World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
40
Citations
8625
World Ranking
7202
National Ranking
1966

Melba M. Crawford 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 Melba M. Crawford 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: 191 publications — 44th percentile

44% of scientists in this discipline score the same or lower.

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

Melba M. Crawford 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 Melba M. Crawford 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: 40 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2007 - IEEE Fellow For applications of satellite data and airborne LIDAR imagery

Overview

Melba M. Crawford is affiliated with Purdue University West Lafayette in the United States. Their research primarily focuses on environmental science and agricultural and biological sciences, with a notable emphasis on subfields such as ecology, plant science, environmental engineering, media technology, and agronomy and crop science.

The main topics covered in their work include remote sensing in agriculture, remote sensing and LiDAR applications, smart agriculture and artificial intelligence, leaf properties and growth measurement, remote-sensing image classification, crop yield and soil fertility, as well as spectroscopy and chemometric analyses.

Melba M. Crawford has published extensively, contributing to several scientific venues. The most frequent publication outlets include:

  • Remote Sensing
  • arXiv (Cornell University)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • Frontiers in Plant Science
  • IEEE Transactions on Geoscience and Remote Sensing

The scientist has collaborated repeatedly with a group of coauthors, including:

  • Mitchell R. Tuinstra
  • Edward J. Delp
  • Ayman Habib
  • Saurabh Prasad
  • Wei Liu

Recent publications showcase the application of remote sensing and machine learning techniques in agriculture and environmental monitoring. Notable recent papers include:

  • "YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning," 2022, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • "Automatic Plant Counting and Location Based on a Few-Shot Learning Technique," 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • "Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data," 2020, Remote Sensing
  • "Integrating crop growth models with remote sensing for predicting biomass yield of sorghum," 2021, in silico Plants
  • "New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping," 2021, Remote Sensing

In 2007, Melba M. Crawford was recognized as an IEEE Fellow for applications of satellite data and airborne LIDAR imagery, indicating a contribution to the use of advanced remote sensing technologies within their field of study.

Best Publications

  • Investigation of the random forest framework for classification of hyperspectral data

    J. Ham;Yangchi Chen;M.M. Crawford;J. Ghosh

  • Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image Classification

    Li Ma;M M Crawford;Jinwen Tian

  • Feature Mining for Hyperspectral Image Classification

    Xiuping Jia;Bor-Chen Kuo;M. M. Crawford

  • Best-bases feature extraction algorithms for classification of hyperspectral data

    S. Kumar;J. Ghosh;M.M. Crawford

  • An Active Learning Approach to Hyperspectral Data Classification

    S. Rajan;J. Ghosh;M.M. Crawford

  • Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning

    Dalton Lunga;Saurabh Prasad;Melba M. Crawford;Okan Ersoy

  • Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis

    Shailesh Kumar;Joydeep Ghosh;Melba M. Crawford

  • Active Learning: Any Value for Classification of Remotely Sensed Data?

    M. M. Crawford;D. Tuia;H. L. Yang

  • Transfer Function Models of Daily Urban Water Use

    David R. Maidment;Shaw‐Pin ‐P Miaou;Melba M. Crawford

  • View Generation for Multiview Maximum Disagreement Based Active Learning for Hyperspectral Image Classification

    Wei Di;M. M. Crawford

  • Adaptive Classification for Hyperspectral Image Data Using Manifold Regularization Kernel Machines

    Wonkook Kim;Melba M Crawford

  • Local-Manifold-Learning-Based Graph Construction for Semisupervised Hyperspectral Image Classification

    Li Ma;Melba M. Crawford;Xiaoquan Yang;Yan Guo

  • Integrating support vector machines in a hierarchical output space decomposition framework

    Yangchi Chen;M.M. Crawford;J. Ghosh

  • Modeling and simulation of a nonhomogeneous poisson process having cyclic behavior

    Unknown

  • Active Learning via Multi-View and Local Proximity Co-Regularization for Hyperspectral Image Classification

    Wei Di;Melba M Crawford

  • Ensemble Multiple Kernel Active Learning For Classification of Multisource Remote Sensing Data

    Yuhang Zhang;Hsiuhan Lexie Yang;Saurabh Prasad;Edoardo Pasolli

  • Unsupervised multistage image classification using hierarchical clustering with a bayesian similarity measure

    Unknown

  • Anomaly Detection for Hyperspectral Images Based on Robust Locally Linear Embedding

    Li Ma;Li Ma;Melba M. Crawford;Jinwen Tian

  • An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

    Zhou Zhang;Edoardo Pasolli;Melba M. Crawford;James C. Tilton

  • Fusing interferometric radar and laser altimeter data to estimate surface topography and vegetation heights

    K.C. Slatton;M.M. Crawford;B.L. Evans

  • Applying nonlinear manifold learning to hyperspectral data for land cover classification

    Yangchi Chen;M.M. Crawford;J. Ghosh

  • Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery

    Ayman Habib;Youkyung Han;Weifeng Xiong;Fangning He

  • Improving Orthorectification of UAV-Based Push-Broom Scanner Imagery Using Derived Orthophotos From Frame Cameras

    Ayman Habib;Weifeng Xiong;Fangning He;Hsiuhan Lexie Yang

  • Comparative Analysis of HRU and Grid-Based SWAT Models

    Garett Pignotti;Hendrik Rathjens;Raj Cibin;Indrajeet Chaubey

  • Automatic Plant Counting and Location Based on a Few-Shot Learning Technique

    Azam Karami;Melba Crawford;Edward J. Delp

  • Boresight Calibration of GNSS/INS-Assisted Push-Broom Hyperspectral Scanners on UAV Platforms

    Ayman Habib;Tian Zhou;Ali Masjedi;Zhou Zhang

  • A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

    Jinha Jung;Edoardo Pasolli;Saurabh Prasad;James C. Tilton

  • A Hierarchical Multiclassifier System for Hyperspectral Data Analysis

    Shailesh Kumar;Joydeep Ghosh;Melba M. Crawford

  • Foreword to the Special Issue on Hyperspectral Image and Signal Processing

    Jocelyn Chanussot;Melba M. Crawford;Bor-Chen Kuo

Frequent Co-Authors

Saurabh Prasad
Saurabh Prasad University of Houston
Ayman Habib
Ayman Habib Purdue University West Lafayette
Edward J. Delp
Edward J. Delp Purdue University West Lafayette
Joydeep Ghosh
Joydeep Ghosh The University of Texas at Austin
Jinwen Tian
Jinwen Tian Huazhong University of Science and Technology
Mitchell R. Tuinstra
Mitchell R. Tuinstra Purdue University West Lafayette
Indrajeet Chaubey
Indrajeet Chaubey University of Connecticut
Scott C. Chapman
Scott C. Chapman University of Queensland
Graeme L. Hammer
Graeme L. Hammer University of Queensland
David S. Ebert
David S. Ebert University of Oklahoma

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