With the rapid development of genetic analysis techniques and crop population size,phenotyping has become the bottleneck restricting crop breeding.Breaking through this bottleneck will require phenomics,defined as the...With the rapid development of genetic analysis techniques and crop population size,phenotyping has become the bottleneck restricting crop breeding.Breaking through this bottleneck will require phenomics,defined as the accurate,high-throughput acquisition and analysis of multi-dimensional phenotypes during crop growth at organism-wide levels,ranging from cells to organs,individual plants,plots,and fields.Here we offer an overview of crop phenomics research from technological and platform viewpoints at various scales,including microscopic,ground-based,and aerial phenotyping and phenotypic data analysis.We describe recent applications of high-throughput phenotyping platforms for abiotic/biotic stress and yield assessment.Finally,we discuss current challenges and offer perspectives on future phenomics research.展开更多
Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have rev...Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent 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 scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These 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 development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.展开更多
The ability to screen larger populations with fewer replicates and non-destructive measurements is one advantage of high-throughput phenotyping(HTP)over traditinal phenotyping techniques.In this study,two wheat access...The ability to screen larger populations with fewer replicates and non-destructive measurements is one advantage of high-throughput phenotyping(HTP)over traditinal phenotyping techniques.In this study,two wheat accessions were grown in a controlled-environment with a moderate drought imposed from stem elongation to post-anthesis.Red-green-blue(RGB)imaging was performed on 17 of the 22 d following the start of drought imposition.Destructive measurements from all plants were performed at the conclusion of the experiment.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 matter.The 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 Chile.This difference of 3 d indicated an increased degree of drought tolerance in the line from Chile.Furthermore,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.展开更多
Recent technological advances in cotton(Gossypium hirsutum L.) phenotyping have offered tools to improve the efficiency of data collection and analysis.High-throughput phenotyping(HTP) is a non-destructive and rapid a...Recent technological advances in cotton(Gossypium hirsutum L.) phenotyping have offered tools to improve the efficiency of data collection and analysis.High-throughput phenotyping(HTP) is a non-destructive and rapid approach of monitoring and measuring multiple phenotypic traits related to the growth,yield,and adaptation to biotic or abiotic stress.Researchers have conducted extensive experiments on HTP and developed techniques including spectral,fluorescence,thermal,and three-dimensional imaging to measure the morphological,physiological,and pathological resistance traits of cotton.In addition,ground-based and aerial-based platforms were also developed to aid in the implementation of these HTP systems.This review paper highlights the techniques and recent developments for HTP in cotton,reviews the potential applications according to morphological and physiological traits of cotton,and compares the advantages and limitations of these HTP systems when used in cotton cropping systems.Overall,the use of HTP has generated many opportunities to accurately and efficiently measure and analyze diverse traits of cotton.However,because of its relative novelty,HTP has some limitations that constrains the ability to take full advantage of what it can offer.These challenges need to be addressed to increase the accuracy and utility of HTP,which can be done by integrating analytical techniques for big data and continuous advances in imaging.展开更多
High-throughput phenotyping(HTP)technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources.To better understand the status of forage phenotyping research an...High-throughput phenotyping(HTP)technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources.To better understand the status of forage phenotyping research and identify key directions for development,this review summarizes advances in HTP technology for forage phenotypic analysis over the past ten years.This paper reviews the unique aspects and research priorities in forage phenotypic monitoring,highlights key remote sensing platforms,examines the applications of advanced sensing technology for quantifying phenotypic traits,explores artificial intelligence(AI)algorithms in phenotypic data integration and analysis,and assesses recent progress in phenotypic genomics.The practical applications of HTP technology in forage remain constrained by several challenges.These include establishing uniform data collection standards,designing effective algorithms to handle complex genetic and environmental interactions,deepening the cross-exploration of phenomics-genomics,solving the problem of pathological inversion of forage phenotypic growth monitoring models,and developing low-cost forage phenotypic equipment.Resolving these challenges will unlock the full potential of HTP,enabling precise identification of superior forage traits,accelerating the breeding of superior varieties,and ultimately improving forage yield.展开更多
Differences in canopy architecture play a role in determining both the light and water use efficiency.Canopy architecture is determined by several component traits,including leaf length,width,number,angle,and phyl-lot...Differences in canopy architecture play a role in determining both the light and water use efficiency.Canopy architecture is determined by several component traits,including leaf length,width,number,angle,and phyl-lotaxy.Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants.As a result,in simulations of the leaf canopies of grain crops such as maize and sorghum,this trait is frequently approximated as alternating 180°angles between sequential leaves.We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alter-nating phyllotaxy across a diverse set of sorghum genotypes.Using a voxel-carving-based approach,we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum geno-types from the sorghum association panel.The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals.Automated phyllotaxy measurements exhibited a repeatability of R^(2)=0.41 across imaging timepoints separated by a period of two days.A resampling based genome wide association study(GWAS)identified several putative genetic associations with lower-canopy phyllotaxy in sorghum.This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.展开更多
For fast in-situ assessment of tiller phenotypes in rice breeding,we introduce the TillerPET model,an improved transformer-based deep learning solution that permits phenotyping the number and compactness of rice tille...For fast in-situ assessment of tiller phenotypes in rice breeding,we introduce the TillerPET model,an improved transformer-based deep learning solution that permits phenotyping the number and compactness of rice tillers in images of post-harvest rice stubble.A rice tiller phenotype dataset covering three years of field data and four experimental sites across China was constructed to train and validate the model.TillerPET reports an R2 of 0.941 for counting tiller number,demonstrating state-of-the-art performance on the proposed RTP dataset.Beyond its minimal errors in estimating tiller number,TillerPET also achieves an R2 of 0.978 for characterizing tiller compactness.The two phenotypic parameters exhibit a high degree of consistency with expert breeders,offering reliable phenotypic indicators to guide further breeding.展开更多
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 rev...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.展开更多
Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years.These technologies provide precise measurements of desired traits among thousands of field-grown plants under di...Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years.These 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 programs.High-throughput phenotyping techniques and platforms help unrave-ling the genetic basis of complex traits associated with plant growth and development and targeted traits.This review focuses on the advancements in technologies involved in high-throughput,field-based,aerial,and unmanned platforms.Development 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.展开更多
Since whole-genome sequencing of many crops has been achieved,crop functional genomics studies have stepped into the big-data and high-throughput era.However,acquisition of large-scale phenotypic data has become one o...Since whole-genome sequencing of many crops has been achieved,crop functional genomics studies have stepped into the big-data and high-throughput era.However,acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies.Nevertheless,recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years.In this article,we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades.We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies.Finally,we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap.It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.展开更多
Phenomics studies a variety of phenotypic plant traits and is the key to understanding genetic functions and environmental effects on plants. With the rapid development of genomics, many plant phenotyping platforms ha...Phenomics studies a variety of phenotypic plant traits and is the key to understanding genetic functions and environmental effects on plants. With the rapid development of genomics, many plant phenotyping platforms have been developed to study complex traits related to the growth, yield, and adaptation to biotic or abiotic stress, but the ability to acquire high-throughput phenotypic data has become the bottleneck in the study of plant genomics. In recent years, researchers around the world have conducted extensive experiments and research on high-throughput, image-based phenotyping techniques,including visible light imaging, fluorescence imaging,thermal imaging, spectral imaging, stereo imaging, and tomographic imaging. This paper considers imaging technologies developed in recent years for high-throughput phenotyping, reviews applications of these technologies in detecting and measuring plant morphological, physiological, and pathological traits, and compares their advantages and limitations.展开更多
Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serio...Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serious but common diseases and their association with atherosclerotic cardiovascular risk factors establish a vicious circle culminating in high atherogenicity.Because of that,it is of paramount importance to perform risk stratification of patients with prediabetes to define phenotypes that benefit from various interventions.Furthermore,stress hyperglycemia assessment of hospitalized patients and consensus on the definition of prediabetes is vital.The roles lifestyle and metformin play in prediabetes are well established.However,the role of glucagon-like peptide agonists and metabolic surgery is less clear.Prediabetes is considered an intermediate between normoglycemia and diabetes along the blood glucose continuum.One billion people are expected to suffer from prediabetes by the year 2045.Therefore,realworld randomized controlled trials to assess major adverse cardiac or cerebrovascular event risk reduction and reversal/prevention of type 2 diabetes among patients are needed to determine the proper interventions.展开更多
High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine ...High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine max)using unmanned aerial vehicle(UAV)remote sensing and deep learning models.In 2018,a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions:drought and control.We used a convolutional neural network(CNN)as a model to estimate the phenotypic values of 5 conventional biomass-related traits:dry weight,main stem length,numbers of nodes and branches,and plant height.We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models.The accuracy of the developed models was assessed through 10-fold cross-validation,which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously.Deep learning enabled us to extract features that exhibited strong correlations with the output(i.e.,phenotypes of the target traits)and accurately estimate the values of the features from the input data.We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits.Furthermore,we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions.The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.展开更多
Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key t...Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key to address FHB-related challenges,but its progress is delayed by traditional methods due to the small-scale,laborious and relatively subjective nature of manual assessment.This study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once(YOLO)model to examine FHB in wheat,enabling us to perform large-scale and automated symptomatic analysis of this disease.We first established an Open FHB(OFHB)training dataset,consisting of 4867 diseased and 106,801 healthy spikes collected from 132 commercial breeding lines during FHB progression.Then,a deep learning model called YOLOv8-WFD was trained for detecting healthy and diseased spikes,followed by an adaptive Excess Green method to identify symptomatic regions and thus FHBrelated traits on spikes.To study resistance levels,we employed an unsupervised SHapley Additive exPlanations(SHAP)method to pinpoint key traits between 10 and 20 d after inoculation(DAIs),resulting in the classification of 423 varieties trialed during the 2023–2024 growing seasons into four resistance levels(i.e.,highly and moderately susceptible,and moderately and highly resistant),which were highly correlated with field specialists’evaluations.Finally,we derived disease developmental curves based on measures of key traits during 10–20 DAI,quantifying varietal disease progression patterns over time.To our knowledge,this work represents a significant advancement in large-scale disease phenotyping and automated analysis of FHB in wheat,providing a valuable toolkit for breeders and plant researchers to assess resistance levels,select disease-resistant varieties,and understand dynamics of the fungal disease.展开更多
Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phe...Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient,subjective and destroys samples.Therefore,the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning.The Azure Kinect was used to shoot canopy color images,depth images,and RGB-D images of the watermelon plug seedlings.The Mask-RCNN network was used to classify,segment,and count the canopy leaves of the watermelon plug seedlings.To reduce the error of leaf area measurement caused by mutual occlusion of leaves,the leaves were repaired by CycleGAN,and the depth images were restored by image processing.Then,the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud.The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray.Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height.The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages.The average relative error of measurement is 2.33%for the number of true leaves,4.59%for the number of cotyledons,8.37%for the leaf area,and 3.27%for the plant height.The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.展开更多
Urinary tract infections(UTIs)are among the most prevalent pediatric bacterial infections,and undertreated episodes may lead to renal scarring,hypertension,or chronic kidney disease.Multidrug-resistant(MDR)Enterobacte...Urinary tract infections(UTIs)are among the most prevalent pediatric bacterial infections,and undertreated episodes may lead to renal scarring,hypertension,or chronic kidney disease.Multidrug-resistant(MDR)Enterobacterales have been increasingly reported in children,with higher rates in Asian and Middle Eastern settings than in high-income countries[1,2].展开更多
Screening gene function in vivo is a powerful approach to discover novel drug targets. We present high-throughput screening （HTS） data for 3 762 distinct global gene knockout （KO） mouse lines with viable adult hom...Screening gene function in vivo is a powerful approach to discover novel drug targets. We present high-throughput screening （HTS） data for 3 762 distinct global gene knockout （KO） mouse lines with viable adult homozygous mice generated using either gene-trap or homologous recombination technologies. Bone mass was determined from DEXA scans of male and female mice at 14 weeks of age and by microCT analyses of bones from male mice at 16 weeks of age. Wild-type （WT） cagemates/littermates were examined for each gene KO. Lethality was observed in an additional 850 KO lines. Since primary HTS are susceptible to false positive findings, additional cohorts of mice from KO lines with intriguing HTS bone data were examined. Aging, ovariectomy, histomorphometry and bone strength studies were performed and possible non-skeletal phenotypes were explored. Together, these screens identified multiple genes affecting bone mass： 23 previously reported genes （Calcr, Cebpb, Crtap, Dcstamp, Dkkl, Duoxa2, Enppl, Fgf23, Kissl/Kisslr, Kl （Klotho）, Lrp5, Mstn, Neol, Npr2, Ostml, Postn, Sfrp4, S1c30a5, Sic39a13, Sost, Sumf1, Src, Wnt10b）, five novel genes extensively characterized （Cldn18, Fam20c, Lrrkl, Sgpll, Wnt16）, five novel genes with preliminary characterization （Agpat2, RassfS, Slc10a7, Stc26a7, Slc30a10） and three novel undisclosed genes coding for potential osteoporosis drug targets.展开更多
With increasing population, degrading soil health, limited arable land area, and high cost of nitrogen(N) fertilizers, improving nitrogen use efficiency(NUE) of potato is an inevitable approach to save the environment...With increasing population, degrading soil health, limited arable land area, and high cost of nitrogen(N) fertilizers, improving nitrogen use efficiency(NUE) of potato is an inevitable approach to save the environment and achieve sufficient tuber yields with less N fertilizer supply. Recently, we have developed an aeroponics system to study NUE in potato using genomics, physiology, and breeding approaches. This study aims on precision phenotyping of plants of two distinct potato varieties(Kufri Gaurav, N efficient;Kufri Jyoti, N inefficient) in the novel aeroponics system. Plants were grown in aeroponics under controlled conditions with low N(0.75 mmol L^-1NO3^-) and high N(7.5 mmol L^-1NO3^-) levels. Plant biomass, root traits, total chlorophyll content, and plant N were increased with increasing N supply, whereas higher NUE parameters namely NUE, agronomic NUE(Ag NUE), N uptake efficiency(NUp E), harvest index(HI), and N harvest index(NHI) were observed at low N. An NUE efficient cv. Kufri Gaurav showed higher tuber dry weight, fresh tuber yield, tuber number per plant, early start of tuber harvesting, root traits, stolon traits, NUE parameters, and higher amino acid(aspartic acid and asparagine) content at low N supply. Higher expression of nitrate reductase(NR), nitrite reductase(NIR), and asparagine synthetase(AS) genes was observed in the leaf tissues of Kufri Gaurav at high N. Thus, aeroponics-based precision phenotyping enables identification of NUE efficient genotypes based on key traits and genes involved in improving NUE in potato. Further, this study suggests that the potential of aeroponics can be utilized to investigate N biology in potato under different N regimes.展开更多
Asthma is a common disease affecting millions of people worldwide and exerting an enormous strain on health resources in many countries. Evidence is increasing that asthma is unlikely to be a single disease but rather...Asthma is a common disease affecting millions of people worldwide and exerting an enormous strain on health resources in many countries. Evidence is increasing that asthma is unlikely to be a single disease but rather a series of complex, overlapping individual diseases or phenotypes, each defined by its unique interaction between genetic and environmental factors. Asthma phenotypes were initially focused on combinations ofclinical characteristics, but they are now evolving to link pathophysiological mechanism to subtypes of asthma. Better characterization of those phenotypes is expected to be most useful for allocating asthma therapies. This article reviews different published researches in terms of unbiased approaches to phenotype asthma and emphasizes how the phenotyping exercise is an important step towards proper asthma treatment. It is structured into three sections; the heterogeneity of asthma, the impact of asthma heterogeneity on asthma management and different trials for phenotyping asthma.展开更多
Drought is envisaged as the greatest demolishing natural impacts throughout the world since it has observed extensive place of agronomical land sterile almost the world. It’s the significant crop output-limiting prod...Drought is envisaged as the greatest demolishing natural impacts throughout the world since it has observed extensive place of agronomical land sterile almost the world. It’s the significant crop output-limiting producer, and elaborated learning of its result on plant enhancement dictation is diametrical. At present, drought tolerant hybrid maize has been trying to induce Bangladesh especially drought affected zone to identify the drought endurance maize genotypes. Consequently, a feasible pot study of 49 hybrid maize genotypes were directed to determine an adequate drought level to promote aliment and promotion of maize plant below the water stress conditions with treatment (control and drought) and three replications. The data were received after 35 days of sowing using appropriate procedures. Specially, the stomata were collected by the white transparent nail polish from the lower part of leaves. Descriptive statistic of the all traits like percentage of SPAD, leaf rolling (LR), maximum root length (MRL), maximum shoot length (MSL), root dry matter (RDM), shoot dry matter (SDM), length of stomata (LS), width of stomata (WS), thickness of stomata (TS), total dry matter (TDM) and ANOVA for control and drought condition individually showed significant (P < 0.05) variations among the germplasm for their genotypes, treatment and interaction. The first fourth principal components (PCs) narrated about 82.0% of the total variation. Cluster analysis placed the 49 hybrid into 6 main groups among those cluster;groups five showed the maximum number mean value of traits. The highest positive relationship was obtained from TS, WS, RDM, SDM and TDM traits by forming genotype-traits bi-plot of 11traits of 49 genotypes. After analyzing, it is explicit that G18 (CML-80 × IPB911-16) and G22 (CZI-04 × IPB911-16) were the most tolerant hybrids maize genotypes and very susceptible hybrids maize genotypes were G16 (P-12 × CML487), G34 (CML-32 × PB911-16) and G37 (P-33 × CML487). It is expected that the higher expression of considered traits might be obligate for better yield under drought stress.展开更多
基金supported by the National Key Research and Development Program of China(2016YFD0100101-18,2020YFD1000904-1-3)the National Natural Science Foundation of China(31601216,31770397)Fundamental Research Funds for the Central Universities(2662019QD053,2662020ZKPY017)。
文摘With the rapid development of genetic analysis techniques and crop population size,phenotyping has become the bottleneck restricting crop breeding.Breaking through this bottleneck will require phenomics,defined as the accurate,high-throughput acquisition and analysis of multi-dimensional phenotypes during crop growth at organism-wide levels,ranging from cells to organs,individual plants,plots,and fields.Here we offer an overview of crop phenomics research from technological and platform viewpoints at various scales,including microscopic,ground-based,and aerial phenotyping and phenotypic data analysis.We describe recent applications of high-throughput phenotyping platforms for abiotic/biotic stress and yield assessment.Finally,we discuss current challenges and offer perspectives on future phenomics research.
基金supported by a grant from the Standardization and Integration of Resources Information for Seed-cluster in Hub-Spoke Material Bank Program,Rural Development Administration,Republic of Korea(PJ01587004).
文摘Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent 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 scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These 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 development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.
基金Support was from the College of Agriculture of Purdue University to Mohsen Mohammadi,USDA(1013073).
文摘The ability to screen larger populations with fewer replicates and non-destructive measurements is one advantage of high-throughput phenotyping(HTP)over traditinal phenotyping techniques.In this study,two wheat accessions were grown in a controlled-environment with a moderate drought imposed from stem elongation to post-anthesis.Red-green-blue(RGB)imaging was performed on 17 of the 22 d following the start of drought imposition.Destructive measurements from all plants were performed at the conclusion of the experiment.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 matter.The 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 Chile.This difference of 3 d indicated an increased degree of drought tolerance in the line from Chile.Furthermore,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.
文摘Recent technological advances in cotton(Gossypium hirsutum L.) phenotyping have offered tools to improve the efficiency of data collection and analysis.High-throughput phenotyping(HTP) is a non-destructive and rapid approach of monitoring and measuring multiple phenotypic traits related to the growth,yield,and adaptation to biotic or abiotic stress.Researchers have conducted extensive experiments on HTP and developed techniques including spectral,fluorescence,thermal,and three-dimensional imaging to measure the morphological,physiological,and pathological resistance traits of cotton.In addition,ground-based and aerial-based platforms were also developed to aid in the implementation of these HTP systems.This review paper highlights the techniques and recent developments for HTP in cotton,reviews the potential applications according to morphological and physiological traits of cotton,and compares the advantages and limitations of these HTP systems when used in cotton cropping systems.Overall,the use of HTP has generated many opportunities to accurately and efficiently measure and analyze diverse traits of cotton.However,because of its relative novelty,HTP has some limitations that constrains the ability to take full advantage of what it can offer.These challenges need to be addressed to increase the accuracy and utility of HTP,which can be done by integrating analytical techniques for big data and continuous advances in imaging.
基金supported by the 2023 Inner Mongolia Autonomous Region“Unveiling and Hanging”Project[grant number 2023JBGS0008]the 2023 Hohhot to introduce high-level innovative and entrepreneurial talents(team)[grant number 2023RC-High Level7].
文摘High-throughput phenotyping(HTP)technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources.To better understand the status of forage phenotyping research and identify key directions for development,this review summarizes advances in HTP technology for forage phenotypic analysis over the past ten years.This paper reviews the unique aspects and research priorities in forage phenotypic monitoring,highlights key remote sensing platforms,examines the applications of advanced sensing technology for quantifying phenotypic traits,explores artificial intelligence(AI)algorithms in phenotypic data integration and analysis,and assesses recent progress in phenotypic genomics.The practical applications of HTP technology in forage remain constrained by several challenges.These include establishing uniform data collection standards,designing effective algorithms to handle complex genetic and environmental interactions,deepening the cross-exploration of phenomics-genomics,solving the problem of pathological inversion of forage phenotypic growth monitoring models,and developing low-cost forage phenotypic equipment.Resolving these challenges will unlock the full potential of HTP,enabling precise identification of superior forage traits,accelerating the breeding of superior varieties,and ultimately improving forage yield.
基金supported by the Foundation for Food and Agriculture Research(602757)USDA-NIFA(2020-68013-32371 and 2024-67013-42449)+3 种基金Department of Energy the Office of Science(BER),U.S.DOE(DESC0020355)the National Science Foundation(IOS-2412930,2417510,and 2412928)the University of Nebraska-Lincoln's Complex Biosystems Graduate Programsupported by the National Science Foundation Graduate Research Fellowship Program under Grant No.2034837.
文摘Differences in canopy architecture play a role in determining both the light and water use efficiency.Canopy architecture is determined by several component traits,including leaf length,width,number,angle,and phyl-lotaxy.Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants.As a result,in simulations of the leaf canopies of grain crops such as maize and sorghum,this trait is frequently approximated as alternating 180°angles between sequential leaves.We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alter-nating phyllotaxy across a diverse set of sorghum genotypes.Using a voxel-carving-based approach,we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum geno-types from the sorghum association panel.The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals.Automated phyllotaxy measurements exhibited a repeatability of R^(2)=0.41 across imaging timepoints separated by a period of two days.A resampling based genome wide association study(GWAS)identified several putative genetic associations with lower-canopy phyllotaxy in sorghum.This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
基金supported by the National Natural Science Foundation of China(32370435,62106080)the Hubei Provincial Natural Science Foundation of China(2024AFB566).
文摘For fast in-situ assessment of tiller phenotypes in rice breeding,we introduce the TillerPET model,an improved transformer-based deep learning solution that permits phenotyping the number and compactness of rice tillers in images of post-harvest rice stubble.A rice tiller phenotype dataset covering three years of field data and four experimental sites across China was constructed to train and validate the model.TillerPET reports an R2 of 0.941 for counting tiller number,demonstrating state-of-the-art performance on the proposed RTP dataset.Beyond its minimal errors in estimating tiller number,TillerPET also achieves an R2 of 0.978 for characterizing tiller compactness.The two phenotypic parameters exhibit a high degree of consistency with expert breeders,offering reliable phenotypic indicators to guide further breeding.
基金Participation of Jos Luis Araus and María Dolors Serret was supported by the Spanish Project AGL2010-20180 (subprogram AGR)the FP7 European Project OPTICHINA (266045)
文摘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.
文摘Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years.These 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 programs.High-throughput phenotyping techniques and platforms help unrave-ling the genetic basis of complex traits associated with plant growth and development and targeted traits.This review focuses on the advancements in technologies involved in high-throughput,field-based,aerial,and unmanned platforms.Development 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.
基金the National Key Research and Development Program of China(2016YFD0100101-18,2016YFD0100103)the National Natural Science Foundation of China(31770397,21800305)+2 种基金the Fundamental Research Funds for the Central Universities(2662017PY058,2662017QD044)UK-China grant BBSRC(grant no.BB/R02118X/1)the National Institute of Food and Agriculture,U.S.Department of Agriculture,Hatch project(ALA014-1-16016).
文摘Since whole-genome sequencing of many crops has been achieved,crop functional genomics studies have stepped into the big-data and high-throughput era.However,acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies.Nevertheless,recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years.In this article,we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades.We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies.Finally,we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap.It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.
基金supported by China Scholarships for Study Abroad
文摘Phenomics studies a variety of phenotypic plant traits and is the key to understanding genetic functions and environmental effects on plants. With the rapid development of genomics, many plant phenotyping platforms have been developed to study complex traits related to the growth, yield, and adaptation to biotic or abiotic stress, but the ability to acquire high-throughput phenotypic data has become the bottleneck in the study of plant genomics. In recent years, researchers around the world have conducted extensive experiments and research on high-throughput, image-based phenotyping techniques,including visible light imaging, fluorescence imaging,thermal imaging, spectral imaging, stereo imaging, and tomographic imaging. This paper considers imaging technologies developed in recent years for high-throughput phenotyping, reviews applications of these technologies in detecting and measuring plant morphological, physiological, and pathological traits, and compares their advantages and limitations.
文摘Patients admitted with prediabetes and atrial fibrillation are at high risk for major adverse cardiac or cerebrovascular events independent of confounding variables.The shared pathophysiology between these three serious but common diseases and their association with atherosclerotic cardiovascular risk factors establish a vicious circle culminating in high atherogenicity.Because of that,it is of paramount importance to perform risk stratification of patients with prediabetes to define phenotypes that benefit from various interventions.Furthermore,stress hyperglycemia assessment of hospitalized patients and consensus on the definition of prediabetes is vital.The roles lifestyle and metformin play in prediabetes are well established.However,the role of glucagon-like peptide agonists and metabolic surgery is less clear.Prediabetes is considered an intermediate between normoglycemia and diabetes along the blood glucose continuum.One billion people are expected to suffer from prediabetes by the year 2045.Therefore,realworld randomized controlled trials to assess major adverse cardiac or cerebrovascular event risk reduction and reversal/prevention of type 2 diabetes among patients are needed to determine the proper interventions.
基金supported by the JST CREST[grant number:JPMJCR16O2]and MEXT KAKENHI[grant number:JP22H02306].The funders had no role in the study design,data collection and analysis,decision to publish,or manuscript preparation.
文摘High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine max)using unmanned aerial vehicle(UAV)remote sensing and deep learning models.In 2018,a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions:drought and control.We used a convolutional neural network(CNN)as a model to estimate the phenotypic values of 5 conventional biomass-related traits:dry weight,main stem length,numbers of nodes and branches,and plant height.We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models.The accuracy of the developed models was assessed through 10-fold cross-validation,which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously.Deep learning enabled us to extract features that exhibited strong correlations with the output(i.e.,phenotypes of the target traits)and accurately estimate the values of the features from the input data.We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits.Furthermore,we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions.The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.
基金supported by the Biological Breeding-National Science and Technology Major Project(2023ZD04025 to Xiu’e Wang)the Seed Industry Revitalization Project of Jiangsu Province(JBGS(2021)006 to Xiu’e Wang)+3 种基金the National Natural Science Foundation of China(32070400 to Ji Zhou)Ji Zhou,Robert Jackson,and Greg Deakin were partially supported by the Allan&Gill Gray Foundation’Sustainable Productivity for Crop Improvement(G118688 to the University of Cambridge and Q-20-0370 to NIAB)Ji Zhou was supported by the United Kingdom Research and Innovation’s(UKRI)Biotechnology and Bio logical Sciences Research Council(BBSRC)AI in Bioscience Grant(BB/Y513969/1 to Ji Zhou)The UK-China research activities were supported by the BBSRC’s International Partnership Grant(BB/Y514081/1 to NIAB)
文摘Fusarium head blight(FHB)is a serious fungal disease that affect small grain cereals,causing significant wheat(Triticum aestivum L.)yield and quality losses globally.Breeding disease-resistant wheat varieties is key to address FHB-related challenges,but its progress is delayed by traditional methods due to the small-scale,laborious and relatively subjective nature of manual assessment.This study presents a new approach that combines ultralow-altitude drone phenotyping with an optimized You Only Look Once(YOLO)model to examine FHB in wheat,enabling us to perform large-scale and automated symptomatic analysis of this disease.We first established an Open FHB(OFHB)training dataset,consisting of 4867 diseased and 106,801 healthy spikes collected from 132 commercial breeding lines during FHB progression.Then,a deep learning model called YOLOv8-WFD was trained for detecting healthy and diseased spikes,followed by an adaptive Excess Green method to identify symptomatic regions and thus FHBrelated traits on spikes.To study resistance levels,we employed an unsupervised SHapley Additive exPlanations(SHAP)method to pinpoint key traits between 10 and 20 d after inoculation(DAIs),resulting in the classification of 423 varieties trialed during the 2023–2024 growing seasons into four resistance levels(i.e.,highly and moderately susceptible,and moderately and highly resistant),which were highly correlated with field specialists’evaluations.Finally,we derived disease developmental curves based on measures of key traits during 10–20 DAI,quantifying varietal disease progression patterns over time.To our knowledge,this work represents a significant advancement in large-scale disease phenotyping and automated analysis of FHB in wheat,providing a valuable toolkit for breeders and plant researchers to assess resistance levels,select disease-resistant varieties,and understand dynamics of the fungal disease.
基金funded by the National Key Research and Development Program of China(Grant No.2019YFD1001900)the HZAU-AGIS Cooperation Fund(Grant No.SZYJY2022006).
文摘Nondestructive measurement technology of phenotype can provide substantial phenotypic data support for applications such as seedling breeding,management,and quality testing.The current method of measuring seedling phenotypes mainly relies on manual measurement which is inefficient,subjective and destroys samples.Therefore,the paper proposes a nondestructive measurement method for the canopy phenotype of the watermelon plug seedlings based on deep learning.The Azure Kinect was used to shoot canopy color images,depth images,and RGB-D images of the watermelon plug seedlings.The Mask-RCNN network was used to classify,segment,and count the canopy leaves of the watermelon plug seedlings.To reduce the error of leaf area measurement caused by mutual occlusion of leaves,the leaves were repaired by CycleGAN,and the depth images were restored by image processing.Then,the Delaunay triangulation was adopted to measure the leaf area in the leaf point cloud.The YOLOX target detection network was used to identify the growing point position of each seedling on the plug tray.Then the depth differences between the growing point and the upper surface of the plug tray were calculated to obtain plant height.The experiment results show that the nondestructive measurement algorithm proposed in this paper achieves good measurement performance for the watermelon plug seedlings from the 1 true-leaf to 3 true-leaf stages.The average relative error of measurement is 2.33%for the number of true leaves,4.59%for the number of cotyledons,8.37%for the leaf area,and 3.27%for the plant height.The experiment results demonstrate that the proposed algorithm in this paper provides an effective solution for the nondestructive measurement of the canopy phenotype of the plug seedlings.
文摘Urinary tract infections(UTIs)are among the most prevalent pediatric bacterial infections,and undertreated episodes may lead to renal scarring,hypertension,or chronic kidney disease.Multidrug-resistant(MDR)Enterobacterales have been increasingly reported in children,with higher rates in Asian and Middle Eastern settings than in high-income countries[1,2].
文摘Screening gene function in vivo is a powerful approach to discover novel drug targets. We present high-throughput screening （HTS） data for 3 762 distinct global gene knockout （KO） mouse lines with viable adult homozygous mice generated using either gene-trap or homologous recombination technologies. Bone mass was determined from DEXA scans of male and female mice at 14 weeks of age and by microCT analyses of bones from male mice at 16 weeks of age. Wild-type （WT） cagemates/littermates were examined for each gene KO. Lethality was observed in an additional 850 KO lines. Since primary HTS are susceptible to false positive findings, additional cohorts of mice from KO lines with intriguing HTS bone data were examined. Aging, ovariectomy, histomorphometry and bone strength studies were performed and possible non-skeletal phenotypes were explored. Together, these screens identified multiple genes affecting bone mass： 23 previously reported genes （Calcr, Cebpb, Crtap, Dcstamp, Dkkl, Duoxa2, Enppl, Fgf23, Kissl/Kisslr, Kl （Klotho）, Lrp5, Mstn, Neol, Npr2, Ostml, Postn, Sfrp4, S1c30a5, Sic39a13, Sost, Sumf1, Src, Wnt10b）, five novel genes extensively characterized （Cldn18, Fam20c, Lrrkl, Sgpll, Wnt16）, five novel genes with preliminary characterization （Agpat2, RassfS, Slc10a7, Stc26a7, Slc30a10） and three novel undisclosed genes coding for potential osteoporosis drug targets.
基金the Competent Authority, Indian Council of Agricultural Research (ICAR)-Central Potato Research Institute (CPRI), Shimla, Himachal Pradesh, India for necessary supports under the Biotechnology Program and the CABin Scheme (ICAR) (HORTCPRICIL 201500300131)
文摘With increasing population, degrading soil health, limited arable land area, and high cost of nitrogen(N) fertilizers, improving nitrogen use efficiency(NUE) of potato is an inevitable approach to save the environment and achieve sufficient tuber yields with less N fertilizer supply. Recently, we have developed an aeroponics system to study NUE in potato using genomics, physiology, and breeding approaches. This study aims on precision phenotyping of plants of two distinct potato varieties(Kufri Gaurav, N efficient;Kufri Jyoti, N inefficient) in the novel aeroponics system. Plants were grown in aeroponics under controlled conditions with low N(0.75 mmol L^-1NO3^-) and high N(7.5 mmol L^-1NO3^-) levels. Plant biomass, root traits, total chlorophyll content, and plant N were increased with increasing N supply, whereas higher NUE parameters namely NUE, agronomic NUE(Ag NUE), N uptake efficiency(NUp E), harvest index(HI), and N harvest index(NHI) were observed at low N. An NUE efficient cv. Kufri Gaurav showed higher tuber dry weight, fresh tuber yield, tuber number per plant, early start of tuber harvesting, root traits, stolon traits, NUE parameters, and higher amino acid(aspartic acid and asparagine) content at low N supply. Higher expression of nitrate reductase(NR), nitrite reductase(NIR), and asparagine synthetase(AS) genes was observed in the leaf tissues of Kufri Gaurav at high N. Thus, aeroponics-based precision phenotyping enables identification of NUE efficient genotypes based on key traits and genes involved in improving NUE in potato. Further, this study suggests that the potential of aeroponics can be utilized to investigate N biology in potato under different N regimes.
文摘Asthma is a common disease affecting millions of people worldwide and exerting an enormous strain on health resources in many countries. Evidence is increasing that asthma is unlikely to be a single disease but rather a series of complex, overlapping individual diseases or phenotypes, each defined by its unique interaction between genetic and environmental factors. Asthma phenotypes were initially focused on combinations ofclinical characteristics, but they are now evolving to link pathophysiological mechanism to subtypes of asthma. Better characterization of those phenotypes is expected to be most useful for allocating asthma therapies. This article reviews different published researches in terms of unbiased approaches to phenotype asthma and emphasizes how the phenotyping exercise is an important step towards proper asthma treatment. It is structured into three sections; the heterogeneity of asthma, the impact of asthma heterogeneity on asthma management and different trials for phenotyping asthma.
文摘Drought is envisaged as the greatest demolishing natural impacts throughout the world since it has observed extensive place of agronomical land sterile almost the world. It’s the significant crop output-limiting producer, and elaborated learning of its result on plant enhancement dictation is diametrical. At present, drought tolerant hybrid maize has been trying to induce Bangladesh especially drought affected zone to identify the drought endurance maize genotypes. Consequently, a feasible pot study of 49 hybrid maize genotypes were directed to determine an adequate drought level to promote aliment and promotion of maize plant below the water stress conditions with treatment (control and drought) and three replications. The data were received after 35 days of sowing using appropriate procedures. Specially, the stomata were collected by the white transparent nail polish from the lower part of leaves. Descriptive statistic of the all traits like percentage of SPAD, leaf rolling (LR), maximum root length (MRL), maximum shoot length (MSL), root dry matter (RDM), shoot dry matter (SDM), length of stomata (LS), width of stomata (WS), thickness of stomata (TS), total dry matter (TDM) and ANOVA for control and drought condition individually showed significant (P < 0.05) variations among the germplasm for their genotypes, treatment and interaction. The first fourth principal components (PCs) narrated about 82.0% of the total variation. Cluster analysis placed the 49 hybrid into 6 main groups among those cluster;groups five showed the maximum number mean value of traits. The highest positive relationship was obtained from TS, WS, RDM, SDM and TDM traits by forming genotype-traits bi-plot of 11traits of 49 genotypes. After analyzing, it is explicit that G18 (CML-80 × IPB911-16) and G22 (CZI-04 × IPB911-16) were the most tolerant hybrids maize genotypes and very susceptible hybrids maize genotypes were G16 (P-12 × CML487), G34 (CML-32 × PB911-16) and G37 (P-33 × CML487). It is expected that the higher expression of considered traits might be obligate for better yield under drought stress.
