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Baskar Ganapathysubramanian
Baskar Ganapathysubramanian
NSF/USDA AI Institute, Iowa State University
Zweryfikowany adres z iastate.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
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
10212016
Deep learning for plant stress phenotyping: trends and future perspectives
AK Singh, B Ganapathysubramanian, S Sarkar, A Singh
Trends in plant science 23 (10), 883-898, 2018
5432018
Sparse grid collocation schemes for stochastic natural convection problems
B Ganapathysubramanian, N Zabaras
Journal of Computational Physics 225 (1), 652-685, 2007
5272007
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 115 (18), 4613-4618, 2018
5142018
Plant disease identification using explainable 3D deep learning on hyperspectral images
K Nagasubramanian, S Jones, AK Singh, S Sarkar, A Singh, ...
Plant methods 15, 1-10, 2019
3032019
Nanoscale control of internal inhomogeneity enhances water transport in desalination membranes
TE Culp, B Khara, KP Brickey, M Geitner, TJ Zimudzi, JD Wilbur, SD Jons, ...
Science 371 (6524), 72-75, 2021
2302021
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 13, 1-12, 2017
2012017
Engineering fluid flow using sequenced microstructures
H Amini, E Sollier, M Masaeli, Y Xie, B Ganapathysubramanian, HA Stone, ...
Nature communications 4 (1), 1826, 2013
1962013
Computationally efficient solution to the Cahn–Hilliard equation: Adaptive implicit time schemes, mesh sensitivity analysis and the 3D isoperimetric problem
O Wodo, B Ganapathysubramanian
Journal of Computational Physics 230 (15), 6037-6060, 2011
1852011
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, 1-13, 2018
1692018
Genome-wide association analysis of seedling root development in maize (Zea mays L.)
BGTL Jordon Pace, Candice Gardner, Cinta Romay
BMC Genomics 16 (47), 2015
1692015
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
1682019
Challenges and opportunities in machine-augmented plant stress phenotyping
A Singh, S Jones, B Ganapathysubramanian, S Sarkar, D Mueller, ...
Trends in Plant Science 26 (1), 53-69, 2021
1482021
Modeling morphology evolution during solvent-based fabrication of organic solar cells
O Wodo, B Ganapathysubramanian
Computational Materials Science 55, 113-126, 2012
1402012
Enhanced charge separation in organic photovoltaic films doped with ferroelectric dipoles
KS Nalwa, JA Carr, RC Mahadevapuram, HK Kodali, S Bose, Y Chen, ...
Energy & Environmental Science 5 (5), 7042-7049, 2012
1402012
Modeling diffusion in random heterogeneous media: Data-driven models, stochastic collocation and the variational multiscale method
B Ganapathysubramanian, N Zabaras
Journal of Computational Physics 226 (1), 326-353, 2007
1392007
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
1322021
Analysis of Maize (Zea mays L.) Seedling Roots with the High-Throughput Image Analysis Tool ARIA (Automatic Root Image Analysis)
J Pace, N Lee, HS Naik, B Ganapathysubramanian, T Lübberstedt
PloS one 9 (9), e108255, 2014
1282014
A scalable framework for the solution of stochastic inverse problems using a sparse grid collocation approach
N Zabaras, B Ganapathysubramanian
Journal of Computational Physics 227 (9), 4697-4735, 2008
1272008
Vertical phase separation in small molecule: polymer blend organic thin film transistors can be dynamically controlled
K Zhao, O Wodo, D Ren, HU Khan, MR Niazi, H Hu, M Abdelsamie, R Li, ...
Advanced Functional Materials 26 (11), 1737-1746, 2016
1242016
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