Asheesh K. Singh
Asheesh K. Singh
Professor, Iowa State University; Co-Director, Iowa Soybean Research Center
Verified email at - Homepage
Cited by
Cited by
Machine Learning for High-Throughput Stress Phenotyping in Plants
A Singh, B Ganapathysubramanian, AK Singh, S Sarkar
Trends in Plant Science 21 (2), 110-124, 2016
Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives
AK Singh, B Ganapathysubramanian, S Sarkar, A Singh
Trends in Plant Science, 2018
An explainable deep machine vision framework for plant stress phenotyping
S Ghosal, D Blystone, AK Singh, B Ganapathysubramanian, A Singh, ...
Proceedings of the National Academy of Sciences, 10.1073/pnas.1716999115, 2018
Plant disease identification using explainable 3D deep learning on hyperspectral images
K Nagasubramanian, S Jones, AK Singh, S Sarkar, A Singh, ...
Plant Methods 15 (1), 1-10, 2019
NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images
A B, O Ben-Shahar, R Timofte, LV Gool, L Zhang, MH Yang, Z Xiong, ...
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition …, 2018
Application of molecular markers to wheat breeding in Canada
HS Randhawa, M Asif, C Pozniak, JM Clarke, RJ Graf, SL Fox, ...
Plant Breeding 132 (5), 458-471, 2013
A real-time phenotyping framework using machine learning for plant stress severity rating in soybean
HS Naik, J Zhang, A Lofquist, T Assefa, S Sarkar, D Ackerman, A Singh, ...
Plant Methods, 2017
Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems
K Nagasubramanian, S Jones, S Sarkar, AK Singh, A Singh, ...
Plant methods 14 (1), 1-13, 2018
A weakly supervised deep learning framework for sorghum head detection and counting
S Ghosal, B Zheng, SC Chapman, AB Potgieter, DR Jordan, X Wang, ...
Plant Phenomics 2019, 2019
Crop yield prediction integrating genotype and weather variables using deep learning
J Shook, T Gangopadhyay, L Wu, B Ganapathysubramanian, S Sarkar, ...
Plos one 16 (6), e0252402, 2021
A deep learning framework to discern and count microscopic nematode eggs
A Akintayo, GL Tylka, AK Singh, B Ganapathysubramanian, A Singh, ...
Scientific Reports 8 (1), 9145, 2018
Computer vision and machine learning for robust phenotyping in genome-wide studies
J Zhang, HS Naik, T Assefa, S Sarkar, RV Reddy, A Singh, ...
Scientific reports 7, 44048, 2017
Genome‐wide association and epistasis studies unravel the genetic architecture of sudden death syndrome resistance in soybean
J Zhang, A Singh, DS Mueller, AK Singh
The Plant Journal 84 (6), 1124-36, 2015
Genetic variability in arbuscular mycorrhizal fungi compatibility supports the selection of durum wheat genotypes for enhancing soil ecological services and cropping systems in …
AK Singh, C Hamel, RM DePauw, RE Knox
Canadian journal of microbiology 58 (3), 293-302, 2012
Computer vision and machine learning enabled soybean root phenotyping pipeline
KG Falk, TZ Jubery, SV Mirnezami, KA Parmley, S Sarkar, A Singh, ...
Plant methods 16 (1), 1-19, 2020
Chromosomal location of the cadmium uptake gene (Cdu1) in durum wheat
RE Knox, CJ Pozniak, FR Clarke, JM Clarke, S Houshmand, AK Singh
Genome 52 (9), 741-747, 2009
Multi-objective optimized genomic breeding strategies for sustainable food improvement
D Akdemir, W Beavis, R Fritsche-Neto, AK Singh, J Isidro-Sánchez
Heredity, 2018
Genetic architecture of Charcoal Rot (Macrophomina phaseolina) Resistance in Soybean revealed using a diverse panel
SM Coser, RV Chowdareddy, J Zhang, DS Mueller, A Mengistu, K Wise, ...
Frontiers in Plant Science 8, 1626, 2017
Allelic variation at Psy1-A1 and association with yellow pigment in durum wheat grain
A Singh, S Reimer, CJ Pozniak, FR Clarke, JM Clarke, RE Knox, ...
Theoretical and applied genetics 118 (8), 1539-1548, 2009
Raffinose Family Oligosaccharides: Friend or Foe for Human and Plant Health?
D Elango, K Rajendran, L Van der Laan, S Sebastiar, J Raigne, ...
Frontiers in Plant Science 13, Art. 829118, 2022
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