World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
44
Citations
10285
World Ranking
5711
National Ranking
1596

Overview

Soumik Sarkar is affiliated with Iowa State University in the United States. Their research is situated primarily at the intersection of engineering and computer science, with significant contributions to plant science and artificial intelligence. Their work demonstrates a strong focus on the application of computational techniques to agricultural and plant phenotyping challenges.

The scientist has explored multiple subfields including plant science, artificial intelligence, computer vision and pattern recognition, computational mechanics, and electrical and electronic engineering. This multidisciplinary approach supports research that integrates data-driven methodologies with plant biology and smart agriculture.

Sarmik Sarkar's main topics of study involve smart agriculture and AI, remote sensing in agriculture, soybean genetics and cultivation, anomaly detection techniques, spectroscopy and chemometric analyses, force microscopy techniques, and time series analysis and forecasting. These topics reflect a broad engagement with both the biological and technical aspects of modern agricultural research.

Frequent coauthors include:

  • Baskar Ganapathysubramanian
  • Aditya Balu
  • Adarsh Krishnamurthy
  • Asheesh K. Singh
  • Talukder Z. Jubery

Sarmik Sarkar has published extensively across various venues, with a predominant presence in arXiv (Cornell University). Other frequent publication venues include Plant Phenomics, SSRN Electronic Journal, Frontiers in Plant Science, and The Plant Phenome Journal.

Their recent papers emphasize advances in plant stress phenotyping, crop yield prediction, and phenotyping pipelines using machine learning and computer vision:

  • Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping, 2020, Trends in Plant Science
  • Crop yield prediction integrating genotype and weather variables using deep learning, 2021, PLoS ONE
  • Computer vision and machine learning enabled soybean root phenotyping pipeline, 2020, Plant Methods
  • UAS-Based Plant Phenotyping for Research and Breeding Applications, 2021, Plant Phenomics
  • Soybean Root System Architecture Trait Study through Genotypic, Phenotypic, and Shape-Based Clusters, 2020, Plant Phenomics

Best Publications

  • LLNet: A deep autoencoder approach to natural low-light image enhancement

    Kin Gwn Lore;Adedotun Akintayo;Soumik Sarkar

  • Machine Learning for High-Throughput Stress Phenotyping in Plants

    Arti Singh;Baskar Ganapathysubramanian;Asheesh Kumar Singh;Soumik Sarkar

  • Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives.

    Asheesh Kumar Singh;Baskar Ganapathysubramanian;Soumik Sarkar;Arti Singh

  • An explainable deep machine vision framework for plant stress phenotyping.

    Sambuddha Ghosal;David Blystone;Asheesh K. Singh;Baskar Ganapathysubramanian

  • Plant disease identification using explainable 3D deep learning on hyperspectral images

    Koushik Nagasubramanian;Sarah Jones;Asheesh K. Singh;Soumik Sarkar

  • A real-time phenotyping framework using machine learning for plant stress severity rating in soybean.

    Hsiang Sing Naik;Jiaoping Zhang;Alec Lofquist;Teshale Assefa

  • Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping.

    Arti Singh;Sarah Jones;Baskar Ganapathysubramanian;Soumik Sarkar

  • A weakly supervised deep learning framework for sorghum head detection and counting

    Sambuddha Ghosal;Bangyou Zheng;Scott C. Chapman;Scott C. Chapman;Andries B. Potgieter

  • Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning

    Johnathon Shook;Tryambak Gangopadhyay;Linjiang Wu;Baskar Ganapathysubramanian

  • Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems

    Koushik Nagasubramanian;Sarah Jones;Soumik Sarkar;Asheesh K. Singh

  • An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems

    Te Han;Chao Liu;Linjiang Wu;Soumik Sarkar

  • Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

    Chinmay Rao;Asok Ray;Soumik Sarkar;Murat Yasar

  • Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks

    Pranamesh Chakraborty;Yaw Okyere Adu-Gyamfi;Subhadipto Poddar;Vesal Ahsani

  • Computer vision and machine learning enabled soybean root phenotyping pipeline

    Kevin G. Falk;Talukder Z. Jubery;Seyed V. Mirnezami;Kyle A. Parmley

  • Collaborative Deep Learning in Fixed Topology Networks

    Zhanhong Jiang;Aditya Balu;Chinmay Hegde;Soumik Sarkar

  • UAS-Based Plant Phenotyping for Research and Breeding Applications.

    Wei Guo;Matthew E Carroll;Arti Singh;Tyson L Swetnam

  • Computer vision and machine learning for robust phenotyping in genome-wide studies

    Jiaoping Zhang;Hsiang Sing Naik;Teshale Assefa;Soumik Sarkar

  • Machine Learning Approach for Prescriptive Plant Breeding.

    Kyle A. Parmley;Race H. Higgins;Baskar Ganapathysubramanian;Soumik Sarkar

  • A deep learning framework to discern and count microscopic nematode eggs

    Adedotun Akintayo;Gregory L. Tylka;Asheesh K. Singh;Baskar Ganapathysubramanian

  • Data-Driven Fault Detection in Aircraft Engines With Noisy Sensor Measurements

    Soumik Sarkar;Xin Jin;Asok Ray

  • NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images

    Unknown

  • Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers

    Ameya Joshi;Amitangshu Mukherjee;Soumik Sarkar;Chinmay Hegde

Frequent Co-Authors

Baskar Ganapathysubramanian
Baskar Ganapathysubramanian Iowa State University
Asok Ray
Asok Ray Pennsylvania State University
Gregor P. Henze
Gregor P. Henze University of Colorado Boulder
Minsu Cho
Minsu Cho Pohang University of Science and Technology
Duane D. Johnson
Duane D. Johnson Iowa State University
Dermot J. Hayes
Dermot J. Hayes Iowa State University
Scott C. Chapman
Scott C. Chapman University of Queensland
Joshua R. Smith
Joshua R. Smith University of Washington
Navdeep Jaitly
Navdeep Jaitly Google (United States)
Patrick S. Schnable
Patrick S. Schnable Iowa State University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Engineering and Technology in the USA opens doors to a variety of dynamic career tracks. Online degree options are becoming increasingly popular, offering flexibility and access to specialized skills needed in the workforce. For those interested in the intersection of technology and user experience, pursuing an online ux degree can be a smart move, providing you with design and usability expertise highly sought after by employers.

The world of project management continues to grow alongside technology fields. If you’re considering whether is a bachelor degree in project management worth it, it’s valuable to note that this credential can open leadership roles where IT and engineering overlap.

The rise of digital currencies and blockchain solutions means there’s also a demand for niche expertise. Earning a masters in crypto can prepare you for innovative careers in fintech, blockchain development, and related industries.

Tech-savvy individuals with a passion for sports may also consider online sports management degrees. This pathway lets you combine management, analytics, and emerging technologies in the thriving sports industry.

These flexible online degrees can help you align your technological interests with real-world career opportunities in the rapidly evolving landscape of engineering, IT, and management.

Best Scientists Citing Soumik Sarkar

Trending Scientists

Recently Published Articles