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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Yield prediction through integration of genetic, environment, and management data through deep learning
Daniel R Kick, Jason G. Wallace, James C. Schnable, et al.
G3 Genes Genomes Genetics (2023) Vol. 13, Iss. 4
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Multimodal deep learning methods enhance genomic prediction of wheat breeding
Abelardo Montesinos‐López, Carolina Rivera, Francisco Pinto, et al.
G3 Genes Genomes Genetics (2023) Vol. 13, Iss. 5
Open Access | Times Cited: 16
The role of artificial intelligence in crop improvement
Karlene L. Negus, Xianran Li, Stephen M. Welch, et al.
Advances in agronomy (2024), pp. 1-66
Closed Access | Times Cited: 5
Application of machine learning and genomics for orphan crop improvement
Tessa R. MacNish, Monica F. Danilevicz, Philipp E. Bayer, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America
Marco Lopez‐Cruz, Fernando Aguate, Jacob D. Washburn, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 14
Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials
Igor Kuivjogi Fernandes, Caio Canella Vieira, Kaio Olímpio das Graças Dias, et al.
Theoretical and Applied Genetics (2024) Vol. 137, Iss. 8
Open Access | Times Cited: 4
Deep learning methods improve genomic prediction of wheat breeding
Abelardo Montesinos‐López, Leonardo Crespo‐Herrera, Susanna Dreisigacker, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 3
A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada
Parisa Sarzaeim, Francisco Muñoz‐Arriola
Agronomy (2024) Vol. 14, Iss. 4, pp. 733-733
Open Access | Times Cited: 3
GxENet: Novel fully connected neural network based approaches to incorporate GxE for predicting wheat yield
Sheikh Jubair, Olivier Tremblay-Savard, Michael Domaratzki
Artificial Intelligence in Agriculture (2023) Vol. 8, pp. 60-76
Open Access | Times Cited: 9
Integrative multi-environmental genomic prediction in apple
Michaela Jung, Carles Quesada‐Traver, Morgane Roth, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 2
High temporal resolution unoccupied aerial systems phenotyping provides unique information between flight dates
Jacob D. Washburn, Alper Adak, Aaron J. DeSalvio, et al.
The Plant Phenome Journal (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 2
Dual sampling linear regression ensemble to predict wheat yield across growing seasons with hyperspectral sensing
Shuaipeng Fei, Shunfu Xiao, Jinyu Zhu, et al.
Computers and Electronics in Agriculture (2023) Vol. 216, pp. 108514-108514
Closed Access | Times Cited: 7
A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada
Parisa Sarzaeim, Francisco Muñoz‐Arriola
(2024)
Open Access | Times Cited: 1
Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates
Jacob D. Washburn, José Ignacio Varela, Alencar Xavier, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates
Jacob D. Washburn, José Ignacio Varela, Alencar Xavier, et al.
Genetics (2024)
Open Access | Times Cited: 1
Ensemble of best linear unbiased predictor, machine learning and deep learning models predict maize yield better than each model alone
Daniel R Kick, Jacob D. Washburn
in silico Plants (2023) Vol. 5, Iss. 2
Open Access | Times Cited: 2
Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
Igor Kuivjogi Fernandes, Caio Canella Vieira, Kaio Olímpio das Graças Dias, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
MegaLMM improves genomic predictions in new environments using environmental covariates
Haixiao Hu, Renaud Rincent, Daniel E. Runcie
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Evaluating a Collection of Agronomic Traits to Better Understand the Importance of Epistasis and Epistasis by Environment Interaction in Maize.
Baris Alaca
(2024)
Open Access
CropCare: Advanced Crop Management System with Intelligent Advisory and Machine Learning Techniques
Md. Jobayer Rahman, Shakil Ahmed, Swapnil Biswas, et al.
(2024), pp. 1356-1361
Closed Access
Data‐driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates
Aaron Kusmec, Cheng‐Ting Yeh, Patrick S. Schnable
New Phytologist (2024) Vol. 244, Iss. 2, pp. 618-634
Open Access
Improvements in Prediction Performance of Ensemble Approaches for Genomic Prediction in Crop Breeding
Shunichiro Tomura, Melanie J. Wilkinson, Mark Cooper, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Closed Access
The use of artificial intelligence in the production of genetically modified (GM) crops: a recent promising strategy for enhancing the acceptability of GM products ?
Gideon Sadikiel Mmbando
Deleted Journal (2024) Vol. 6, Iss. 11
Open Access
A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding
Osval A. Montesinos‐López, Moisés Chavira-Flores, Kiasmiantini, et al.
Genetics (2024)
Closed Access
Integrative multi-environmental genomic prediction in apple
Michaela Jung, Carles Quesada‐Traver, Morgane Roth, et al.
Horticulture Research (2024) Vol. 12, Iss. 2
Open Access
ConvCGP: A Convolutional Neural Network to Predict Genotypic Values of Rice Traits from Compressed Genome-Wide Polymorphisms
Tanzila Islam, Chyon Hae Kim, Hiroyuki Shimono, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
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