Jordan Ubbens

844 total citations
9 papers, 499 citations indexed

About

Jordan Ubbens is a scholar working on Plant Science, Ecology and Genetics. According to data from OpenAlex, Jordan Ubbens has authored 9 papers receiving a total of 499 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Plant Science, 4 papers in Ecology and 3 papers in Genetics. Recurrent topics in Jordan Ubbens's work include Smart Agriculture and AI (5 papers), Remote Sensing in Agriculture (4 papers) and Species Distribution and Climate Change (2 papers). Jordan Ubbens is often cited by papers focused on Smart Agriculture and AI (5 papers), Remote Sensing in Agriculture (4 papers) and Species Distribution and Climate Change (2 papers). Jordan Ubbens collaborates with scholars based in Canada, United States and United Kingdom. Jordan Ubbens's co-authors include Ian Stavness, Mikolaj Cieslak, Przemysław Prusinkiewicz, Isobel A. P. Parkin, Jana Ebersbach, Christina Eynck, Andrew Sharpe, Mitchell J. Feldmann, Joseph L. Gage and Lisa M. Lix and has published in prestigious journals such as SHILAP Revista de lepidopterología, Frontiers in Plant Science and Journal of Anxiety Disorders.

In The Last Decade

Jordan Ubbens

8 papers receiving 483 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jordan Ubbens Canada	7	390	189	79	71	56	9	499
Sruti Das Choudhury United States	12	327 0.8×	163 0.9×	67 0.8×	43 0.6×	65 1.2×	19	489
Chao Ruan China	13	353 0.9×	301 1.6×	193 2.4×	25 0.4×	84 1.5×	39	586
Sarah E. Jones United States	7	481 1.2×	203 1.1×	218 2.8×	108 1.5×	29 0.5×	11	720
Wesley Banks United States	3	278 0.7×	90 0.5×	61 0.8×	19 0.3×	14 0.3×	4	363
Jean-Michel Pape Germany	7	420 1.1×	206 1.1×	62 0.8×	72 1.0×	69 1.2×	11	531
Koushik Nagasubramanian United States	8	540 1.4×	209 1.1×	225 2.8×	72 1.0×	31 0.6×	10	674
Tianle Yang China	12	245 0.6×	170 0.9×	95 1.2×	28 0.4×	63 1.1×	29	388
Conghao Wang China	6	239 0.6×	139 0.7×	51 0.6×	23 0.3×	62 1.1×	8	313
Feiyu Zhu United States	12	309 0.8×	103 0.5×	47 0.6×	54 0.8×	89 1.6×	27	439
Alexandra J. Townsend United Kingdom	6	371 1.0×	115 0.6×	39 0.5×	35 0.5×	38 0.7×	6	473

Countries citing papers authored by Jordan Ubbens

Since Specialization

Citations

This map shows the geographic impact of Jordan Ubbens's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jordan Ubbens with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jordan Ubbens more than expected).

Fields of papers citing papers by Jordan Ubbens

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jordan Ubbens. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jordan Ubbens. The network helps show where Jordan Ubbens may publish in the future.

Co-authorship network of co-authors of Jordan Ubbens

This figure shows the co-authorship network connecting the top 25 collaborators of Jordan Ubbens. A scholar is included among the top collaborators of Jordan Ubbens based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jordan Ubbens. Jordan Ubbens is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Ubbens, Jordan, Ian Stavness, Michael P. Pound, & Wei Guo. (2025). Deep learning in plant phenotyping: the first ten years. Plant Phenomics. 7(4). 100062–100062.

2.

Ubbens, Jordan, Mitchell J. Feldmann, Ian Stavness, & Andrew Sharpe. (2022). Quantitative evaluation of nonlinear methods for population structure visualization and inference. G3 Genes Genomes Genetics. 12(9). 2 indexed citations

3.

Feldmann, Mitchell J., et al.. (2021). Images carried before the fire: The power, promise, and responsibility of latent phenotyping in plants. SHILAP Revista de lepidopterología. 4(1). 9 indexed citations

4.

Ubbens, Jordan, Isobel A. P. Parkin, Christina Eynck, Ian Stavness, & Andrew Sharpe. (2021). Deep neural networks for genomic prediction do not estimate marker effects. The Plant Genome. 14(3). e20147–e20147. 16 indexed citations

5.

Ubbens, Jordan, Mikolaj Cieslak, Przemysław Prusinkiewicz, et al.. (2020). Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies. Plant Phenomics. 2020. 5801869–5801869. 29 indexed citations

6.

Ubbens, Jordan, et al.. (2019). ProTractor: A Lightweight Ground Imaging and Analysis System for Early-Season Field Phenotyping. 2629–2638. 8 indexed citations

7.

Ubbens, Jordan, Mikolaj Cieslak, Przemysław Prusinkiewicz, & Ian Stavness. (2018). The use of plant models in deep learning: an application to leaf counting in rosette plants. Plant Methods. 14(1). 6–6. 178 indexed citations

8.

Ubbens, Jordan & Ian Stavness. (2017). Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks. Frontiers in Plant Science. 8. 1190–1190. 236 indexed citations

9.

Carleton, R. Nicholas, Chris Oriet, Sophie Duranceau, et al.. (2015). A randomized controlled trial of attention modification for social anxiety disorder. Journal of Anxiety Disorders. 33. 35–44. 21 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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