World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
44
Citations
10285
World Ranking
5713
National Ranking
1597

Soumik Sarkar publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Soumik Sarkar sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 312 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Soumik Sarkar D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Soumik Sarkar sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 44 D-Index — 42nd percentile

42% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

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
Asheesh K. Singh
Asheesh K. Singh Iowa State University
Gregor P. Henze
Gregor P. Henze University of Colorado Boulder
Minsu Cho
Minsu Cho Pohang University of Science and Technology
Dermot J. Hayes
Dermot J. Hayes Iowa State University
Duane D. Johnson
Duane D. Johnson Iowa State University
Joshua R. Smith
Joshua R. Smith University of Washington
Scott C. Chapman
Scott C. Chapman University of Queensland
Patrick S. Schnable
Patrick S. Schnable Iowa State University

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