Kyle Parmley

474 total citations
8 papers, 275 citations indexed

About

Kyle Parmley is a scholar working on Plant Science, Genetics and Ecology. According to data from OpenAlex, Kyle Parmley has authored 8 papers receiving a total of 275 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Plant Science, 3 papers in Genetics and 1 paper in Ecology. Recurrent topics in Kyle Parmley's work include Soybean genetics and cultivation (6 papers), Genetics and Plant Breeding (3 papers) and Genetic Mapping and Diversity in Plants and Animals (3 papers). Kyle Parmley is often cited by papers focused on Soybean genetics and cultivation (6 papers), Genetics and Plant Breeding (3 papers) and Genetic Mapping and Diversity in Plants and Animals (3 papers). Kyle Parmley collaborates with scholars based in United States. Kyle Parmley's co-authors include Asheesh K. Singh, Soumik Sarkar, Baskar Ganapathysubramanian, R. Higgins, Koushik Nagasubramanian, Arti Singh, Talukder Z. Jubery, Seyed Vahid Mirnezami, Fernando E. Miguez and Mariana V. Chiozza and has published in prestigious journals such as Scientific Reports, Frontiers in Plant Science and Field Crops Research.

In The Last Decade

Kyle Parmley

6 papers receiving 270 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kyle Parmley United States	6	235	58	57	40	21	8	275
Maximilian J. Feldman United States	5	202 0.9×	48 0.8×	75 1.3×	19 0.5×	52 2.5×	6	257
Brandi Sigmon United States	6	212 0.9×	78 1.3×	94 1.6×	65 1.6×	31 1.5×	7	278
Hsiang Sing Naik United States	4	290 1.2×	56 1.0×	70 1.2×	54 1.4×	36 1.7×	4	323
Afef Marzougui United States	10	209 0.9×	24 0.4×	133 2.3×	39 1.0×	20 1.0×	15	291
Yuvraj Chopra United States	3	258 1.1×	104 1.8×	43 0.8×	24 0.6×	16 0.8×	4	283
Gengshen Chen China	12	327 1.4×	181 3.1×	53 0.9×	27 0.7×	56 2.7×	18	387
Johnathon M. Shook United States	5	182 0.8×	31 0.5×	46 0.8×	32 0.8×	19 0.9×	6	218
César Lizárraga United States	4	188 0.8×	32 0.6×	74 1.3×	21 0.5×	47 2.2×	6	241
Ming‐Hsin Lai Taiwan	13	295 1.3×	39 0.7×	64 1.1×	21 0.5×	83 4.0×	23	354
Leonardo Chavez United States	2	163 0.7×	26 0.4×	71 1.2×	21 0.5×	36 1.7×	2	211

Countries citing papers authored by Kyle Parmley

Since Specialization

Citations

This map shows the geographic impact of Kyle Parmley'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 Kyle Parmley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyle Parmley more than expected).

Fields of papers citing papers by Kyle Parmley

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kyle Parmley. 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 Kyle Parmley. The network helps show where Kyle Parmley may publish in the future.

Co-authorship network of co-authors of Kyle Parmley

This figure shows the co-authorship network connecting the top 25 collaborators of Kyle Parmley. A scholar is included among the top collaborators of Kyle Parmley 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 Kyle Parmley. Kyle Parmley is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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8 of 8 papers shown

1.

Parmley, Kyle, et al.. (2025). Genomic and phenomic prediction for soybean seed yield, protein, and oil. The Plant Genome. 18(1). e70002–e70002.

2.

Chiozza, Mariana V., et al.. (2024). Changes in the leaf area-seed yield relationship in soybean driven by genetic, management and environments: implications for high-throughput phenotyping. 6(2).

3.

Chiozza, Mariana V., Kyle Parmley, R. Higgins, Asheesh K. Singh, & Fernando E. Miguez. (2021). Comparative prediction accuracy of hyperspectral bands for different soybean crop variables: From leaf area to seed composition. Field Crops Research. 271. 108260–108260. 34 indexed citations

4.

Jubery, Talukder Z., Seyed Vahid Mirnezami, Kyle Parmley, et al.. (2020). Computer vision and machine learning enabled soybean root phenotyping pipeline. Plant Methods. 16(1). 5–5. 80 indexed citations

5.

Parmley, Kyle, et al.. (2019). Identification and Genetic Characterization of Soybean Accessions Exhibiting Antibiosis and Antixenosis Resistance to Aphis glycines (Hemiptera: Aphididae). Journal of Economic Entomology. 112(3). 1428–1438. 12 indexed citations

6.

Parmley, Kyle, R. Higgins, Baskar Ganapathysubramanian, Soumik Sarkar, & Asheesh K. Singh. (2019). Machine Learning Approach for Prescriptive Plant Breeding. Scientific Reports. 9(1). 17132–17132. 67 indexed citations

7.

Parmley, Kyle, Koushik Nagasubramanian, Soumik Sarkar, Baskar Ganapathysubramanian, & Asheesh K. Singh. (2019). Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean. Plant Phenomics. 2019. 5809404–5809404. 63 indexed citations

8.

Jubery, Talukder Z., Johnathon M. Shook, Kyle Parmley, et al.. (2017). Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity. Frontiers in Plant Science. 7. 19 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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