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 for crop monitoring at both leaf and canopy scales is essential for understanding plant responses to various stresses.PhenoGazer,a high-throughput phenotyping system,enhances crop moni-tori...High throughput phenotyping for crop monitoring at both leaf and canopy scales is essential for understanding plant responses to various stresses.PhenoGazer,a high-throughput phenotyping system,enhances crop moni-toring in controlled environments by integrating a portable hyperspectral spectrometer with eight fiber optics,four Raspberry Pi cameras,and blue LED lights.This system allows for comprehensive assessment of plant health and development.PhenoGazer features automated moveable upper and lower racks for continuous measure-ments.The lower rack,equipped with four blue LED lights and spectrometer fiber optics,captures blue light-induced chlorophyll fluorescence at night.The upper rack,carrying four spectrometer fiber optics and cam-eras,captures hyperspectral reflectance and RGB images during the day.This dual capability enables detailed evaluation of plant phenology,stress responses,and growth dynamics throughout the entire crop growth cycle.Fully automated and managed by a Raspberry Pi running Python scripts,PhenoGazer ensures precise control and data acquisition with minimal human intervention.Additionally,it includes continuous measurements through a datalogger to acquire photosynthetically active radiation(PAR),soil moisture and temperature,and features expansion capability for additional analog or digital sensors as desired by end users.To test the system,soybean plants representing three conditions,healthy well watered,healthy droughted,and diseased,were monitored to evaluate growth and stress responses.PhenoGazer successfully phenotyped plants under different conditions in a walk-in growth chamber.By combining nighttime blue light induced chlorophyll fluorescence,hyperspectral reflectance-based vegetation indices,and RGB imagery,PhenoGazer represented a significant advancement in plant phenotyping technology,enhancing our understanding of crop responses to environmental conditions and supporting optimized crop performance in research and agricultural applications.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
Norway spruce(Picea abies Karst L.)is one of the most ecologically and economically significant tree species in Europe,accounting for nearly half of the continent's forest economic value.However,drought is a signi...Norway spruce(Picea abies Karst L.)is one of the most ecologically and economically significant tree species in Europe,accounting for nearly half of the continent's forest economic value.However,drought is a significant stress factor associated with increasing Norway spruce mortality across Europe.Provenance trials,a traditional approach to assess adaptive variation,face limitations stemming from the finite number of sites,seed sources involved,and their required labor-intensive nature.In response,we developed a comprehensive multisensor high-throughput phenotyping method and integrated it with metabolomics,transcriptomics,and anatomical analyses to study the drought stress responses in two climatically contrasting but geographically proximal provenances at the seedling stage by exposing them to drought stress for a period of 21 days.Based on more than 50 physiological and growth-related traits assessed by the phenotyping platform,it was possible to characterize early and late drought stress responses.Consistent with phenotypic data,mRNA-seq,and metabolic profiles revealed apparent differences between treatments.While during the drought stress the metabolic data indicated an increased pro-duction of ABA,α-tocopherol,zeaxanthin,lutein,and phenolics,mRNA-seq showed modulation of related pathways and downregulation of photosystem transcripts.Although drought responses were largely conserved between the two provenances,they differed phenotypically in traits related to the activation of re-oxidation of the plastoquinone pool,and molecularly in transcriptional and phenolic profiles.In conclusion,our study demon-strates the potential of the high-throughput phenotyping approach for evaluating drought stress adaptation in Norway spruce thus accelerating the screening and selection of best adapted provenances.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
Understanding the genetic basis of quantitative traits related to crop growth,yield,and stress response requires the acquisition of large-scale,high-quality phenotypic datasets.High-throughput phenotyping platforms ha...Understanding the genetic basis of quantitative traits related to crop growth,yield,and stress response requires the acquisition of large-scale,high-quality phenotypic datasets.High-throughput phenotyping platforms have become effective tools for meeting this requirement.Autonomous mobile robots have gained prominence owing to their ability to carry heavy payloads,their operational flexibility,and their proximity to crops,which allows for higher imaging resolution.In this study,we introduce PhenoRob-F(a phenotyping robot for the field),a cross-row,wheeled robot designed for efficient and automated phenotyping under field conditions.The mobile platform and phenotyping module of the robot were engineered to meet the specific demands of field pheno-typing,with integrated visual and satellite navigation systems enabling autonomous operation.We validated the performance of the robot through a series of experiments involving various crop canopies.By capturing RGB images of rice and wheat,we independently performed wheat ear detection and rice panicle segmentation.For wheat ear detection,we achieve a precision of 0.783,a recall of 0.822,and a mean average precision(mAP)of 0.853 when the YOLOv8m model is used.For rice panicle segmentation,the SegFormer_BO model yielded a mean intersection over union(mIoU)of 0.949 and an accuracy of 0.987.Additionally,by capturing RGB-D data of maize canopies,we performed 3D reconstructions to calculate plant height,achieving an R^(2) of 0.99 compared with manual measurements.Similar experiments with rapeseed yielded an R^(2) of 0.97.Near-infrared spectral data collected from drought-stressed rice plants enabled the classification of drought severity into five categories,with classification accuracies ranging from 0.977 to 0.996.Our results reveal that PhenoRob-F is an effective tool for high-throughput phenotyping and is capable of providing precise data to support phenotypic trait analysis and the selection of superior crop genotypes.展开更多
Fusiform rust,caused by the pathogen Cronartium quercuum(Berk.)Miyabe ex Shirai f.sp.fusiforme,is the most important disease of loblolly pine(Pinus taeda L.)in the U.S.,causing millions of dollars in damage each year....Fusiform rust,caused by the pathogen Cronartium quercuum(Berk.)Miyabe ex Shirai f.sp.fusiforme,is the most important disease of loblolly pine(Pinus taeda L.)in the U.S.,causing millions of dollars in damage each year.Using resistant genotypes has proven a successful strategy to limit the disease,but resistance selection still relies on visual inspection for symptoms,which can lead to misclassification due to human error and the presence of'escaped susceptibles'(i.e.,susceptible individuals with no visible symptoms due to either an extended asymp-tomatic phase of the disease or the lack of adequate disease pressure to become infected).Here,we propose the use of near-infrared(NIR)spectroscopy and chemometrics to improve the accuracy of how phenotypes are rated.We collected and analyzed phloem and needle spectra from 34 non-related families replicated across eight stands in three states in the southeastern region of the U.S.using a portable,handheld NIR spectrometer.We also used a benchtop Fourier-transformed mid-infrared(FT-IR)spectrometer to analyze phloem phenolic extracts of the same samples,as this phenotyping approach has proved successful in other pathosystems.Our results show a moderate association between the phloem spectra and resistance,and models built with NIR spectra were able to classify extremes(i.e.,very resistant or very susceptible)with up to 69%testing accuracy.This study provides a framework for using NIR spectroscopy for phenotyping loblolly pine resistance against pathogens and advocates for using alternative technologies in forestry.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
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
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
The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health an...The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health and provide mitigation as early as possible.Phenotyping is a non-destructive method for assessing crop responses to environmental stresses and can be performed using airborne systems.Unmanned Aerial Systems(UAS)have significantly contributed to high-throughput phenotyping andmade the process rapid,efficient,and non-invasive for collecting large-scale agronomic data.Because of the high complexity and cost of specialized equipment used in aerial phenotyping,such as multispectral and hyperspectral cameras as well as lidar,this study proposes a framework for implementing aerial phenotyping where chlorophyll estimation,leaf count,and coverage are determined using the RGB(Red,Green and Blue)camera native to a UAS.Thestudy proposes the Dynamic Coefficient Triangular Greenness Index(DCTGI)for aerial phenotyping.Evaluation of the proposed DCTGI includes the correlation with chlorophyll content estimated using a Soil Plant Analysis Development(SPAD)chlorophyll meter on randomly sampled Liberica coffee seedlings.Analysis revealed a strong relationship between DCTGI values and chlorophyll estimates derived from SPAD measurements,with a Pearson’s correlation coefficient of 0.912.However,the study didn’t implement tissue-level validation and field-scale temporal analysis to assess seasonal variability.In addition,the SPAD meter provided the approximate nitrogen content together with the chlorohyll estimate.展开更多
BACKGROUND Many conditions may affect left ventricular(LV)phenotypes which have been classified according to LV mass and geometry.There is limited data on the prognostic value of LV phenotypes classified by cardiac ma...BACKGROUND Many conditions may affect left ventricular(LV)phenotypes which have been classified according to LV mass and geometry.There is limited data on the prognostic value of LV phenotypes classified by cardiac magnetic resonance(CMR).This study aimed to determine the prognostic value of LV phenotypes in elderly and non-elderly patients with known or suspected coronary artery disease.METHODS This is a retrospective cohort study among patients who underwent stress or viability CMR.LV phenotypes were classified according to the LV mass index,the LV end-diastolic volume index and the LV mass/volume ratio,into normal,concentric remodeling,concentric hypertrophy,and eccentric hypertrophy.The primary outcome was a composite of death or heart failure.RESULTS A total of 3289 patients was studied.The average age was 68.0±12.7 years,52.2%of patients were women.Elderly were defined as age≥65 years accounting for 63.9%of the cohort.LV phenotypes were normal,concentric remodeling,concentric hypertrophy,and eccentric hypertrophy at 74.5%,5.8%,9.2%,and 10.5%,respectively.The median duration of follow-up was 41.4 months.The composite outcome of death or heart failure occurred in 7.3%of patients.The prognostic impact of LV phenotypes was more pronounced in the elderly,with eccentric hypertrophy showing the worst prognosis,followed by concentric hypertrophy and concentric remodeling with the adjusted hazard ratio(95%CI)of 2.37(1.72–3.25),1.53(1.12–2.08),and 1.14(0.76–1.71),respectively,compared to normal phenotype.Patients with eccentric hypertrophy also demonstrated abnormal global longitudinal LV strain,left atrial strain,and extracellular volume fraction.CONCLUSIONS LV phenotypes by CMR independently predict adverse clinical outcomes in elderly patients with known or suspected coronary artery disease.In non-elderly patients,the prognostic value of LV phenotypes was less evident.Assessment of LV phenotypes may be useful for risk stratification.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This work was supported by a Cooperative Research And Development Agreement with JB Hyperspectral Devices,GmbH(CRADA No.58-8042-2-029F).
文摘High throughput phenotyping for crop monitoring at both leaf and canopy scales is essential for understanding plant responses to various stresses.PhenoGazer,a high-throughput phenotyping system,enhances crop moni-toring in controlled environments by integrating a portable hyperspectral spectrometer with eight fiber optics,four Raspberry Pi cameras,and blue LED lights.This system allows for comprehensive assessment of plant health and development.PhenoGazer features automated moveable upper and lower racks for continuous measure-ments.The lower rack,equipped with four blue LED lights and spectrometer fiber optics,captures blue light-induced chlorophyll fluorescence at night.The upper rack,carrying four spectrometer fiber optics and cam-eras,captures hyperspectral reflectance and RGB images during the day.This dual capability enables detailed evaluation of plant phenology,stress responses,and growth dynamics throughout the entire crop growth cycle.Fully automated and managed by a Raspberry Pi running Python scripts,PhenoGazer ensures precise control and data acquisition with minimal human intervention.Additionally,it includes continuous measurements through a datalogger to acquire photosynthetically active radiation(PAR),soil moisture and temperature,and features expansion capability for additional analog or digital sensors as desired by end users.To test the system,soybean plants representing three conditions,healthy well watered,healthy droughted,and diseased,were monitored to evaluate growth and stress responses.PhenoGazer successfully phenotyped plants under different conditions in a walk-in growth chamber.By combining nighttime blue light induced chlorophyll fluorescence,hyperspectral reflectance-based vegetation indices,and RGB imagery,PhenoGazer represented a significant advancement in plant phenotyping technology,enhancing our understanding of crop responses to environmental conditions and supporting optimized crop performance in research and agricultural applications.
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Financial support was provided by the Austrian Research Promotion Agency(FFG)and grant Nu.2021–0.382.292(project:climate smart forests:provenance selection and planting method_AP2)with funds from the departmental research program through dafne.at with resources from the Federal Ministry of Agriculture,Regions,and Water Management.
文摘Norway spruce(Picea abies Karst L.)is one of the most ecologically and economically significant tree species in Europe,accounting for nearly half of the continent's forest economic value.However,drought is a significant stress factor associated with increasing Norway spruce mortality across Europe.Provenance trials,a traditional approach to assess adaptive variation,face limitations stemming from the finite number of sites,seed sources involved,and their required labor-intensive nature.In response,we developed a comprehensive multisensor high-throughput phenotyping method and integrated it with metabolomics,transcriptomics,and anatomical analyses to study the drought stress responses in two climatically contrasting but geographically proximal provenances at the seedling stage by exposing them to drought stress for a period of 21 days.Based on more than 50 physiological and growth-related traits assessed by the phenotyping platform,it was possible to characterize early and late drought stress responses.Consistent with phenotypic data,mRNA-seq,and metabolic profiles revealed apparent differences between treatments.While during the drought stress the metabolic data indicated an increased pro-duction of ABA,α-tocopherol,zeaxanthin,lutein,and phenolics,mRNA-seq showed modulation of related pathways and downregulation of photosystem transcripts.Although drought responses were largely conserved between the two provenances,they differed phenotypically in traits related to the activation of re-oxidation of the plastoquinone pool,and molecularly in transcriptional and phenolic profiles.In conclusion,our study demon-strates the potential of the high-throughput phenotyping approach for evaluating drought stress adaptation in Norway spruce thus accelerating the screening and selection of best adapted provenances.
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This work was supported by the National Key Research and Development Program of China(2021YFD1200504,2022YFD2002304)the National Natural Science Foundation of China(32471992)+1 种基金the Key Core Technology Project in Agriculture of Hubei Province(HBNYHXGG2023-9)the Supporting Project for High-Quality Development of the Seed Industry of Hubei Province(HBZY2023B001-06).
文摘Understanding the genetic basis of quantitative traits related to crop growth,yield,and stress response requires the acquisition of large-scale,high-quality phenotypic datasets.High-throughput phenotyping platforms have become effective tools for meeting this requirement.Autonomous mobile robots have gained prominence owing to their ability to carry heavy payloads,their operational flexibility,and their proximity to crops,which allows for higher imaging resolution.In this study,we introduce PhenoRob-F(a phenotyping robot for the field),a cross-row,wheeled robot designed for efficient and automated phenotyping under field conditions.The mobile platform and phenotyping module of the robot were engineered to meet the specific demands of field pheno-typing,with integrated visual and satellite navigation systems enabling autonomous operation.We validated the performance of the robot through a series of experiments involving various crop canopies.By capturing RGB images of rice and wheat,we independently performed wheat ear detection and rice panicle segmentation.For wheat ear detection,we achieve a precision of 0.783,a recall of 0.822,and a mean average precision(mAP)of 0.853 when the YOLOv8m model is used.For rice panicle segmentation,the SegFormer_BO model yielded a mean intersection over union(mIoU)of 0.949 and an accuracy of 0.987.Additionally,by capturing RGB-D data of maize canopies,we performed 3D reconstructions to calculate plant height,achieving an R^(2) of 0.99 compared with manual measurements.Similar experiments with rapeseed yielded an R^(2) of 0.97.Near-infrared spectral data collected from drought-stressed rice plants enabled the classification of drought severity into five categories,with classification accuracies ranging from 0.977 to 0.996.Our results reveal that PhenoRob-F is an effective tool for high-throughput phenotyping and is capable of providing precise data to support phenotypic trait analysis and the selection of superior crop genotypes.
基金This research was funded by the United States Forest Service,Forest Health Protection Special Technology Development Program(grant number 20-DG-11083150-003)the Southern Pine Health Research Cooperative(SPHRC)at the University of Georgia(Athens,Georgia,United States).
文摘Fusiform rust,caused by the pathogen Cronartium quercuum(Berk.)Miyabe ex Shirai f.sp.fusiforme,is the most important disease of loblolly pine(Pinus taeda L.)in the U.S.,causing millions of dollars in damage each year.Using resistant genotypes has proven a successful strategy to limit the disease,but resistance selection still relies on visual inspection for symptoms,which can lead to misclassification due to human error and the presence of'escaped susceptibles'(i.e.,susceptible individuals with no visible symptoms due to either an extended asymp-tomatic phase of the disease or the lack of adequate disease pressure to become infected).Here,we propose the use of near-infrared(NIR)spectroscopy and chemometrics to improve the accuracy of how phenotypes are rated.We collected and analyzed phloem and needle spectra from 34 non-related families replicated across eight stands in three states in the southeastern region of the U.S.using a portable,handheld NIR spectrometer.We also used a benchtop Fourier-transformed mid-infrared(FT-IR)spectrometer to analyze phloem phenolic extracts of the same samples,as this phenotyping approach has proved successful in other pathosystems.Our results show a moderate association between the phloem spectra and resistance,and models built with NIR spectra were able to classify extremes(i.e.,very resistant or very susceptible)with up to 69%testing accuracy.This study provides a framework for using NIR spectroscopy for phenotyping loblolly pine resistance against pathogens and advocates for using alternative technologies in forestry.
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.
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.
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.
文摘The effects of climate change are becoming more evident nowadays,and the environmental stress imposed on crops has become more severe.Farmers around the globe continually seek ways to gain insights into crop health and provide mitigation as early as possible.Phenotyping is a non-destructive method for assessing crop responses to environmental stresses and can be performed using airborne systems.Unmanned Aerial Systems(UAS)have significantly contributed to high-throughput phenotyping andmade the process rapid,efficient,and non-invasive for collecting large-scale agronomic data.Because of the high complexity and cost of specialized equipment used in aerial phenotyping,such as multispectral and hyperspectral cameras as well as lidar,this study proposes a framework for implementing aerial phenotyping where chlorophyll estimation,leaf count,and coverage are determined using the RGB(Red,Green and Blue)camera native to a UAS.Thestudy proposes the Dynamic Coefficient Triangular Greenness Index(DCTGI)for aerial phenotyping.Evaluation of the proposed DCTGI includes the correlation with chlorophyll content estimated using a Soil Plant Analysis Development(SPAD)chlorophyll meter on randomly sampled Liberica coffee seedlings.Analysis revealed a strong relationship between DCTGI values and chlorophyll estimates derived from SPAD measurements,with a Pearson’s correlation coefficient of 0.912.However,the study didn’t implement tissue-level validation and field-scale temporal analysis to assess seasonal variability.In addition,the SPAD meter provided the approximate nitrogen content together with the chlorohyll estimate.
文摘BACKGROUND Many conditions may affect left ventricular(LV)phenotypes which have been classified according to LV mass and geometry.There is limited data on the prognostic value of LV phenotypes classified by cardiac magnetic resonance(CMR).This study aimed to determine the prognostic value of LV phenotypes in elderly and non-elderly patients with known or suspected coronary artery disease.METHODS This is a retrospective cohort study among patients who underwent stress or viability CMR.LV phenotypes were classified according to the LV mass index,the LV end-diastolic volume index and the LV mass/volume ratio,into normal,concentric remodeling,concentric hypertrophy,and eccentric hypertrophy.The primary outcome was a composite of death or heart failure.RESULTS A total of 3289 patients was studied.The average age was 68.0±12.7 years,52.2%of patients were women.Elderly were defined as age≥65 years accounting for 63.9%of the cohort.LV phenotypes were normal,concentric remodeling,concentric hypertrophy,and eccentric hypertrophy at 74.5%,5.8%,9.2%,and 10.5%,respectively.The median duration of follow-up was 41.4 months.The composite outcome of death or heart failure occurred in 7.3%of patients.The prognostic impact of LV phenotypes was more pronounced in the elderly,with eccentric hypertrophy showing the worst prognosis,followed by concentric hypertrophy and concentric remodeling with the adjusted hazard ratio(95%CI)of 2.37(1.72–3.25),1.53(1.12–2.08),and 1.14(0.76–1.71),respectively,compared to normal phenotype.Patients with eccentric hypertrophy also demonstrated abnormal global longitudinal LV strain,left atrial strain,and extracellular volume fraction.CONCLUSIONS LV phenotypes by CMR independently predict adverse clinical outcomes in elderly patients with known or suspected coronary artery disease.In non-elderly patients,the prognostic value of LV phenotypes was less evident.Assessment of LV phenotypes may be useful for risk stratification.
