| 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 | 333* | 2019 |
| Ntire 2020 challenge on spectral reconstruction from an rgb image B Arad, R Timofte, O Ben-Shahar, YT Lin, GD Finlayson Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 218 | 2020 |
| 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 | 166 | 2018 |
| 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 | 135 | 2021 |
| Development of optimized phenomic predictors for efficient plant breeding decisions using phenomic-assisted selection in soybean K Parmley, K Nagasubramanian, S Sarkar, B Ganapathysubramanian, ... Plant Phenomics, 2019 | 73 | 2019 |
| How useful is active learning for image‐based plant phenotyping? K Nagasubramanian, T Jubery, F Fotouhi Ardakani, SV Mirnezami, ... The Plant Phenome Journal 4 (1), e20020, 2021 | 24 | 2021 |
| Automated trichome counting in soybean using advanced image‐processing techniques SV Mirnezami, T Young, T Assefa, S Prichard, K Nagasubramanian, ... Applications in plant sciences 8 (7), e11375, 2020 | 22 | 2020 |
| High-throughput phenotyping in soybean AK Singh, A Singh, S Sarkar, B Ganapathysubramanian, W Schapaugh, ... High-throughput crop phenotyping, 129-163, 2021 | 20 | 2021 |
| PIRM2018 challenge on spectral image super-resolution: methods and results M Shoeiby, A Robles-Kelly, R Timofte, R Zhou, F Lahoud, S Susstrunk, ... Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 0-0, 2018 | 20 | 2018 |
| Usefulness of interpretability methods to explain deep learning based plant stress phenotyping K Nagasubramanian, AK Singh, A Singh, S Sarkar, ... arXiv preprint arXiv:2007.05729, 2020 | 18 | 2020 |
| Plant phenotyping with limited annotation: Doing more with less K Nagasubramanian, A Singh, A Singh, S Sarkar, ... The Plant Phenome Journal 5 (1), e20051, 2022 | 17 | 2022 |
| Self‐supervised learning improves classification of agriculturally important insect pests in plants S Kar, K Nagasubramanian, D Elango, ME Carroll, CA Abel, A Nair, ... The Plant Phenome Journal 6 (1), e20079, 2023 | 16* | 2023 |
| Cyber-agricultural systems for crop breeding and sustainable production S Sarkar, B Ganapathysubramanian, A Singh, F Fotouhi, S Kar, ... Trends in plant science, 2023 | 10 | 2023 |
| Distributed deep learning for persistent monitoring of agricultural fields Y Esfandiari, K Nagasubramanian, F Fotouhi, PS Schnable, ... NeurIPS 2021 AI for Science Workshop, 2021 | 6 | 2021 |
| On load disaggregation using discrete events NK Thokala, MG Chandra, K Nagasubramanian 2016 IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia), 324-329, 2016 | 6 | 2016 |
| Exploring the use of 3D point cloud data for improved plant stress rating S Chiranjeevi, T Young, TZ Jubery, K Nagasubramanian, S Sarkar, ... AI for Agriculture and Food Systems, 2022 | 5 | 2022 |
| Privacy-preserving deep models for plant stress phenotyping M Cho, K Nagasubramanian, AK Singh, A Singh, ... AI for Agriculture and Food Systems, 2022 | 3 | 2022 |
| Self-supervised maize kernel classification and segmentation for embryo identification D Dong, K Nagasubramanian, R Wang, UK Frei, TZ Jubery, T Lübberstedt, ... Frontiers in Plant Science 14, 1108355, 2023 | 1 | 2023 |
| frontiers Research Topics January 2024 J Li, Y Li, J Qiao, L Li, X Wang, J Yao, G Liao, A Das, SD Choudhury, ... Machine Vision and Machine Learning for Plant Phenotyping and Precision …, 2024 | | 2024 |
| Plant phenotyping with limited annotation: Doing more with less K Nagasubramanian Iowa State University, 2022 | | 2022 |
