| LLNet: A deep autoencoder approach to natural low-light image enhancement KG Lore, A Akintayo, S Sarkar Pattern Recognition 61, 650-662, 2017 | 2115 | 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 | 1318 | 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 | 711 | 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 | 655 | 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), 98, 2019 | 499* | 2019 |
| 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 | 241 | 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 | 239 | 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), 23, 2017 | 234 | 2017 |
| Collaborative deep learning in fixed topology networks Z Jiang, A Balu, C Hegde, S Sarkar Advances in Neural Information Processing Systems 30, 2017 | 233 | 2017 |
| 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 | 232 | 2019 |
| 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), 86, 2018 | 203 | 2018 |
| 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 | 192 | 2019 |
| 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 | 172 | 2018 |
| 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 | 162 | 2009 |
| UAS-based plant phenotyping for research and breeding applications W Guo, ME Carroll, A Singh, TL Swetnam, N Merchant, S Sarkar, ... Plant Phenomics, 2021 | 152 | 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 | 149* | 2018 |
| 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), 5, 2020 | 147 | 2020 |
| 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 | 147 | 2019 |
| 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 | 130* | 2020 |
| Occupancy sensing in buildings: A review of data analytics approaches H Saha, AR Florita, GP Henze, S Sarkar Energy and Buildings 188, 278-285, 2019 | 130 | 2019 |
Baskar GanapathysubramanianNSF/USDA AI Institute, Iowa State UniversityVerified email at iastate.edu
