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Peer-Reviewed Journals
XWaveNet: Enabling uncertainty quantification in short-term ocean wave height forecasts and extreme event prediction.
Kar, S., McKenna, J.R., Sunkara, V., Coniglione, R., Stanic, S. and Bernard, L., 2024. Applied Ocean Research, 148, p.103994.
Forecasting Vertical Profiles of Ocean Currents from Surface Characteristics: A Multivariate Multi-Head
Convolutional Neural Network-Long Short-Term Memory Approach.
Kar, S., McKenna, J.R., Anglada, G., Sunkara, V., Coniglione, R., Stanic, S. and Bernard, L., 2023. Journal of Marine Science and
Engineering, 11(10), p.1964.
Self-Supervised Learning Improves Classification of
Agriculturally Important Insect Pests in Plants.
Kar, S., Nagasubramanian, K., Elango, D., Nair, A., Mueller, D.S., O'Neal, M.E., Singh, A.K., Sarkar,
S., Ganapathysubramanian, B. and Singh, A, 2023.The Plant Phenome Journal, 6(1), p.e20079.
Cyber-Agricultural Systems
(CAS) for Crop Breeding and Sustainable Production.
Sarkar, S., Ganapathysubramanian, Singh, A, Fotouhi, F., Kar, S., Nagasubramanian, K., Chowdary, G.,
Das, S., Kantor, G., Krishnamurthy, A., Merchant, N. and Singh, A.K., 2023.Trends in Plant Science, pp.2514-20.
Unoccupied aerial systems imagery for phenotyping in cotton,
maize, soybean, and wheat breeding.
Herr, A.W., Adak, A., Carroll, M.E., Elango, D., Kar, S., Li, C., Jones, S.E., Carter, A.H., Murray, S.C.,
Paterson, A. and Sankaran, S., 2023.Crop Science, 63(4), pp.1722-1749.
The Gulf of Mexico in trouble:
Big data solutions to climate change science.
Sunkara, V., McKenna, J., Kar, S., Iliev, I. and Bernstein, D.N., 2023.Frontiers in Marine Science, 10:1075822.
Transpiration and water use efficiency of sorghum canopies
have a large genetic variability and are positively related under naturally high evaporative demand.
Pilloni, R., Kakkera, A., El Ghazzal, Z., Kar, S., Kumar, A.A., Hajjarpoor, A., Affortit, P., Ribière, W.,Kholova, J., Tardieu, F. and Vadez, V., 2022.bioRxiv, pp.2022-09.
An ensemble machine learning approach for determination of the optimum sampling
time for evapotranspiration assessment from high- throughput phenotyping data.
Kar, S., Purbey, V.K., Suradhaniwar, S., Korbu, L.B., Kholová, J., Durbha, S.S., Adinarayana, J., and
Vadez, V., 2021.Computers and
Electronics in Agriculture, 182, p.105992.
Time Series Forecasting of Univariate
Agrometeorological Data: A Comparative Performance Evaluation via One- Step and Multi-Step Ahead
Forecasting Strategies.
Suradhaniwar, S., Kar, S., Durbha, S.S. and Jagarlapudi, A., 2021.Sensors, 21(7), p.2430.
SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of
Temporal High-Throughput Phenotyping Data.
Kar, S., Garin, V., Kholová, J., Vadez, V., Durbha, S.S., Tanaka, R., Iwata, H., Urban, M.O. and
Adinarayana, J., 2020.Frontiers in Plant Science, 11, p.1746.
Automated discretization of 'transpiration restriction to increasing VPD' features from outdoors
high-throughput phenotyping data.
Kar, S., Tanaka, R., Korbu, L.B., Kholová, J., Iwata, H., Durbha, S.S., Adinarayana, J. and Vadez, V.,
2020.Plant Methods, 16(1), pp.1-20.
Classification of river water pollution using
Hyperion data.
Kar, S., Rathore, V.S., Sharma, R. and Swain, S.K., 2016.Journal of Hydrology, 537, pp.221-233.
Book Chapters
Deep Learning and Reinforcement Learning Methods for Advancing
Sustainable Agricultural and Natural Resource Management. In Big Data series.
Kar, S. and Jagarlapudi, A., 2024.Springer Nature
Improving data management
and decision-making in precision agriculture. In Improving data management and decision support
systems in agriculture.
Kar, S., Nandan, R. Raj, R., Suradhaniwar, S. and Jagarlapudi, A., 2020.Burleigh Dodds Science Publishing, Cambridge, UK (pp. 135-156) (ISBN: 978 1 78676
340 2; www.bdspublishing.com).
Precision Agriculture and Unmanned Aerial
Vehicles (UAVs). In Unmanned Aerial Vehicle: Applications in Agriculture and Environment (pp. 7
23).
Raj, R., Kar, S., Nandan, R. and Jagarlapudi, A., 2020.Springer, Cham.
Geo-ICDTs: Principles and
applications in agriculture. In Geospatial Technologies in Land Resources Mapping, Monitoring and
Management. Geotechnologies and the Environment, vol 21 (pp. 75-99).
Suradhaniwar, S., Kar, S., Nandan, R., Raj, R. and Jagarlapudi, A., 2018.Springer, Cham.
International Conference Proceedings
Incorporating Phenotypic Similarity into Trait
Description Embeddings using Deep Learning.
Kar, S., Balhoff, J., Lapp, H. and Dahdul, W., 2024.In NSF-HDR Ecosystem Conference, Illinois, US, Sep 9-12,
2024.
Potential Fields Modeling To Support Machine
Learning. Applications In Maritime Environments.
McKenna, J., Luttrell, J., Riedel, R. and Kar, S., 2024.In COMSOL 2024, Boston, US, Oct 2-4, 2024.
Uncrewed Systems
Hypoxia Mapping in the Northern Gulf of Mexico.
McKenna, J.R., Kar, S., Sunkara, V., Bernard, L., Rippy, W. and Cousino, J., 2023.In OCEANS 2023-MTS/IEEE US Gulf Coast (pp.
1-6). IEEE.
Maritime Workforce Training for the New
Blue Economy.
McKenna, J.R., Sunkara, V., Kar, S., Schmachtenberger, C., Arguelles, A., Arnold, M., Rippy, W.,
McQuillan, P., Annulis, H., Lomas, M. and Davis, K., 2023.In OCEANS 2023-MTS/IEEE US Gulf Coast (pp. 1-5). IEEE.
Predictive Analysis of
Climate Change Implications from Legacy ADCP Data using Ensemble Learning.
Kar, S., Weathers, K., Copeland, A., Sunkara, V., McKenna, J. and Hoffman, P.In Fall Meeting 2022. AGU.
Multi-step ahead wave forecasting
and extreme event prediction from buoy data using an ensemble of LSTM and genetic algorithm-aided
classification model.
Kar, S., McKenna, J., Sunkara, V., Stanic, S. and Bernard, L., 2022.In OCEANS 2022, Hampton Roads (pp. 1-7). IEEE.
Observations and Predictions of Water Quality Using Long Endurance Hybrid
Unmanned Maritime Vehicles and an Underwater Sensor Network.
Sunkara, V., Kar, S., McKenna, J., Rippy, W., Howden, S.D., Coleman, J., Coniglione, B., Mowitt, W.
and Hall, P., 2022.Frontiers in Hydrology 2022, pp.239
02.
Near Real-Time Radio Frequency
(RF) Data Analysis Pipeline for Aiding Marine Domain Awareness and Surveillance.
Kar, S., Sunkara, V., McKenna, J., Stanic, S. and Bernard, L., 2022.In OCEANS 2022,
Hampton Roads (pp. 1-8). IEEE.
Assimilating Statistical and Machine
Learning for Profiling Sub-Surface Currents via Air-Sea Interaction Modeling.
Kar, S., McKenna, J., Sunkara, V., Stanic, S. and Bernard, L., 2022. In MTS Buoy Workshop
2022.
Self-Supervised Learning Improves Agricultural Pest
Classification.
Kar, S., Nagasubramanian, K., Elango, D., Nair, A., Mueller, D.S., O’Neal, M.E., Singh, A.K., Sarkar,
S., Ganapathysubramanian, B. and Singh, A., 2021.In AI for Agriculture and Food Systems.
Self-supervised agricultural insect pest classification.
Kar, S., Nagasubramanian, K., Elango, D., Nair, A., Mueller, D., O’Neal, M., Singh, A., Sarkar, S.,
Ganapathysubramanian, B. and Singh, A., 2021.In
NAPPN 2022, Athens, Georgia, USA.
Multi-Scale
Time Series Analysis of Evapotranspiration for High-Throughput Phenotyping Frequency
Optimization.
Kar, S., Tanaka R., Iwata H., Kholova J., Durbha SS., Adinarayana J. and Vadez V., 2020.In 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) 2020
Mar 22 (pp. 98-103). IEEE.
Identification
of Genotypic Differences in ET Time Series Using Data-Mining Methods.
Kar, S., Tanaka R., Iwata H., Kholová J., Durbha SS., Adinarayana J. and Vadez V., 2019.In MLCAS 2019, Iowa State
University, USA.
Random
Forests Feature Selection-based Analysis of Genotypic Differences in the Water-Saving Trait of
Chickpea Crop.
Kar, S., Tanaka R., Iwata H., Kholová J., Durbha SS., Adinarayana J. and Vadez V.In 6th International Plant Phenotyping Symposium (IPPN) 2019, Oct 26, Nanjing, China.
Temporal analysis of Touzi parameters
for wheat crop characterization using L-band AgriSAR 2006 data.
Kar, S., Mandal, D., Bhattacharya, A. and Adinarayana, J., 2017.In 2017 IEEE International Geoscience
and Remote Sensing Symposium (IGARSS) (pp. 3909-3912). IEEE.
A review of studies for crop water stress identification
and design of a High Throughput Phenotyping framework.
Kar, S., Adinarayana, J. and Ninomiya, S., 2016.In 2016 International Conference on Statistics &
Big Data Bioinformatics in Agricultural Research, Nov 21-23, 2016, ICRISAT Hyderabad, India.