scholarly journals Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

2020 ◽  
Vol 11 ◽  
Author(s):  
Tiago Bresolin ◽  
João R. R. Dórea
Keyword(s):  
Electromagnetic Radiation ◽  
High Throughput ◽  
Complex Traits ◽  
Infrared Spectrometry ◽  
Electromagnetic Spectrum ◽  
Wide Range ◽  
Level Information ◽  
High Throughput Phenotyping ◽  
The Impact ◽  
Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

scholarly journals Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

2020 ◽  
Vol 12 (3) ◽  
pp. 574 ◽  
Author(s):  
Yuncai Hu ◽  
Samuel Knapp ◽  
Urs Schmidhalter
Keyword(s):  
Grain Yield ◽  
Plant Breeding ◽  
High Throughput ◽  
Complex Traits ◽  
New Technologies ◽  
Early Selection ◽  
Spectral Indices ◽  
Wheat Genotypes ◽  
High Throughput Phenotyping ◽  
Spectral Sensing

Enhancing plant breeding to ensure global food security requires new technologies. For wheat phenotyping, only limited seeds and resources are available in early selection cycles. This forces breeders to use small plots with single or multiple row plots in order to include the maximum number of genotypes/lines for their assessment. High-throughput phenotyping through remote sensing may meet the requirements for the phenotyping of thousands of genotypes grown in small plots in early selection cycles. Therefore, the aim of this study was to compare the performance of an unmanned aerial vehicle (UAV) for assessing the grain yield of wheat genotypes in different row numbers per plot in the early selection cycles with ground-based spectral sensing. A field experiment consisting of 32 wheat genotypes with four plot designs (1, 2, 3, and 12 rows per plot) was conducted. Near infrared (NIR)-based spectral indices showed significant correlations (p < 0.01) with the grain yield at flowering to grain filling, regardless of row numbers, indicating the potential of spectral indices as indirect selection traits for the wheat grain yield. Compared with terrestrial sensing, aerial-based sensing from UAV showed consistently higher levels of association with the grain yield, indicating that an increased precision may be obtained and is expected to increase the efficiency of high-throughput phenotyping in large-scale plant breeding programs. Our results suggest that high-throughput sensing from UAV may become a convenient and efficient tool for breeders to promote a more efficient selection of improved genotypes in early selection cycles. Such new information may support the calibration of genomic information by providing additional information on other complex traits, which can be ascertained by spectral sensing.

scholarly journals Heliaphen, an outdoor high-throughput phenotyping platform designed to integrate genetics and crop modeling

2018 ◽  
Author(s):  
Florie Gosseau ◽  
Nicolas Blanchet ◽  
Didier Varès ◽  
Philippe Burger ◽  
Didier Campergue ◽  
...  
Keyword(s):  
Simulation Model ◽  
High Throughput ◽  
Simulation Study ◽  
Large Population ◽  
Model Parameters ◽  
Crop Simulation Model ◽  
Continental Scale ◽  
Crop Simulation ◽  
High Throughput Phenotyping ◽  
The Impact

AbstractHeliaphen is an outdoor pot platform designed for high-throughput phenotyping. It allows automated management of drought scenarios and plant monitoring during the whole plant cycle. A robot moving between plants growing in 15L pots monitors plant water status and phenotypes plant or leaf morphology, from which we can compute more complex traits such as the response of leaf expansion (LE) or plant transpiration (TR) to water deficit. Here, we illustrate the platform capabilities for sunflower on two practical cases: a genetic and genomics study for the response to drought of yield-related traits and a simulation study, where we use measured parameters as inputs for a crop simulation model. For the genetic study, classical measurements of thousand-kernel weight (TKW) were done on a sunflower bi-parental population under water stress and control conditions managed automatically. The association study using the TKW drought-response highlighted five genetic markers. A complementary transcriptomic experiment identified closeby candidate genes differentially expressed in the parental backgrounds in drought conditions. For the simulation study, we used the SUNFLO crop simulation model to assess the impact of two traits measured on the platform (LE and TR) on crop yield in a large population of environments. We conducted simulations in 42 contrasted locations across Europe and 21 years of climate data. We defined the pattern of abiotic stresses occurring at this continental scale and identified ideotypes (i.e. genotypes with specific traits values) that are more adapted to specific environment types. This study exemplifies how phenotyping platforms can help with the identification of the genetic architecture of complex response traits and the estimation of eco-physiological model parameters in order to define ideotypes adapted to different environmental conditions.

scholarly journals Secret of radiation protection and anti-static clothing

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Junxiu Zhang ◽  
Haiyang Tang ◽  
Shiguo Chen
Keyword(s):  
Electromagnetic Radiation ◽  
Radiation Protection ◽  
Static Electricity ◽  
Basic Principles ◽  
Daily Lives ◽  
Specific Analysis ◽  
Wide Range ◽  
Working Principle ◽  
Definition Of ◽  
The Impact

In the current era, electromagnetic radiation is everywhere. Every day electromagnetic radiation and static electricity caused by a variety of hazards. So, anti-electromagnetic radiation and anti-static awareness gradually enjoys popular support, more attention are gained by people on the anti-electromagnetic radiation and anti-static. This caused radiation protection and anti-static clothing industry’s rise by the day. Radiation protection and anti-static clothing will enter various households to provide a certain amount of protection to the people's health. We discuss two parts in this paper, specifically from the effects of the electromagnetic radiation and electrostatic effects which started on radiation clothing and anti-static clothing. The main contents of this paper are as follows: The first part of the definition of electromagnetic radiation and its brief introduction, while explaining the types of electromagnetic radiation and electromagnetic radiation sources in daily lives, followed by the emphasis of serious harms on electromagnetic radiation on human health It is precisely because of electromagnetic radiation on people's lives have serious threat, that makes the development of radiation protection. This follows the basic introduction of the radiation suit and the development of radiation protection clothings. The development of radiation protection suits is an established industry. Materials made of radiation protection are constantly changing, but their basic working principle has not changed. Followed by the introduction of the basic principles of radiation protection clothings, we theoretically present specific analysis and demonstration. However, the theoretical analysis and practice is often consists a certain gap, so we highlight a few actual situations on the impact of radiation protection clothings. Finally, we present a simple discussion on wide range of applications of radiation protection clothings. The thought process of second part is similar as the first part, respectively, we introduce the health hazards and the impact on people's lives of electrostatic effect and static electricity . Followed by that it is the basic principles, relevant analysis and discussion of anti-static clothing Finally, we provide the detailed explanation of the application of anti-static clothing.

scholarly journals Conventional and hyperspectral time-series imaging of maize lines widely used in field trials

2017 ◽  
Author(s):  
Zhikai Liang ◽  
Piyush Pandey ◽  
Vincent Stoerger ◽  
Yuhang Xu ◽  
Yumou Qiu ◽  
...  
Keyword(s):  
High Throughput ◽  
Measurement Errors ◽  
Hyperspectral Image ◽  
Image Data ◽  
Field Trials ◽  
Plant Phenotyping ◽  
Wide Range ◽  
High Throughput Phenotyping ◽  
University Of Nebraska Lincoln ◽  
The Cost

ABSTRACTMaize (Zea mays ssp. mays) is one of three crops, along with rice and wheat, responsible for more than 1/2 of all calories consumed around the world. Increasing the yield and stress tolerance of these crops is essential to meet the growing need for food. The cost and speed of plant phenotyping is currently the largest constraint on plant breeding efforts. Datasets linking new types of high throughput phenotyping data collected from plants to the performance of the same genotypes under agronomic conditions across a wide range of environments are essential for developing new statistical approaches and computer vision based tools. A set of maize inbreds – primarily recently off patent lines – were phenotyped using a high throughput platform at University of Nebraska-Lincoln. These lines have been previously subjected to high density genotyping, and scored for a core set of 13 phenotypes in field trials across 13 North American states in two years by the Genomes to Fields consortium. A total of 485 GB of image data including RGB, hyperspectral, fluorescence and thermal infrared photos has been released. Correlations between image-based measurements and manual measurements demonstrated the feasibility of quantifying variation in plant architecture using image data. However, naive approaches to measuring traits such as biomass can introduce nonrandom measurement errors confounded with genotype variation. Analysis of hyperspectral image data demonstrated unique signatures from stem tissue. Integrating heritable phenotypes from high-throughput phenotyping data with field data from different environments can reveal previously unknown factors influencing yield plasticity.

Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS

2021 ◽  
Vol 4 (1) ◽  
pp. 1-19
Author(s):  
Lisa Bastarache
Keyword(s):  
High Throughput ◽  
Association Studies ◽  
International Classification Of Diseases ◽  
Complex Data ◽  
Common Genetic Variants ◽  
Wide Range ◽  
Classification Of Diseases ◽  
High Throughput Phenotyping ◽  
Data Source ◽  
Electronic Health

Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out of this highly complex data source is challenging. Phecodes represent one strategy for defining phenotypes for research using EHR data. They are a high-throughput phenotyping tool based on ICD (International Classification of Diseases) codes that can be used to rapidly define the case/control status of thousands of clinically meaningful diseases and conditions. Phecodes were originally developed to conduct phenome-wide association studies to scan for phenotypic associations with common genetic variants. Since then, phecodes have been used to support a wide range of EHR-based phenotyping methods, including the phenotype risk score. This review aims to comprehensively describe the development, validation, and applications of phecodes and suggest some future directions for phecodes and high-throughput phenotyping.

scholarly journals Robust colony recognition for high-throughput growth analysis from suboptimal low-magnification brightfield micrographs

2018 ◽  
Author(s):  
Yevgeniy Plavskin ◽  
Shuang Li ◽  
Naomi Ziv ◽  
Sasha F. Levy ◽  
Mark L. Siegal
Keyword(s):  
High Throughput ◽  
Time Lapse ◽  
Manual Curation ◽  
Technological Advances ◽  
Wide Range ◽  
Colony Recognition ◽  
Gradient Based ◽  
High Throughput Phenotyping ◽  
High Resolution Images ◽  
Object Center

AbstractNew technological advances have enabled high-throughput phenotyping at the single-cell level. However, analyzing the large amount of data automatically and accurately is a great challenge. Currently available software achieves cell and colony tracking through the use of either manual curation of images, which is time consuming, or high-resolution images requiring specialized microscopy setups or fluorescence, which limits applicability and results in greatly decreased experimental throughput. Here we introduce a new algorithm, Processing Images Easily (PIE), that automatically tracks colonies of the yeast Saccharomyces cerevisiae in low-magnification brightfield images by combining adaptive object-center detection with gradient-based object-outline detection. We tested the performance of PIE on low-magnification brightfield time-lapse images. PIE recognizes colony outlines very robustly and accurately across a wide range of image brightnesses and focal depths. We show that PIE allows for unbiased and precise measurement of growth rates in a large number (>90,000) of microcolonies in a single time-lapse experiment.

scholarly journals Analyses Regarding Possible Effects of Electromagnetic Waves Emitted on Agricultural Productions

2021 ◽  
Vol 8 (1) ◽  
pp. 43
Author(s):  
Lavinia Popescu ◽  
Adela Sorinela Safta
Keyword(s):  
Electromagnetic Radiation ◽  
Empirical Research ◽  
Electromagnetic Waves ◽  
Quantitative Methods ◽  
New Technologies ◽  
Technical Solution ◽  
Agricultural Systems ◽  
Wide Range ◽  
Digital Agriculture ◽  
The Impact

This paper aims to highlight in a unique way the effects on sustainable agricultural systems due to the global growth of the development of the communications system. From the very beginning, we discussed the disruptions of climate change and, in particular, the risk of disaster, which causes long academic debates regarding electromagnetic radiation. During the study, we conducted empirical research using the wide range of receptors and a detailed determination bridge; we used quantitative methods to collect the processes and analyze the data and information incorporated to formulate observations and conclusions. The aim of this paper is to highlight an assessment for obtaining an answer to the possible causes of climate disturbances in agriculture, given the epistemic uncertainty. Mainly, we will reflect on the effects that interfere with the level of electromagnetic radiation produced by antennas. Opening advanced technologies with new satellite capabilities is expensive, so now, the density of high-power data transmissions useful in digital agriculture is the technical solution of operators. Our empirical research experimentally analyzes the data we collect in the statistical monitoring of electromagnetic waves, investigating to what extent the electromagnetic radiation affects agricultural systems concerned with trying to sequester C from the soil and reduce greenhouse effects. The paper defines and presents the evolution of the impact of new technologies developed in order to facilitate the implementation of intelligent agricultural solutions, admitting that the opening of new technologies facilitates the creation of the economically and socially interconnected global community (McLuchan, 1973). Taking into account the intensification of the use of digital agriculture, the analysis proposed for research is a topic that needs updating, and in this sense, it is necessary that this research be analyzed from environmental perspectives.

scholarly journals Bottlenecks and opportunities in field-based high-throughput phenotyping for heat and drought stress

2021 ◽  
Author(s):  
Nathan T Hein ◽  
Ignacio A Ciampitti ◽  
S V Krishna Jagadish
Keyword(s):  
Remote Sensing ◽  
Drought Stress ◽  
High Throughput ◽  
Complex Traits ◽  
Crop Improvement ◽  
Grain Filling ◽  
Time Of Day ◽  
Water Soluble ◽  
High Throughput Phenotyping ◽  
Improvement Programs

Abstract Flowering and grain-filling stages are highly sensitive to heat and drought stress exposure, leading to significant loss in crop yields. Therefore, phenotyping to enhance resilience to these abiotic stresses is critical for sustaining genetic gains in crop improvement programs. However, traditional methods for screening traits related to these stresses are slow, laborious, and often expensive. Remote sensing provides opportunities to introduce low-cost, less-biased, high-throughput phenotyping methods to capture large genetic diversity to facilitate enhancement of stress resilience in crops. This review focuses on four key physiological traits or processes that are critical in understanding crop responses to drought and heat stress during reproductive and grain-filling periods. Specifically, these traits include: i) time-of-day of flowering, to escape these stresses during flowering, ii) optimizing photosynthetic efficiency, iii) storage and translocation of water-soluble carbohydrates, and iv) yield and yield components to provide in-season yield estimates. An overview of current advances in remote sensing in capturing these traits, limitations with existing technology and future direction of research to develop high-throughput phenotyping approaches for these traits are discussed in this review. In the future, phenotyping these complex traits will require sensor advancement, high-quality imagery combined with machine learning methods, and efforts in transdisciplinary science to foster integration across disciplines.

scholarly journals SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data

2020 ◽  
Vol 11 ◽  
Author(s):  
Soumyashree Kar ◽  
Vincent Garin ◽  
Jana Kholová ◽  
Vincent Vadez ◽  
Surya S. Durbha ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area ◽  
High Throughput ◽  
Missing Values ◽  
Time Series Data ◽  
Series Data ◽  
Genotypic Differences ◽  
Wide Range ◽  
High Throughput Phenotyping ◽  
Over Time

The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.

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