Soumik Sarkar
Soumik Sarkar
Director, Translational AI Center, Professor, Iowa State University
Verified email at - Homepage
Cited by
Cited by
LLNet: A deep autoencoder approach to natural low-light image enhancement
KG Lore, A Akintayo, S Sarkar
Pattern Recognition 61, 650-662, 2017
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
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
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
Collaborative deep learning in fixed topology networks
Z Jiang, A Balu, C Hegde, S Sarkar
Advances in Neural Information Processing Systems 30, 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, 1-13, 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
An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems
T Han, C Liu, L Wu, S Sarkar, D Jiang
Mechanical Systems and Signal Processing 117, 170-187, 2019
Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns
C Rao, A Ray, S Sarkar, M Yasar
Signal, Image and Video Processing 3 (2), 101-114, 2009
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
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
Traffic congestion detection from camera images using deep convolution neural networks
P Chakraborty, YO Adu-Gyamfi, S Poddar, V Ahsani, A Sharma, S Sarkar
Transportation Research Record 2672 (45), 222-231, 2018
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, RVC Reddy, A Singh, ...
Scientific Reports 7 (1), 44048, 2017
Semantic adversarial attacks: Parametric transformations that fool deep classifiers
A Joshi, A Mukherjee, S Sarkar, C Hegde
Proceedings of the IEEE/CVF international conference on computer vision …, 2019
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, 1-19, 2020
Data-driven fault detection in aircraft engines with noisy sensor measurements
S Sarkar, X Jin, A Ray
Predicting county-scale maize yields with publicly available data
Z Jiang, C Liu, B Ganapathysubramanian, DJ Hayes, S Sarkar
Scientific Reports 10 (1), 14957, 2020
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