for data downloading and browsing,functional gene exploration,and experimental practice.A community forum is also available for information sharing,and a summary of current research progress on different aspects of lettuce is included.We believe that LettuceGDB can be a comprehensive functional database amenable to data mining and database-driven exploration,useful for both scientific research and lettuce breeding.展开更多
Dynamic virtual plant simulation is an attractive research issue in both botany and computer graphics.Data-driven method is an efficient way for motion analysis and animation synthesis.As a widely used tool,motion cap...Dynamic virtual plant simulation is an attractive research issue in both botany and computer graphics.Data-driven method is an efficient way for motion analysis and animation synthesis.As a widely used tool,motion capture has been used in plant motion data acquisition and analysis.The most prominent and important problem in motion capture for plants is primary data processing such as missing markers reconstruction.This paper presents a novel physics-based approach to motion capture data processing of plants.Firstly,a physics-based mechanics model is found by Lagrangian mechanics for a motion captured plant organ such as a leaf,and then its dynamic mechanical properties are analyzed and relevant model parameters are evaluated.Further,by using the physical model with evaluated parameters,we can calculate the next positions of a maker to reconstruct the missing makers in motion capture sequence.We take an example of a maize leaf and pachira leaf to examine the proposed approach,and the results show that the physics-based method is feasible and effective for plant motion data processing.展开更多
This paper presents a general 3D method to simulate a rotting process in fruits using a visual model for the digital design of the fruits.The global rot parameter and rot resistance parameter are used to control a dyn...This paper presents a general 3D method to simulate a rotting process in fruits using a visual model for the digital design of the fruits.The global rot parameter and rot resistance parameter are used to control a dynamic simulation of a rotting process.The rot resistance parameters of every point of a 3D fruit model are generated by an interactive designing method that is similar to the traditional sketch drawing tools.We construct a texture of a rot region on a fruit surface by resistance parameters.The degree of rot that is used to control both shape and appearance of rotten fruit surface can be computed by tuning the resistance parameters and global rot parameters.We derive an exponential function to calculate the depression displacement of geometric shape caused by the rot.In order to render a wrinkle on the rot region,we use a normal noise map to modify a normal vector of fruit model and use an isotropic ward BRDF model to represent an appearance of fruit in which the time-varying diffuse reflectance is derived from the real photos.We utilize a linear function to control the dynamic simulation processes including shape deformation and aging appearance.We have evaluated our method by simulating the rotten apple and moldy orange.The results have shown that our method provides a dynamic,real-time and realistic simulation,and it is flexible,fast and of a general character for digital fruit design as a visualization model.展开更多
The radiation use efficiency(RUE)is one of the most important functional traits determining crop productivity.The coordination of the vertical distribution of light and leaf nitrogen has been proven to be effective in...The radiation use efficiency(RUE)is one of the most important functional traits determining crop productivity.The coordination of the vertical distribution of light and leaf nitrogen has been proven to be effective in boosting the RUE from both experimental and computational evidence.However,previous simulation studies have primarily assumed that the leaf area is uniformly distributed along the canopy depth,rarely considering the optimization of the leaf area distribution,especially for C4 crops.The present study hypothesizes that the RUE may be maximized by matching the leaf area and leaf nitrogen vertical distributions in the canopy.To test this hypothesis,various virtual maize canopies were generated by combining the leaf inclination angle,vertical leaf area distribution,and vertical leaf nitrogen distribution and were further evaluated by an improved multilayer canopy photosynthesis model.We found that a greater fraction of leaf nitrogen is preferentially allocated to canopy layers with greater leaf areas to maximize the RUE.The coordination of light and nitrogen emerged as a property from the simulations to maximize the RUE in most scenarios,particularly in dense canopies.This study not only facilitates explicit and precise profiling of ideotypes for maximizing the RUE but also represents a primary step toward high-throughput phenotyping and screening of the RUE for massive numbers of inbred lines and cultivars.展开更多
基金supported by the National Key R&D Program of China(2022YFD2002300)the Construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)+1 种基金the National Natural Science Foundation of China(32071891)the earmarked fund(CARS-02 and CARS-054).
文摘Crop phenomics has rapidly progressed in recent years due to the growing need for crop functional geno-mics,digital breeding,and smart cultivation.Despite this advancement,the lack of standards for the cre-ation and usage of crop phenomics technology and equipment has become a bottleneck,limiting the industry’s high-quality development.This paper begins with an overview of the crop phenotyping indus-try and presents an industrial mapping of technology and equipment for big data in crop phenomics.It analyzes the necessity and current state of constructing a standard framework for crop phenotyping.Furthermore,this paper proposes the intended organizational structure and goals of the standard frame-work.It details the essentials of the standard framework in the research and development of hardware and equipment,data acquisition,and the storage and management of crop phenotyping data.Finally,it discusses promoting the construction and evaluation of the standard framework,aiming to provide ideas for developing a high-quality standard framework for crop phenotyping.
基金funded by the National Nature Science Foundation of China (31671577)the Natural Science Foundation of Beijing, China (5174033)+2 种基金the Scientific and Technological Innovation Capacity Construction Project of Beijing Academy of Agricultural and Forestry Sciences, China (KJCX20170404)the Scientific and Technological Innovation Team of Beijing Academy of Agricultural and Forestry Sciences, China (JNKYT201604)the Beijing Postdoctoral Research Foundation, China (2016 ZZ-66)
文摘Maize(Zea mays L.) yield depends not only on the conversion and accumulation of carbohydrates in kernels, but also on the supply of carbohydrates by leaves. However, the carbon metabolism process in leaves can vary across different leaf regions and during the day and night. Hence, we used Weighted Gene Co-expression Network analysis(WGCNA) with the gene expression profiles of carbon metabolism to identify the modules and genes that may associate with particular regions in a leaf and time of day. There were a total of 45 samples of maize leaves that were taken from three different regions of a growing maize leaf at five time points. Robust Multi-array Average analysis was used to pre-process the raw data of GSE85963(accession number), and quality control of data was based on Pearson correlation coefficients. We obtained eight co-expression network modules. The modules with the highest significance of association with LeafRegion and TimePoint were selected. Functional enrichment and gene-gene interaction analyses were conducted to acquire the hub genes and pathways in these significant modules. These results can support the findings of similar studies by providing evidence of potential module genes and enriched pathways associated with leaf development in maize.
基金supported by the Construction of Collaborative Innovation Center of Beijing Academy of Agriculture and Forestry Science (KJCX201917)Beijing Academy of Agriculture and Forestry Sciences Grants (QNJJ202124)+1 种基金the National Natural Science Foundation of China (31801254 and U21A20205)Beijing Natural Science Foundation (5202018)。
文摘Plant vascular bundles are responsible for water and material transportation, and their quantitative and functional evaluation is desirable in plant research. At the single-plant level, the number, size, and distribution of vascular bundles vary widely, posing a challenge to automatically and accurately identifying and quantifying them. In this study, a deep learning-integrated phenotyping pipeline was developed to robustly and accurately detect vascular bundles in Computed Tomography(CT) images of stem internodes. Two semantic indicators were used to evaluate and identify a suitable feature extraction network for semantic segmentation models. The epidermis thickness of maize stem was evaluated for the first time and adjacent vascular bundles were improved using an adaptive watershed-based approach. The counting accuracy(R^(2)) of vascular bundles was 0.997 for all types of stem internodes, and the measured accuracy of size traits was over 0.98. Combining sap flow experiments, multiscale traits of vascular bundles were evaluated at the single-plant level, which provided an insight into the water use efficiency of the maize plant.
基金supported by National Key Research and Development Program of China(2022YFD2001003)the National Natural Science Foundation of China(32071891)+1 种基金Science and Technology Innovation Special Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences(KJCX20220401)the earmarked fund(CARS-02 and CARS-54).
文摘Segmentation of three-dimensional(3D)point clouds is fundamental in comprehending unstructured structural and morphological data.It plays a critical role in research related to plant phenomics,3D plant modeling,and functional-structural plant modeling.Although technologies for plant point cloud segmentation(PPCS)have advanced rapidly,there has been a lack of a systematic overview of the development process.This paper presents an overview of the progress made in 3D point cloud segmentation research in plants.It starts by discussing the methods used to acquire point clouds in plants,and analyzes the impact of point cloud resolution and quality on the segmentation task.It then introduces multi-scale point cloud segmentation in plants.The paper summarizes and analyzes traditional methods for PPCS,including the global and local features.This paper discusses the progress of machine learning-based segmentation on plant point clouds through supervised,unsupervised,and integrated approaches.It also summarizes the datasets that for PPCS using deep learning-oriented methods and explains the advantages and disadvantages of deep learning-based methods for projection-based,voxel-based,and point-based approaches respectively.Finally,the development of PPCS is discussed and prospected.Deep learning methods are predicted to become dominant in the field of PPCS,and 3D point cloud segmentation would develop towards more automated with higher resolution and precision.
基金supported by the National Key Research and Development Program(2021YFD1200705)the Collaborative Innovation Center of the Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)the Science and Technology Innovation Special Construction Funded Program of the Beijing Academy of Agriculture and Forestry Sciences(KJCX20220401).
文摘Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture analysis.However,the complex internal structure of maize kernels presents several challenges.In CT(computed tomog-raphy)images,the pixel intensity differences between the vitreous and starchy endosperm regions in maize kernel CT images are not distinct,potentially leading to low segmentation accuracy or oversegmentation.Moreover,the blurred edges between the vitreous and starchy endosperm make segmentation difficult,often resulting in jagged segmentation outcomes.We propose a deep learning-based CT image analysis pipeline to examine the internal structure of maize seeds.First,CT images are acquired using a multislice CT scanner.To improve the efficiency of maize kernel CT imaging,a batch scanning method is used.Individual kernels are accurately segmented from batch-scanned CT images using the Canny algorithm.Second,we modify the conventional architecture for high-quality segmentation of the vitreous and starchy endosperm in maize kernels.The conventional U-Net is modified by integrating the CBAM(convolutional block attention module)mechanism in the encoder and the SE(squeeze-and-excitation attention)mechanism in the decoder,as well as by using the focal-Tversky loss function instead of the Dice loss,and the boundary smoothing term is weighted as an additional loss term,named CSFTU-Net.The experimental results show that the CSFTU-Net model significantly improves the ability of segmenting vitreous and starchy endosperm.Finally,a segmented mask-based method is proposed to extract phenotype parameters of maize kernel texture,including the volume of the kernel(V),volume of the vitreous endosperm(VV),volume of starchy endosperm(SV),and ratios over their respective total kernel volumes(W/V and SV/V).The proposed pipeline facilitates the nondestructive quantification of the internal structure of maize kernels,offering valuable insights for maize breeding and processing.
基金partially supported by the National Science and Technology Major Project(2022ZD0115705)the National Natural Science Foundation of China(32071891)+2 种基金the Science and Technology Innovation Special Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences(KJCX20220401)the China Postdoctoral Science Foundation(2023M730314)the earmarked fund(CARS-02 and CARS-054).
文摘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.
基金partially supported by the National Key R&D Program of China(2021YFD1200700)the Construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)+2 种基金the Science and Technology Innovation SpecialConstruction Funded Prog-ram of Beijing Academy of Agriculture and Forestry Sciences(KJCX20220401)the National Natural Science Foundation of China(32071891)the Earmarked Fund(CARS-02 and CARS-54).
文摘Marked variations in the 3-dimensional(3D)shape of corn leaves can be discerned as a function of various influences,including genetics,environmental factors,and the management of cultivation processes.However,the causes of these variations remain unclear,primarily due to the absence of quantitative methods to describe the 3D spatial morphology of leaves.To address this issue,this study acquired 3D digitized data of ear-position leaves from 478 corn inbred lines during the grain-filling stage.We propose quantitative calculation methods for 13 3D leaf shape features,such as the leaf length,3D leaf area,leaf inclination angle,blade-included angle,blade self-twisting,blade planarity,and margin amplitude.Correlation analysis,cluster analysis,and heritability analysis were conducted among the 13 leaf traits.Leaf morphology differences among subpopulations of the inbred lines were also analyzed.The results revealed that the 3D leaf traits are capable of revealing the morphological differences among different leaf surfaces,and the genetic analysis revealed that 84.62%of the 3D phenotypic traits of ear-position leaves had a heritability greater than 0.3.However,the majority of 3D leaf shape traits were strongly affected by environmental conditions.Overall,this study quantitatively investigated 3D leaf shape in corn,providing a reliable basis for further research on the genetic regulation of corn leaf morphology and advancing the understanding of the complex interplay among crop genetics,phenotypes,and the environment.
基金supported by the National Key Research and Development Program(2022YFD1900701)the Heilongjiang Province“Enlisting and Leading”Science and Technology Research Projects(20212XJ05A02)+1 种基金the Construction of Colaborative Innovation Center of Beijing Academy of Agriculture and Forestry Science(KJCX20230429)the National Natural Science Foundation of China(U21A20205).
文摘The tassel state in maize hybridization fields not only reflects the growth stage of the maize but also reflects the performance of the detasseling operation.Existing tassel detection models are primarily used to identify mature tassels with obvious features,making it difficult to accurately identify small tassels or detasseled plants.This study presents a novel approach that utilizes unmanned aerial vehicles(UAVs)and deep learning techniques to accurately identify and assess tassel states,before and after manually detasseling in maize hybridization fields.The proposed method suggests that a specific tassel annotation and data augmentation strategy is valuable for substantial enhancing the quality of the tassel training data.This study also evaluates mainstream object detection models and proposes a series of highly accurate tassel detection models based on tassel categories with strong data adaptability.In addition,a strategy for blocking large UAV images,as well as improving tassel detection accuracy,is proposed to balance UAV image acquisition and computational cost.The experimental results demonstrate that the proposed method can accurately identify and classify tassels at various stages of detasseling.The tassel detection model optimized with the enhanced data achieves an average precision of 94.5%across all categories.An optimal model combination that uses blocking strategies for different development stages can improve the tassel detection accuracy to 98%.This could be useful in addressing the issue of missed tassel detections in maize hybridization fields.The data annotation strategy and image blocking strategy may also have broad applications in object detection and recognition in other agricultural scenarios.
基金This work was supported by National Natural Science Foundation of China(No.61762013)Shanghai Agriculture Applied Technology Development Program,China(Grant No.G2015060402)Basic Ability Improvement Project for Young and middle-aged teachers in universities of Guangxi province(No.2017KY0075).
文摘Accurate structural phenotyping analysis is essential to understand plant architectural adaptation strategy to environment change.The aim of this study was to analyze leaf arrangement and geometry influenced by azimuthally generated light gradient;and to simulate static and heterogeneous cucumber canopies using regression equations by considering more geometric parameters.Three continuous measurements of structural organ parameters were obtained to fit the organ initiation and expansion curves.Four measurements with three density treatments were obtained to validate model accuracy.To describe leaf distribution and orientation characteristics in more detail,azimuth and elevation models were introduced into canopy structure modelling.Leaf distribution frequency was simulated based on leaf area index and solar elevation angle while leaf elevation was simulated based on leaf azimuth and acropetal phytomer number.This study provides an important basis for structural phenotyping analysis of cucumber canopy,which is essential for more accurate functional-structural modelling in the future.
基金supported by the National Key R&D Program of China(2022ZD0115703)the Hainan Provincial Natural Science Foundation of China(725QN518)+1 种基金the project of Sanya Yazhou Bay Science and Technology City(SKIC-JYRC-2024-55)the Agricultural Science and Technology Innovation Program(CAAS-CSIAF-202303).
文摘Agronomic traits in maize(Zea mays L.)are complex and modulated by pleiotropic loci and interconnected genetic networks.However,the traditional single-trait genome-wide association study(GWAS)method often misses genetic associations among traits,overlooks pleiotropic effects,and underestimates shared regulatory mechanisms.In the current study,we employed multi-trait analysis of GWAS(MTAG)and constructed a genetic network to dissect the genetic architecture of 18 agronomic traits across a genetically diverse panel of 2,448 maize inbred lines.Incorporating MTAG significantly improved the detection of pleiotropic loci that had not been detected by single-trait GWAS.Using a genetic network,we uncovered numerous previously unrecognized connections among traits related to plant architecture,yield,and flowering time.The 49 detected hub nodes,including Zm00001d028840 and Zm00001d033859(knotted1),influence multiple traits.Co-expression analysis of candidate genes across two developmental stages validated their distinct yet complementary roles,with Zm00001d028840 linked to early cell wall remodeling and Zm00001d033849 involved in chromatin remodeling during tasseling.Moreover,we integrated results from GWAS,MTAG,and genetic network analyses to prioritize pleiotropic loci and hub genes that regulate multiple agronomic traits.This integrative approach offers a practical framework for selecting stable,multi-trait-associated targets,thereby supporting more precise and efficient crop improvement strategies.
基金This research was funded by the Construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences(KJCX201917)the National Natural Science Foundation of China(31871519 and 31601215)+1 种基金the Modern Agro-Industry Technology Research System of Maize(CARS-02-87)the Construction of Scientific Research and Innovation Platform in Beijing Academy of Agricultural and Forestry Sciences(Digital Plant).
文摘Plant phenotyping technologies play important roles in plant research and agriculture.Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation.Accordingly,high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed,and MVS-Pheno,a portable and low-cost phenotyping platform for individual plants,was developed.The platform is composed of four major components:a semiautomatic multiview stereo(MVS)image acquisition device,a data acquisition console,data processing and phenotype extraction software for maize shoots,and a data management system.The platform’s device is detachable and adjustable according to the size of the target shoot.Image sequences for each maize shoot can be captured within 60-120 seconds,yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software,and the phenotypic traits at the organ and individual plant levels are then extracted by the software.The correlation coefficient(R^(2))between the extracted and manually measured plant height,leaf width,and leaf area values are 0.99,0.87,and 0.93,respectively.A data management system has also been developed to store and manage the acquired raw data,reconstructed point clouds,agronomic information,and resulting phenotypic traits.The platform offers an optional solution for high-throughput phenotyping of field-grown plants,which is especially useful for large populations or experiments across many different ecological regions.
基金supported by the Beijing Academy of Agriculture and Forestry Sciences(KJCX201907-2 to J.W.,KJCX201917 to C.Z.,and KJCX20200204 and KJCX20220105 to X.Y.)the Beijing Postdoctoral Research Foundation(2021-ZZ-133 to B.L.)the National Natural Science Foundation of China(31621001 to X.Y.).
文摘As a globally popular leafy vegetable and a representative plant of the Asteraceae family,lettuce has great economic and academic significance.In the last decade,high-throughput sequencing,phenotyping,and other multi-omics data in lettuce have accumulated on a large scale,thus increasing the demand for an integrative lettuce database.Here,we report the establishment of a comprehensive lettuce database,LettuceGDB(https://www.lettucegdb.com/).As an omics data hub,the current LettuceGDB includes two reference genomes with detailed annotations;re-sequencing data from over 1000 lettuce varieties;a collection of more than 1300 worldwide germplasms and millions of accompanying phenotypic records obtained with manual and cutting-edge phenomics technologies;re-analyses of 256 RNA sequencing datasets;a complete miRNAome;extensive metabolite information for representative varieties and wild relatives;epigenetic data on the genome-wide chromatin accessibility landscape;and various lettuce research papers published in the last decade.Five hierarchically accessible functions(Genome,Genotype,Germplasm,Phenotype,and O-Omics)have been developed with a user-friendly interface to enable convenient data access.Eight built-in tools(Assembly Converter,Search Gene,BLAST,JBrowse,Primer Design,Gene Annotation,Tissue Expression,Literature,and Data)are available for data downloading and browsing,functional gene exploration,and experimental practice.A community forum is also available for information sharing,and a summary of current research progress on different aspects of lettuce is included.We believe that LettuceGDB can be a comprehensive functional database amenable to data mining and database-driven exploration,useful for both scientific research and lettuce breeding.
基金National Natural Science Foundation of China(Grant No.61300079).
文摘Dynamic virtual plant simulation is an attractive research issue in both botany and computer graphics.Data-driven method is an efficient way for motion analysis and animation synthesis.As a widely used tool,motion capture has been used in plant motion data acquisition and analysis.The most prominent and important problem in motion capture for plants is primary data processing such as missing markers reconstruction.This paper presents a novel physics-based approach to motion capture data processing of plants.Firstly,a physics-based mechanics model is found by Lagrangian mechanics for a motion captured plant organ such as a leaf,and then its dynamic mechanical properties are analyzed and relevant model parameters are evaluated.Further,by using the physical model with evaluated parameters,we can calculate the next positions of a maker to reconstruct the missing makers in motion capture sequence.We take an example of a maize leaf and pachira leaf to examine the proposed approach,and the results show that the physics-based method is feasible and effective for plant motion data processing.
基金Beijing Postdoctoral Research Foundation(Grant No.4162028)by National Natural Science Foundation of China(Grant No.61300079)Beijing Municipal Natural Science Foundation(Grant No.4162028).
文摘This paper presents a general 3D method to simulate a rotting process in fruits using a visual model for the digital design of the fruits.The global rot parameter and rot resistance parameter are used to control a dynamic simulation of a rotting process.The rot resistance parameters of every point of a 3D fruit model are generated by an interactive designing method that is similar to the traditional sketch drawing tools.We construct a texture of a rot region on a fruit surface by resistance parameters.The degree of rot that is used to control both shape and appearance of rotten fruit surface can be computed by tuning the resistance parameters and global rot parameters.We derive an exponential function to calculate the depression displacement of geometric shape caused by the rot.In order to render a wrinkle on the rot region,we use a normal noise map to modify a normal vector of fruit model and use an isotropic ward BRDF model to represent an appearance of fruit in which the time-varying diffuse reflectance is derived from the real photos.We utilize a linear function to control the dynamic simulation processes including shape deformation and aging appearance.We have evaluated our method by simulating the rotten apple and moldy orange.The results have shown that our method provides a dynamic,real-time and realistic simulation,and it is flexible,fast and of a general character for digital fruit design as a visualization model.
基金supported by the National Key R&D Program of China(2022YFD2001003)the National Natural Science Foundation of China(32330075 and 32001420)+2 种基金the Science and Technology Innovation Special Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences(KJCX20220401)the Young Elite Scientist Sponsorship Program by BAST(no.BYESS2023204)the earmarked fund for CARS-02 and CARS-54.
文摘The radiation use efficiency(RUE)is one of the most important functional traits determining crop productivity.The coordination of the vertical distribution of light and leaf nitrogen has been proven to be effective in boosting the RUE from both experimental and computational evidence.However,previous simulation studies have primarily assumed that the leaf area is uniformly distributed along the canopy depth,rarely considering the optimization of the leaf area distribution,especially for C4 crops.The present study hypothesizes that the RUE may be maximized by matching the leaf area and leaf nitrogen vertical distributions in the canopy.To test this hypothesis,various virtual maize canopies were generated by combining the leaf inclination angle,vertical leaf area distribution,and vertical leaf nitrogen distribution and were further evaluated by an improved multilayer canopy photosynthesis model.We found that a greater fraction of leaf nitrogen is preferentially allocated to canopy layers with greater leaf areas to maximize the RUE.The coordination of light and nitrogen emerged as a property from the simulations to maximize the RUE in most scenarios,particularly in dense canopies.This study not only facilitates explicit and precise profiling of ideotypes for maximizing the RUE but also represents a primary step toward high-throughput phenotyping and screening of the RUE for massive numbers of inbred lines and cultivars.
