Baskar Ganapathysubramanian
Baskar Ganapathysubramanian
NSF/USDA AI Institute, Iowa State University
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 23 (10), 883-898, 2018
Sparse grid collocation schemes for stochastic natural convection problems
B Ganapathysubramanian, N Zabaras
Journal of Computational Physics 225 (1), 652-685, 2007
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
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
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
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
Engineering fluid flow using sequenced microstructures
H Amini, E Sollier, M Masaeli, Y Xie, B Ganapathysubramanian, HA Stone, ...
Nature communications 4 (1), 1826, 2013
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
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
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
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
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
Modeling morphology evolution during solvent-based fabrication of organic solar cells
O Wodo, B Ganapathysubramanian
Computational Materials Science 55, 113-126, 2012
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
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
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
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
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
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
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