Seth C. Murray
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View article: Distributional Data Analysis Uncovers Hundreds of Novel and Heritable Phenomic Features from Temporal Cotton and Maize Drone Imagery
Distributional Data Analysis Uncovers Hundreds of Novel and Heritable Phenomic Features from Temporal Cotton and Maize Drone Imagery Open
Genomic and phenomic analyses suggest additional heritable phenomic features can improve modeling of important end traits like senescence or yield. Field phenotyping generally uses trait values averaged across individual experimental units…
View article: Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients
Phenotypic plasticity in maize grain yield: Genetic and environmental insights of response to environmental gradients Open
Understanding genotype‐by‐environment (G × E) interactions that underlie phenotypic variation, when observed for complex traits in multi‐environment trials, is important for biological discovery and for crop improvement. The regression‐on‐…
View article: Harmonized Population and Labor Force Statistics
Harmonized Population and Labor Force Statistics Open
The official labor force statistics often exhibit discontinuities in January, when updated population estimates are incorporated into the Current Population Survey (CPS) for the current year but are not revised backward through history. We…
View article: Designing a nitrogen-efficient cold-tolerant maize for modern agricultural systems
Designing a nitrogen-efficient cold-tolerant maize for modern agricultural systems Open
Maize (Zea mays L.) is the world's most productive grain crop and a cornerstone of global food supply. However, in temperate agricultural systems, maize exhibits 2 key anomalies. First, as a tropical species, maize cannot be planted in the…
View article: The value and broader impacts of agricultural and environmental scientific meetings
The value and broader impacts of agricultural and environmental scientific meetings Open
The socioeconomic value of content presented at the ASA‐CSSA‐SSSA (where ASA‐CSSA‐SSSA is American Society of Agronomy–Crop Science Society of America–Soil Science Society of America) Annual Meetings from 2014 to 2023 is estimated at $64.2…
View article: Deep learning‐based high‐throughput detection of flowered maize ( <i>Zea mays</i> L.) plots from UAS imagery across environments
Deep learning‐based high‐throughput detection of flowered maize ( <i>Zea mays</i> L.) plots from UAS imagery across environments Open
Flowering time is a critical phenological trait in maize ( Zea mays L.) breeding programs. Traditional measurements for assessing flowering time involve semi‐subjective and labor‐intensive manual observation, limiting the scale and efficie…
View article: Provision of online information and resources for resistance training in Australian youth sports: A scoping review
Provision of online information and resources for resistance training in Australian youth sports: A scoping review Open
Objectives This study aimed to identify and synthesize online information/resources related to resistance training provided by governing bodies of the ten most popular youth sports in Australia. Design A scoping review. Methods The top 10 …
View article: FIELDimagePy: A tool to estimate zonal statistics from an image, bounded by one or multiple polygons
FIELDimagePy: A tool to estimate zonal statistics from an image, bounded by one or multiple polygons Open
Vegetation indices have become an indispensable tool in remote sensing‐based agricultural research. A recent area of advancement in agricultural remote sensing research is in high‐throughput phenotyping, often conducted on a plot by plot b…
View article: High temporal resolution unoccupied aerial systems phenotyping provides unique information between flight dates
High temporal resolution unoccupied aerial systems phenotyping provides unique information between flight dates Open
Unoccupied aerial systems (UAS, unoccupied aerial vehicle, and drone) are high‐throughput phenotyping tools that can provide transformational insights into biological and agricultural research, but practical and scientific questions remain…
View article: Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence
Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence Open
Summary Senescence is a highly ordered degenerative biological process that affects yield and quality in annuals and perennials. Images from 14 unoccupied aerial system (UAS, UAV, drone) flights captured the senescence window across two ex…
View article: Deep Learning-Based High-Throughput Phenotyping Of Maize (<i>Zea mays</i>L.) Tasseling From Uas Imagery Across Environments
Deep Learning-Based High-Throughput Phenotyping Of Maize (<i>Zea mays</i>L.) Tasseling From Uas Imagery Across Environments Open
A bstract Flowering time is a critical phenological trait in maize ( Zea mays L.) breeding programs. Traditional measurements for assessing flowering time involve semi-subjective and labor-intensive manual observation, limiting the scale a…
View article: Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize
Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize Open
Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environment…
View article: Near‐infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments
Near‐infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments Open
For nearly two decades, genomic prediction and selection have supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies for predicting complex traits in maize have recently pro…
View article: Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations
Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations Open
Summary Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development, requiring further modeling techniques. In this study, we analyzed the growth patt…
View article: Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images
Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images Open
Senescence is a degenerative biological process that affects most organisms. Timing of senescence is critical for annual and perennial crops and is associated with yield and quality. Tracking time-series senescence data has previously requ…
View article: Temporal image sandwiches enable link between functional data analysis and deep learning for single-plant cotton senescence
Temporal image sandwiches enable link between functional data analysis and deep learning for single-plant cotton senescence Open
Abstract. Senescence is a highly ordered biological process involving resource redistribution away from ageing tissues that affects yield and quality in annuals and perennials. Images from 14 unmanned/unoccupied/uncrewed aerial system/vehi…
View article: Crop Science Futurology: A Data-Driven Approach Through Phenomic, Genomic and Enviromic Insights
Crop Science Futurology: A Data-Driven Approach Through Phenomic, Genomic and Enviromic Insights Open
In recent years, the use of field-based high-throughput phenotyping (FHTP) has surged across diverse disciplines. Particularly, it has gained significant traction in agricultural research, enabling scientists to efficiently gather extensiv…
View article: Facilitating community unoccupied aerial systems (UAS, drone) knowledge, communication, and data processing across agriculture
Facilitating community unoccupied aerial systems (UAS, drone) knowledge, communication, and data processing across agriculture Open
Unoccupied / Unmanned / Uncrewed Aerial Systems (UAS, also known as drones) are tools that can provide field-based phenotyping and phenomics derived insights into plant breeding, biology, genetics and agronomy. There are many important, ye…
View article: Near Infrared Reflectance Spectroscopy Phenomic and Genomic Prediction of Maize Agronomic and Composition Traits Across Environments
Near Infrared Reflectance Spectroscopy Phenomic and Genomic Prediction of Maize Agronomic and Composition Traits Across Environments Open
For nearly two decades, genomic selection has supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies helping to predict complex traits in maize have proven beneficial when i…
View article: Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding
Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding Open
High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportuni…
View article: Genomes to Fields 2022 Maize Genotype by Environment Prediction Competition
Genomes to Fields 2022 Maize Genotype by Environment Prediction Competition Open
Objectives: The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (G x E) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize G x E project field trials, leveraging the datasets previously …
View article: 2018–2019 field seasons of the Maize Genomes to Fields (G2F) G x E project
2018–2019 field seasons of the Maize Genomes to Fields (G2F) G x E project Open
Objectives This report provides information about the public release of the 2018–2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines acros…
View article: 2020-2021 Field Seasons of Maize G x E Project within Maize Genomes to Fields Initiative
2020-2021 Field Seasons of Maize G x E Project within Maize Genomes to Fields Initiative Open
Objectives: This release note describes the Maize G x E project datasets within the Genomes to Fields (G2F) Initiative. The Maize G x E project aims to understand genotype by environment (G × E) interactions and use the information collect…
View article: Current Challenges and Future of Agricultural Genomes to Phenomes in the U.S.
Current Challenges and Future of Agricultural Genomes to Phenomes in the U.S. Open
Dramatic improvements in measuring genetic variation across agriculturally relevant populations (genomics) must be matched by improvements in identifying and measuring relevant trait variation in such populations across many environments (…
View article: Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights
Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights Open
Unoccupied aerial systems (UAS) based high throughput phenotyping studies require further investigation to combine different environments and planting times into one model. Here 100 elite breeding hybrids of maize ( Zea mays L.) were evalu…