Genomes To Fields (G2F) Publications
Symbol Legend: * Authors contributed equally to the article
The Plant Phenome Journal. 2024, 7(1): e20113 | 29 July 2024
doi:10.1002/ppj2.20113
High temporal resolution unoccupied aerial systems phenotyping provides unique information between flight dates
Frontiers in Plant Sciences. 2024, 15: 1408047 | 24 July 2024
doi:10.3389/fpls.2024.1408047
Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments
G3: Genes|Genomes|Genetics. 2024, jkae092 | 22 May 2024
doi:10.1093/g3journal/jkae092
Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize
Journal of Experimental Botany. 2024: 73(15), 5336-5354, | 8 April 2024
doi:10.1093/jxb/erac146
Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability
Agronomy. 2024: 14(4), 733 | 2 April 2024
doi:10.3390/agronomy14040733
A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada
Journal of Agriculture, Food Systems, and Comminity Development. 2024: 13(2) | 6 March 2024
doi:10.5304/jafscd.2024.132.008
Participatory breeding in organic systems: Experiences from maize case studies in the United States
bioRχiv. 2024.02.08.579534 | 12 February 2024
doi:10.1101/2024.02.08.579534
Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
Nature Communications. 2023, 14: 6904 | 30 October 2023
doi:10.1038/s41467-023-42687-4
Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America
BMC Research Notes. 2023, 16: 219 | 14 September 2023
doi:10.1186/s13104-023-06430-y
2020-2021 field seasons of Maize GxE project within the Genomes to Fields Initiative
Earth System Science Data. 2023: 15(9), 3963-3990 | 6 September 2023
doi:10.5194/essd-15-3963-2023
CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada
BMC Research Notes. 2023, 16:148 | 17 July 2023
doi:10.1186/s13104-023-06421-z
Genomes to Fields 2022 Maize Genotype by Environment Prediction Competition
BMC Genomic Data. 2023, 24:24 | 25 May 2023
doi:10.1186/s12863-023-01129-2
2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project
arXiv. 2211.05986 | 11 November 2022
doi:10.48550/arXiv.2211.05986
DeepG2P: Fusing Multi-Modal Data to Improve Crop Production
Crop Science. 2022, 63(2): 511-534 | 5 October 2022
doi:10.1002/csc2.20857
Calibration of a crop growth model in APSIM for 15 publicly available corn hybrids in North America
G3: Genes|Genomes|Genetics. 2022, jkac226 | 19 September 2022
doi:10.1093/g3journal/jkac226
learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data
bioRχiv. 2022.07.29.502051 | 30 June 2022
doi:10.1101/2022.07.29.502051
Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning
Proceedings Volume 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V. 1141402 | 13 May 2022
doi:10.1117/12.2558729
Investigating the potential of satellite imagery for high-throughput field phenotyping applications
arXiv. 2203.15629 | 29 March 2022
doi:10.48550/arXiv.2203.15629
Stochastic Conservative Contextual Linear Bandits
G3: Genes|Genomes|Genetics. 2022, 12(2) | 28 December 2021
doi:10.1093/g3journal/jkab440
Environment-specific genomic prediction ability in maize using environmental covariates depends on environmental similarity to training data
Frontiers in Plant Sciences. 2021, 12: 2529 | 11 November 2021
doi:10.3389/fpls.2021.699589
Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks
Theoretical and Applied Genetics. 2021, 134: 3997-4001 | 27 August 2021
doi:10.1007/s00122-021-03943-7
Predicting phenotypes from genetic, environment, management, and historical data using CNNs
Frontiers in Genetics. 2021, 11: 1819 | 8 Mach 2021
doi:10.3389/fgene.2020.592769
Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield within the Genomes to Fields Maize Project
G3: Genes|Genomes|Genetics. 2021, 11(2): jkaa050 | 4 January 2021
doi:10.1093/g3journal/jkaa050
The Importance of Dominance and Genotype-by-Environment Interactions on Grain Yield Variation in a Large-Scale Public Cooperative Maize Experiment
Transactions of the ASABE. 64(3): 879-891 | 1 January 2021
doi:10.13031/trans.14197
Can High-Resolution Satellite Multispectral Imagery Be Used to Phenotype Canopy Traits and Yield Potential in Field Conditions?
Theoretical and Applied Genetics. 2020 | 1 July 2020
doi:10.1007/s00122-020-03637-6
Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize
The Plant Phenome Journal. 2020, 3(1): e20004 | 15 May 2020
doi:10.1002/ppj2.20004
ImageBreed: Open-access plant breeding web-database for image-based phenotyping
Field Crops Research. 2020, 249: 107737 | 1 April 2020
doi:10.1016/j.fcr.2020.107737
Stalk bending strength is strongly associated with maize stalk lodging incidence across multiple environments
BMC Research Notes. 2020, 13: 71 | 12 February 2020
doi:10.1186/s13104-020-4922-8
Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets
Crop Science. 2020, 60(1): 62-81| 19 December 2019
doi:10.2135/cropsci2019.05.0294
Relative utility of agronomic, phenological, and morphological traits for assessing genotype-by-environment interaction in maize inbreds
Frontiers in Plant Science. 2019, 10: 1550 | 12 December 2019
doi:10.3389/fpls.2019.01550
Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data
The Plant Genome Journal. 2019, 2(1): 190011 | 22 November 2019
doi:10.2135/tppj2019.07.0011
In-Field whole-plant maize architecture characterized by subcanopy rovers and latent space phenotyping
The Plant Phenome Journal. 2019, 2(1) | 7 November 2019
doi:10.2135/tppj2019.03.0006
Autonomous detection of plant disease symptoms directly from aerial imagery
Remote Sensing. 2019, 11: 2209 | 29 July 2019
doi:10.3390/rs11192209
Quantitative phenotyping of northern leaf blight in uav images using deep learning
Plant Phenome Journal. 2019, 2: 190004 | 11 July 2019
doi:10.2135/tppj2019.02.0004
Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems
Crop Science. 2019, 59(4): 1406-1410 | 27 June 2019
doi:10.2135/cropsci2019.02.0071
Idea factory: the maize genomes to fields initiative
Computers and Electronics in Agriculture. 2019, 160: 71-81 | 16 March 2019
doi:10.1016/j.compag.2019.03.009
NU-Spidercam: A large-scale, cable-driven, integrated sensing and robotic system for advanced phenotyping, remote sensing, and agronomic research
BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. 2018, pp. 155-164 | August 29 - September 1, 2018
doi:10.1145/3233547.3233593
Detecting divergent subpopulations in phenomics data using interesting flares
BMC Research Notes. 2018, 11: 452 | 9 July 2018
doi:10.1186/s13104-018-3508-1
Maize genomes to fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets
BMC Research Notes. 2018, 11: 440 | 3 July 2018
doi:10.1186/s13104-018-3548-6
Image set for deep learning: field images of maize annotated with disease symptoms
Genomes to Fields NIFA Fact Workshop Whitepaper | January 28-30, 2018
2018 NIFA FACT workshop: high throughput, field-based phenotyping technologies for the genomes to fields (G2F) initiative
Nature Communications. 2017, 8(1): 1348 | 7 November 2017
doi:10.1038/s41467-017-01450-2
The effect of artificial selection on phenotypic plasticity in maize
Phytopathology. 2017, 107: 1426-1432 | 24 August 2017
doi:10.1094/PHYTO-11-16-0417-R