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high-throughput phenotyping identifies plant growth differences under well-watered and drought treatments

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Journal of Integrative Agriculture 2020年第10期19卷 2429-2438页

作者： Seth TOLLEY Yang YANG Mohsen MOHAMMADI Department of Agronomy Purdue UniversityWest LafayetteIN 47907United States College of Agricuture Department of AgronomyPurdue UniversityWest LafayetteIN 47907United States

The ability to screen larger populations with fewer replicates and non-destructive measurements is one advantage of high-throughput phenotyping(HTP)over traditinal phenotyping *** this study,two wheat accessions were grown in a controlled-environment with a moderate drought imposed from stem elongation to ***-green-blue(RGB)imaging was performed on 17 of the 22 d following the start of drought *** measurements from all plants were performed at the conclusion of the *** effect of line was signifcant for shoot dry matter,spike dry matter,root dry matter,and tller number,while the water treatment was significant on shoot dry matter and root dry *** temporal,non-destructive nature of HTP allowed the drought treatment to be significantly differentiated from the well-watered treatment after 6 d in a line from Argentina and 9 d in a line from *** difference of 3 d indicated an increased degree of drought tolerance in the line from ***,HTP from the final day of imaging accurately predicted reference plant height(r=1),shoot dry matter(r=0.95)and tller number(r=0.91).This experiment ilustrates the potential of HTP and its use in modeling plant growth and development.

关键词： high-throughput phenotyping drought controlled-environment wheat

high-throughput phenotyping and Genomic Selection:The Frontiers of Crop Breeding Converge

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Journal of Integrative Plant Biology 2012年第5期54卷 312-320页

作者： Lloren Cabrera-Bosquet Jos Crossa Jarislav von Zitzewitz María Dolors Serret Jos Luis Araus French National Institute for Agricultural Research (INRA UMR759)Ecophysiology Laboratory of Plants under Environmental StressMontpellier F-34060France International Maize and Wheat Improvement Center (CIMMYT) El BatnTexcoco CP 56130Mexico National Research Program on Rainfed Crops National Institute for Agricultural ResearchEst.Exp.La EstanzuelaColonia 70000Uruguay Unit of Plant Physiology Faculty of BiologyUniversity of BarcelonaAv.Diagonal643Barcelona 08028Spain

Genomic selection （GS） and high-throughput phenotyping have recently been captivating the interest of the crop breeding com- munity from both the public and private sectors world-wide. Both approaches promise to revolutionize the prediction of complex traits, including growth, yield and adaptation to stress. Whereas high-throughput phenotyping may help to improve understanding of crop physiology, most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and compa- rable to genomic selection. Despite the fact that the two method- ological approaches represent the extremes of what is understood as the breeding process （phenotype versus genome）, they both consider the targeted traits （e.g. grain yield, growth, phenology, plant adaptation to stress） as a black box instead of dissectingthem as a set of secondary traits （i.e. physiological） putatively related to the target trait. Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology. This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy （NIRS） and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.

关键词： Genomic selection high-throughput phenotyping NIRS quantitative traits SNPs.

high-throughput phenotyping:A Platform to Accelerate Crop Improvement

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Phenomics 2021年第2期1卷 31-53页

作者： Sumit Jangra Vrantika Chaudhary Ram C.Yadav Neelam R.Yadav Department of Molecular Biology Biotechnologyand BioinformaticsCCS Haryana Agricultural UniversityHisar 125004India

Development of high-throughput phenotyping technologies has progressed considerably in the last 10 *** technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments;this is a critical step towards selection of better performing lines as to yield,disease resistance,and stress tolerance to accelerate crop improvement ***-throughput phenotyping techniques and platforms help unrave-ling the genetic basis of complex traits associated with plant growth and development and targeted *** review focuses on the advancements in technologies involved in high-throughput,field-based,aerial,and unmanned *** of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques,which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.

关键词： high-throughput phenotyping Imaging Technologies Platform Crop improvement Biotic and abiotic stress

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

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Journal of Integrative Agriculture 2024年第6期23卷 1787-1802页

作者： Mansoor Sheikh Farooq Iqra Hamadani Ambreen Kumar A Pravin Manzoor Ikra Yong Suk Chung Phenomics Laboratory Department of Plant Resources and EnvironmentJeju National UniversityJeju 63234Republic of Korea Council of Scientific&Industrial Research Indian Institute of Integrative MedicinePulwamaJ&K 192301India Animal and Dairy Science University of WisconsinMadisonWI 530706United States of America Division of Fruit Science Faculty of HorticultureSher-e-Kashmir University of Agricultural Sciences and Technology of KashmirSrinagar 190025India

Crop improvement is crucial for addressing the global challenges of food security and sustainable *** advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large *** integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop *** algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic *** technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety ***,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop *** leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop *** aim of this review is to explore the transformative potential of integrating HTP and AI in crop *** review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

关键词： artificial intelligence crop improvement data analysis high-throughput phenotyping machine learning precision agriculture trait selection

Combining the high-throughput maize phenotyping and quantitative trait loci analysis to reveal the dynamic genetic architecture of maize plant growth

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Combining the high-throughput maize phenotyping and quantita...

Sensors for measuring plant phenotyping:A review

2016年全国植物生物学大会

作者： Xuehai Zhang Chenglong Huang Di Wu Feng Qiao Ke Wang Yingjie Xiao Guoxing Chen Lizhong Xiong Wanneng Yang Jianbing Yan National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University National Center of Plant Gene Research (Wuhan) Huazhong Agricultural University College of Engineering Huazhong Agricultural University MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River Huazhong Agricultural University

With the increasing demands in crop breeding for novel traits,plant research community faces to quantitatively analyze the structure and function of large numbers of plants. A clear goal of highthroughput phenotyping is to bridge the gap between genomics and phenomics. In this study,we obtained 106 traits from a maize recombinant inbred line population across 16 development stages using the automatic phenotyping *** results showed that the exponential model had better predication ability for biomass accumulation,even in the early growth stage. QTL mapping with high density genetic linkage map was used to uncover the genetic basis of these complex agronomic traits,and have identified 2265 QTLs for all investigated traits. We also conduct the traits-loci network analysis and 3 house-keeping loci were detected in the hub points for 2 traits. These results reveal the dynamic genetic architecture of maize plant growth thus enhance maize ideotype breeding in the near future.

关键词： maize high-throughput phenotyping dynamic genetic architecture QTL mapping

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International Journal of Agricultural and Biological Engineering 2018年第2期11卷 1-17页

作者： Ruicheng Qiu Shuang Wei Man Zhang Han Li Hong Sun Gang Liu Minzan Li Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of EducationChina Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Information Acquisition Technology Ministry of AgricultureChina Agricultural UniversityBeijing 100083China

Food crisis is a matter of prime importance because it becomes more severe as the global population *** the solutions to this crisis,breeding is deemed one of the most effective ***,traditional phenotyping in breeding is time consuming and laborious,and the database is insufficient to meet the requirements of plant breeders,which hinders the development of ***,innovations in phenotyping are urgent to solve this *** morphometric and physiological parameters of plant are particularly interested to *** sensors have been employed and novel algorithms have been proposed to collect data on such *** paper presents a brief review on the parameter measurement for phenotyping to describe its development in recent *** parameters that have been measured in phenotyping are introduced and discussed,including plant height,leaf parameters,in-plant space,chlorophyll,water stress,and *** the measurement methods of each parameter with different sensors were classified and *** comprehensive measurement platforms were also summarized,which are able to measure several parameters ***,some deficiencies of phenotyping should be addressed,and novel methods should be proposed to reduce cost,improve efficiency,and promote phenotyping in the future.

关键词： plant phenotype high-throughput phenotyping sensor morphometric parameters physiological parameters

Modern phenomics to empower holistic crop science, agronomy, and breeding research

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Journal of Genetics and Genomics 2024年第8期51卷 790-800页

作者： Ni Jiang Xin-Guang Zhu Institute of Genetics and Developmental Biology Chinese Academy of SciencesBeijing 100101China Center of Excellence for Molecular Plant Sciences Chinese Academy of SciencesShanghai 200032China

Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales,representing a greater data collection throughput compared with traditional *** modern crop phenomics use different sensors to collect reflective,emitted,and fluorescence signals,etc.,from plant organs at different spatial and temporal *** multi-modal,high-dimensional data not only accelerates basic research on crop physiology,genetics,and whole plant systems modeling,but also supports the optimization of field agronomic practices,internal environments of plant factories,and ultimately crop *** challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection,management,sharing,and processing,developing capabilities to measure physiological parameters,and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.

关键词： Crop phenomics high-throughput phenotyping Traits Crop designer breeding Agronomy

Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies

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The Crop Journal 2022年第5期10卷 1303-1311页

作者： Lei Li Muhammad Adeel Hassan Shurong Yang Furong Jing Mengjiao Yang Awais Rasheed Jiankang Wang Xianchun Xia Zhonghu He Yonggui Xiao Institute of Crop Sciences National Wheat Improvement CentreChinese Academy of Agricultural Sciences(CAAS)Beijing 100081China Electronic Information School Foshan PolytechnicFoshan 528137GuangdongChina International Maize and Wheat Improvement Centre(CIMMYT)China Office c/o CAASBeijing 100081China Department of Plant Sciences Quaid-i-Azam UniversityIslamabad 44000Pakistan

Spike number(SN) per unit area is one of the major determinants of grain yield in wheat. Development of high-throughput techniques to count SN from large populations enables rapid and cost-effective selection and facilitates genetic studies. In the present study, we used a deep-learning algorithm, i.e., Faster Region-based Convolutional Neural Networks(Faster R-CNN) on Red-Green-Blue(RGB) images to explore the possibility of image-based detection of SN and its application to identify the loci underlying SN. A doubled haploid population of 101 lines derived from the Yangmai 16/Zhongmai 895 cross was grown at two sites for SN phenotyping and genotyped using the high-density wheat 660 K SNP *** of manual spike number(MSN) in the field, image-based spike number(ISN), and verification of spike number(VSN) by Faster R-CNN revealed significant variation(P high heritability ranged from 0.71 to 0.96. The coefficients of determination(R^(2)) between ISN and VSN was 0.83, which was higher than that between ISN and MSN(R^(2)= 0.51), and between VSN and MSN(R^(2)= 0.50). Results showed that VSN data can effectively predict wheat spikes with an average accuracy of 86.7% when validated using MSN data. Three QTL ***-4 DS, ***-7 DS, and ***-7 DL were identified based on MSN, ISN and VSN data, while ***-7 DS was detected in all the three data sets. These results indicate that using Faster R-CNN model for image-based identification of SN per unit area is a precise and rapid phenotyping method, which can be used for genetic studies of SN in wheat.

关键词： Deeping learning high-throughput phenotyping QTL mapping RGB imaging