Asheesh K. Singh

8.3k total citations · 5 hit papers
130 papers, 4.9k citations indexed

About

Asheesh K. Singh is a scholar working on Plant Science, Genetics and Agronomy and Crop Science. According to data from OpenAlex, Asheesh K. Singh has authored 130 papers receiving a total of 4.9k indexed citations (citations by other indexed papers that have themselves been cited), including 118 papers in Plant Science, 23 papers in Genetics and 16 papers in Agronomy and Crop Science. Recurrent topics in Asheesh K. Singh's work include Wheat and Barley Genetics and Pathology (35 papers), Genetics and Plant Breeding (31 papers) and Soybean genetics and cultivation (23 papers). Asheesh K. Singh is often cited by papers focused on Wheat and Barley Genetics and Pathology (35 papers), Genetics and Plant Breeding (31 papers) and Soybean genetics and cultivation (23 papers). Asheesh K. Singh collaborates with scholars based in United States, Canada and India. Asheesh K. Singh's co-authors include Baskar Ganapathysubramanian, Soumik Sarkar, Arti Singh, Jiaoping Zhang, R. E. Knox, Koushik Nagasubramanian, Sambuddha Ghosal, J. M. Clarke, Sarah E. Jones and R. M. DePauw and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Asheesh K. Singh

123 papers receiving 4.8k citations

Hit Papers

Machine Learning for High-Throughput Stress Phenotyping i... 2015 2026 2018 2022 2015 2018 2018 2019 2022 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Machine Learning for High-Throughput Stress Phenotyping in Plants
    2015 711 citations Arti Singh, Baskar Ganapathysubramanian et al. profile →
  2. Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives
    2018 410 citations Asheesh K. Singh, Baskar Ganapathysubramanian et al. profile →
  3. An explainable deep machine vision framework for plant stress phenotyping
    2018 366 citations Sambuddha Ghosal, Asheesh K. Singh et al. Proceedings of the National Academy of Sciences profile →
  4. Plant disease identification using explainable 3D deep learning on hyperspectral images
    2019 265 citations Sarah E. Jones, Asheesh K. Singh et al. profile →
  5. Raffinose Family Oligosaccharides: Friend or Foe for Human and Plant Health?
    2022 134 citations Dinakaran Elango, Arti Singh et al. profile →

Peers

Asheesh K. Singh
Arti Singh United States
Wanneng Yang China
Andreas Hund Switzerland
Changying Li United States
Seth C. Murray United States
James C. Schnable United States
H. W. Dehne Germany
Michael Pumphrey United States
Yufeng Ge United States
Awais Rasheed Pakistan
Arti Singh United States
Asheesh K. Singh
Citations per year, relative to Asheesh K. Singh Asheesh K. Singh (= 1×) peers Arti Singh

Countries citing papers authored by Asheesh K. Singh

Since Specialization
Citations

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

Fields of papers citing papers by Asheesh K. Singh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Asheesh K. Singh. 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 Asheesh K. Singh. The network helps show where Asheesh K. Singh may publish in the future.

Co-authorship network of co-authors of Asheesh K. Singh

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

All Works

20 of 20 papers shown
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Joshi, Ameya, Minsu Cho, Aditya Balu, et al.. (2024). Zero‐shot insect detection via weak language supervision. SHILAP Revista de lepidopterología. 7(1). 9 indexed citations
4.
Koushik, Jayanth, Talukder Z. Jubery, Daren S. Mueller, et al.. (2024). InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline. PNAS Nexus. 4(1). pgae575–pgae575. 4 indexed citations
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Balu, Aditya, et al.. (2024). Class‐specific data augmentation for plant stress classification. SHILAP Revista de lepidopterología. 7(1). 1 indexed citations
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Adak, Alper, Dinakaran Elango, Soumyashree Kar, et al.. (2023). Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding. Crop Science. 63(4). 1722–1749. 36 indexed citations
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Dutta, Somak, et al.. (2022). Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean. Crop Science. 63(1). 204–226. 5 indexed citations
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Shook, Johnathon M., et al.. (2021). Crop yield prediction integrating genotype and weather variables using deep learning. PLoS ONE. 16(6). e0252402–e0252402. 124 indexed citations
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Shook, Johnathon M., Jiaoping Zhang, Sarah E. Jones, et al.. (2021). Meta-GWAS for quantitative trait loci identification in soybean. G3 Genes Genomes Genetics. 11(7). 35 indexed citations
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Assefa, Teshale, Jiaoping Zhang, R. Chowda-Reddy, et al.. (2020). Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches. BMC Plant Biology. 20(1). 42–42. 35 indexed citations
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Jubery, Talukder Z., Jamie A. O’Rourke, Arti Singh, et al.. (2020). Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters. Plant Phenomics. 2020. 1925495–1925495. 40 indexed citations
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Singh, Parul, et al.. (2019). Physicochemical, Phytochemical Antioxidant activity and Toxicity study of cissus quadrangularis. international journal of green and herbal chemistry. 8(2). 1 indexed citations
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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
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Shine, M.B., Qing-Ming Gao, R. Chowda-Reddy, et al.. (2019). Glycerol-3-phosphate mediates rhizobia-induced systemic signaling in soybean. Nature Communications. 10(1). 5303–5303. 39 indexed citations
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Akdemir, Deniz, William D. Beavis, Roberto Fritsche‐Neto, Asheesh K. Singh, & Julio Isidro y Sánchez. (2018). Multi-objective optimized genomic breeding strategies for sustainable food improvement. Heredity. 122(5). 672–683. 73 indexed citations
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Ghosal, Sambuddha, et al.. (2018). An explainable deep machine vision framework for plant stress phenotyping. Proceedings of the National Academy of Sciences. 115(18). 4613–4618. 366 indexed citations breakdown →
19.
Singh, Arti, Jiaoping Zhang, Mehdi Kabbage, et al.. (2017). Main and epistatic loci studies in soybean for Sclerotinia sclerotiorum resistance reveal multiple modes of resistance in multi-environments. Scientific Reports. 7(1). 55 indexed citations
20.
Singh, Asheesh K., et al.. (2009). Studies on genetic divergence in lablab bean through principal component analysis.. Indian Journal of Horticulture. 66(4). 483–487. 1 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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