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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.