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

5 papers align trajectories

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.

Machine Learning for High-Throughput Stress Phenotyping in Plants

2015 711 citations Arti Singh, Baskar Ganapathysubramanian et al. profile →
Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives

2018 410 citations Asheesh K. Singh, Baskar Ganapathysubramanian et al. profile →
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 →
Plant disease identification using explainable 3D deep learning on hyperspectral images

2019 265 citations Sarah E. Jones, Asheesh K. Singh et al. profile →
Raffinose Family Oligosaccharides: Friend or Foe for Human and Plant Health?

2022 134 citations Dinakaran Elango, Arti Singh et al. profile →

Peers

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

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20 of 20 papers shown

#	Title	Journal	Authors	Indexed citations
1	Comprehensive assessment of soybean seed composition from field trials spanning 22 US states and 24 years: Predictive insights	Crop Science	Mariana V. Chiozza, Johnathon M. Shook et al.	0
2	Using soybean historical field trial data to study genotype by environment variation and identify mega‐environments with the integration of genetic and non‐genetic factors	Agronomy Journal	Kaio Olímpio das Graças Dias, Asheesh K. Singh et al.	3
3	Zero‐shot insect detection via weak language supervision	SHILAP Revista de lepidopterología	Ameya Joshi, Minsu Cho et al.	9
4	InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline	PNAS Nexus	Jayanth Koushik, Talukder Z. Jubery et al.	4
5	Changes in the leaf area-seed yield relationship in soybean driven by genetic, management and environments: implications for high-throughput phenotyping		Mariana V. Chiozza, Kyle Parmley et al.	0
6	Class‐specific data augmentation for plant stress classification	SHILAP Revista de lepidopterología	Aditya Balu, Talukder Z. Jubery et al.	1
7	Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding	Crop Science	Alper Adak, Dinakaran Elango et al.	36
8	Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean	Crop Science	Somak Dutta, Asheesh K. Singh et al.	5
9	Crop yield prediction integrating genotype and weather variables using deep learning	PLoS ONE	Johnathon M. Shook, Linjiang Wu et al.	124
10	Interaction between Rag genes results in a unique synergistic transcriptional response that enhances soybean resistance to soybean aphids	BMC Genomics	Michelle A. Graham, Asheesh K. Singh et al.	6
11	Meta-GWAS for quantitative trait loci identification in soybean	G3 Genes Genomes Genetics	Johnathon M. Shook, Jiaoping Zhang et al.	35
12	Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches	BMC Plant Biology	Teshale Assefa, Jiaoping Zhang et al.	35
13	Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters	Plant Phenomics	Talukder Z. Jubery, Jamie A. O’Rourke et al.	40
14	Physicochemical, Phytochemical Antioxidant activity and Toxicity study of cissus quadrangularis	international journal of green and herbal chemistry	Parul Singh, Asheesh K. Singh et al.	1
15	Identification and Genetic Characterization of Soybean Accessions Exhibiting Antibiosis and Antixenosis Resistance to Aphis glycines (Hemiptera: Aphididae)	Journal of Economic Entomology	Kyle Parmley, Teshale Assefa et al.	12
16	Glycerol-3-phosphate mediates rhizobia-induced systemic signaling in soybean	Nature Communications	M.B. Shine, Qing-Ming Gao et al.	39
17	Multi-objective optimized genomic breeding strategies for sustainable food improvement	Heredity	Deniz Akdemir, William D. Beavis et al.	73
18	An explainable deep machine vision framework for plant stress phenotyping breakdown →	Proceedings of the National Academy of Sciences	Sambuddha Ghosal, Asheesh K. Singh et al.	366
19	Main and epistatic loci studies in soybean for Sclerotinia sclerotiorum resistance reveal multiple modes of resistance in multi-environments	Scientific Reports	Arti Singh, Jiaoping Zhang et al.	55
20	Studies on genetic divergence in lablab bean through principal component analysis.	Indian Journal of Horticulture	Asheesh K. Singh, Mathura Rai et al.	1

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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