High-throughput phenotyping technologies allow accurate selection of stay-green

Abstract Objective Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. Materials and methods Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. Results The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. Discussion Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. Conclusion The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping.

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Identification of stay-green and early senescence phenotypes in high-yielding winter wheat, and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques

Functional Plant Biology ◽

10.1071/fp13221 ◽

2014 ◽

Vol 41 (3) ◽

pp. 227 ◽

Cited By ~ 35

Author(s):

Sebastian Kipp ◽

Bodo Mistele ◽

Urs Schmidhalter

Keyword(s):

Winter Wheat ◽

Grain Yield ◽

High Throughput ◽

Protein Concentration ◽

Partial Least Square ◽

Large Field ◽

Grain Protein ◽

Stay Green ◽

Grain Protein Concentration ◽

High Throughput Phenotyping

Yield and grain protein concentration (GPC) represent crucial factors in the global agricultural wheat (Triticum aestivum L.) production and are predominantly determined via carbon and nitrogen metabolism, respectively. The maintenance of green leaf area and the onset of senescence (Osen) are expected to be involved in both C and N accumulation and their translocation into grains. The aim of this study was to identify stay-green and early senescence phenotypes in a field experiment of 50 certified winter wheat cultivars and to investigate the relationships among Osen, yield and GPC. Colour measurements on flag leaves were conducted to determine Osen for 20 cultivars and partial least square regression models were used to calculate Osen for the remaining 30 cultivars based on passive spectral reflectance measurements as a high-throughput phenotyping technique for all varieties. Using this method, stay-green and early senescence phenotypes could be clearly differentiated. A significant negative relationship between Osen and grain yield (r2 = 0.81) was observed. By contrast, GPC showed a significant positive relationship to Osen (r2 = 0.48). In conclusion, the high-throughput character of our proposed phenotyping method should help improve the detection of such traits in large field trials as well as help us reach a better understanding of the consequences of the timing of senescence on yield.

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Aerial High-Throughput Phenotyping Enabling Indirect Selection for Grain Yield at the Early-generation Seed-limited Stages in Breeding Programs

10.1101/2020.04.21.054163 ◽

2020 ◽

Author(s):

Margaret R. Krause ◽

Suchismita Mondal ◽

José Crossa ◽

Ravi P. Singh ◽

Francisco Pinto ◽

...

Keyword(s):

Grain Yield ◽

High Throughput ◽

Vegetation Indices ◽

Early Generation ◽

Breeding Programs ◽

Breeding Lines ◽

High Throughput Phenotyping ◽

Seed Increase ◽

Yield Trials ◽

Selection Of

ABSTRACTBreeding programs for wheat and many other crops require one or more generations of seed increase before replicated yield trials can be sown. Extensive phenotyping at this stage of the breeding cycle is challenging due to the small plot size and large number of lines under evaluation. Therefore, breeders typically rely on visual selection of small, unreplicated seed increase plots for the promotion of breeding lines to replicated yield trials. With the development of aerial high-throughput phenotyping technologies, breeders now have the ability to rapidly phenotype thousands of breeding lines for traits that may be useful for indirect selection of grain yield. We evaluated early generation material in the irrigated bread wheat (Triticum aestivum L.) breeding program at the International Maize and Wheat Improvement Center to determine if aerial measurements of vegetation indices assessed on small, unreplicated plots were predictive of grain yield. To test this approach, two sets of 1,008 breeding lines were sown both as replicated yield trials and as small, unreplicated plots during two breeding cycles. Vegetation indices collected with an unmanned aerial vehicle in the small plots were observed to be heritable and moderately correlated with grain yield assessed in replicated yield trials. Furthermore, vegetation indices were more predictive of grain yield than univariate genomic selection, while multi-trait genomic selection approaches that combined genomic information with the aerial phenotypes were found to have the highest predictive abilities overall. A related experiment showed that selection approaches for grain yield based on vegetation indices could be more effective than visual selection; however, selection on the vegetation indices alone would have also driven a directional response in phenology due to confounding between those traits. A restricted selection index was proposed for improving grain yield without affecting the distribution of phenology in the breeding population. The results of these experiments provide a promising outlook for the use of aerial high-throughput phenotyping traits to improve selection at the early-generation seed-limited stage of wheat breeding programs.

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HIGH THROUGHPUT PHENOTYPING OF PHYSIOLOGICAL GROWTH DYNAMICS FROM UAS-BASED 3D MODELING IN SOYBEAN

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-357-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 357-361 ◽

Cited By ~ 3

Author(s):

M. Herrero-Huerta ◽

K. M. Rainey

Keyword(s):

High Throughput ◽

Digital Camera ◽

Growth Dynamics ◽

Cost Effective ◽

Growing Season ◽

3D Modelling ◽

Crop Genetics ◽

High Throughput Phenotyping ◽

Research And Education ◽

Selection Of

<p><strong>Abstract.</strong> Nowadays, an essential tool to improve the efficiency of crop genetics is automated, precise and cost-effective phenotyping of the plants. The aim of this study is to generate a methodology for high throughput phenotyping the physiological growth dynamics of soybeans by UAS-based 3D modelling. During the 2018 growing season, a soybean experiment was performed at the Agronomy Center for Research and Education (ACRE) in West-Lafayette (Indiana, USA). Periodic images were acquired by G9X Canon compact digital camera on board senseFly eBee. The study area is reconstructed in 3D by Image-based modelling. Algorithms and techniques were combined to analyse growth dynamics of the crop via height variations and to quantify biomass. Results provide practical information for the selection of phenotypes for breeding.</p>

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Short communication: Applicability of feeding behaviour traits as high-throughput phenotyping methods for identifying protein-efficient pigs

10.1101/2020.11.13.381103 ◽

2020 ◽

Author(s):

Esther Ewaoluwagbemiga ◽

Giuseppe Bee ◽

Claudia Kasper

Keyword(s):

High Throughput ◽

Feed Intake ◽

Feeding Behaviour ◽

Carcass Composition ◽

Large White ◽

Automatic Feeder ◽

High Throughput Phenotyping ◽

The Relationship ◽

Feeding Behaviours ◽

Selection Of

AbstractThe objective of this study was to explore the potential of using automatically recorded feeding behaviour as a proxy for protein efficiency (PE) by investigating the relationship between feeding behaviour and PE. A total of 402 Swiss Large White pigs were used in this experiment (204 females and 198 castrated males). Pigs were fed ad libitum on a reduced protein diet (80% of standard) from 20kg to 100kg BW. Individual daily feed intake was monitored and carcass composition at slaughter was determined by dual-energy X-ray absorptiometry (DXA). The PE was calculated as the ratio of protein in the carcass (estimated by DXA) to the total protein consumed. Feeding behaviour traits monitored were daily feed intake (DFI; g/day), feed intake per visit (FIV; g/visit), number of daily visits (NDV; visits/day), duration of visits (DUV; min/visit), feeding rate (FR; g/min), and feeder occupation (FO; min/day). Regression analysis was used to estimate the relationship between PE and feeding behaviour, while correcting for the effects of sex, experimental series and age. Weak Pearson’s correlations (−0.25 to 0.12) were found between PE and feeding behaviour traits. Beta (β) estimates from this analysis for feeding behaviours were also very low (0.0093% to 0.087%). An increase in FR (g/min) will increase PE by 0.087% and an increase in DFI (g/day) will decrease PE by 0.0093%. In conclusion, feeding behaviours are not suitable for the identification of protein-efficient pigs, as estimates are negligible.ImplicationsThis study suggests that feeding behaviour traits recorded via automatic feeder are not reliable predictors of protein efficiency in Swiss Large White pigs receiving a protein-reduced diet. Despite the large differences in protein efficiency, only negligible changes in a range of feeding behaviours were observed. Hence, feeding behaviours are not suitable proxies for the high-throughput phenotyping of protein efficiency and the selection of live animals for use in nutrition experiments or for breeding.

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Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology

Frontiers in Plant Science ◽

10.3389/fpls.2021.768742 ◽

2022 ◽

Vol 12 ◽

Author(s):

Jing Zhou ◽

Eduardo Beche ◽

Caio Canella Vieira ◽

Dennis Yungbluth ◽

Jianfeng Zhou ◽

...

Keyword(s):

High Throughput ◽

Multispectral Imaging ◽

Large Population ◽

Image Features ◽

Environment Interaction ◽

Breeding Programs ◽

Soybean Yield ◽

Yield Trial ◽

High Throughput Phenotyping ◽

Selection Of

The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.

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