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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:

Phenomic data-facilitated rust and senescence prediction in maize using machine learning algorithms
Aaron J. DeSalvio, Alper Adak, Seth C. Murray, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 11

Showing 11 citing articles:

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding
Andrew W. Herr, Alper Adak, Matthew E. Carroll, et al.
Crop Science (2023) Vol. 63, Iss. 4, pp. 1722-1749
Open Access | Times Cited: 39
Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions
Alper Adak, Seth C. Murray, Steven L. Anderson
G3 Genes Genomes Genetics (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 32
Detecting novel plant pathogen threats to food system security by integrating the Plant Reactome and remote sensing
Seth C. Murray, Aart Verhoef, Alper Adak, et al.
Current Opinion in Plant Biology (2025) Vol. 83, pp. 102684-102684
Closed Access
Temporal field phenomics of transgenic maize events subjected to drought stress: Cross‐validation scenarios and machine learning models
Hélcio Duarte Pereira, Juliana Vieira Almeida Nonato, Rafaela Caroline Rangni Moltocaro Duarte, et al.
The Plant Phenome Journal (2025) Vol. 8, Iss. 1
Open Access
Algorithms for Plant Monitoring Applications: A Comprehensive Review
Giovanni Paolo Colucci, Paola Battilani, Marco Camardo Leggieri, et al.
Algorithms (2025) Vol. 18, Iss. 2, pp. 84-84
Open Access
UAV‐based RGB imagery and ground measurements for high‐throughput phenotyping of senescence and QTL mapping in bread wheat
Lei Li, Muhammad Adeel Hassan, Jie Song, et al.
Crop Science (2023) Vol. 63, Iss. 6, pp. 3292-3309
Closed Access | Times Cited: 9
Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem
Katerina L. Holan, C.H. White, Steven A. Whitham
Phytopathology (2024) Vol. 114, Iss. 5, pp. 990-999
Closed Access | Times Cited: 3
Cotton Chronology: Convolutional Neural Network Enables Single-Plant Senescence Scoring with Temporal Drone Images
Aaron J. DeSalvio, Mustafa Arık, Seth C. Murray, et al.
Research Square (Research Square) (2024)
Open Access | Times Cited: 1
High‐throughput phenotyping platforms for pulse crop biofortification
Amod Madurapperumage, M.Z. Naser, J. Lucas Boatwright, et al.
Plants People Planet (2024) Vol. 7, Iss. 1, pp. 49-61
Open Access | Times Cited: 1
Temporal field phenomics allows discovery of nature AND nurture, so can we saturate the phenome?
Seth C. Murray, Alper Adak, Aaron J. DeSalvio, et al.
Authorea (Authorea) (2022)
Open Access | Times Cited: 7
Genomic and phenomic approaches for studying Puccinia sorghi-maize interactions
Katerina L. Holan
(2023)
Open Access
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