World's Best Scientists 2026 revealed!
José M. Peña

José M. Peña

D-Index & Metrics

Computer Science

D-Index
42
Citations
10296
World Ranking
8232
National Ranking
494

José M. Peña publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where José M. Peña sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 190 publications — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

José M. Peña D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where José M. Peña sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

José M. Peña is affiliated with Lurtis Rules in the United Kingdom and has a research focus spanning Agricultural and Biological Sciences, Mathematics, and Environmental Science. Their work extends into several specialized subfields, including Statistics and Probability, Plant Science, Ecology, Environmental Engineering, and Analytical Chemistry.

The scientist's research interests encompass various topics, notably Remote Sensing in Agriculture, Advanced Causal Inference Techniques, Smart Agriculture and AI, Statistical Methods and Inference, Remote Sensing and LiDAR Applications, Horticultural and Viticultural Research, and Spectroscopy and Chemometric Analyses.

José M. Peña has contributed to multiple recent scientific publications. Among them are:

  • "UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions" (2021, Remote Sensing)
  • "Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review" (2023, Frontiers in Plant Science)
  • "A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the 'Cipolla Rossa di Tropea' (Italy)" (2020, Remote Sensing)
  • "Transfer and zero-shot learning for scalable weed detection and classification in UAV images" (2024, Knowledge-Based Systems)
  • "How weed management influence plant community composition, taxonomic diversity and crop yield: A long-term study in a Mediterranean vineyard" (2021, Agriculture Ecosystems & Environment)

The scientist collaborates frequently with other researchers, including José Dorado, Ana Isabel de Castro, Gustavo A. Mesías-Ruiz, César Fernández-Quintanilla, and J. G. M. Guerra. These ongoing partnerships have resulted in a substantial corpus of joint publications.

Publication venues where José M. Peña has frequently published include:

  • arXiv (Cornell University)
  • Remote Sensing
  • Journal of Causal Inference
  • SSRN Electronic Journal
  • Computers and Electronics in Agriculture

In addition to articles, Peña has authored a book titled Insights in Functional Plant Ecology 2022, published by Frontiers Media in 2023.

Best Publications

  • An empirical comparison of four initialization methods for the K-Means algorithm

    J.M Peña;J.A Lozano;P Larrañaga

  • Object-based crop identification using multiple vegetation indices, textural features and crop phenology

    José M. Peña-Barragán;Moffatt K. Ngugi;Richard E. Plant;Johan Six

  • Feature selection for multi-label naive Bayes classification

    Min-Ling Zhang;José M. Peña;Victor Robles

  • Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV

    J. Torres-Sánchez;J.M. Peña;A.I. de Castro;F. López-Granados

  • Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.

    José Manuel Peña;Jorge Torres-Sánchez;Ana Isabel de Castro;Maggi Kelly

  • Evaluation of autofocus functions in molecular cytogenetic analysis

    A. Santos;C. Ortiz De Solórzano;J. J. Vaquero;J. M. Peña

  • Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery

    Isabel Luisa Castillejo-González;Francisca López-Granados;Alfonso García-Ferrer;José Manuel Peña-Barragán

  • An automatic object-based method for optimal thresholding in UAV images

    J. Torres-Sánchez;F. López-Granados;J.M. Peña

  • An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

    Ana I. de Castro;Jorge Torres-Sánchez;José M. Peña;Francisco Manuel Jiménez-Brenes

  • Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks

    Pedro Larrañaga;Ramón Etxeberria;Jose Antonio Lozano;Jose Manuel Peña

  • High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology

    Jorge Torres-Sánchez;Francisca López-Granados;Nicolás Serrano;Octavio Arquero

  • Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution.

    José M. Peña;Jorge Torres-Sánchez;Angélica Serrano-Pérez;Ana I. de Castro

  • Object-Based Image Classification of Summer Crops with Machine Learning Methods

    José M. Peña;Pedro Antonio Gutiérrez;César Hervás-Martínez;Johan Six

  • Consistent Feature Selection for Pattern Recognition in Polynomial Time

    Roland Nilsson;José M. Peña;Johan Björkegren;Jesper Tegnér

  • A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method

    M. Pérez-Ortiz;J.M. Peña;P.A. Gutiérrez;J. Torres-Sánchez

  • Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery

    María Pérez-Ortiz;José Manuel Peña;Pedro Antonio Gutiérrez;Jorge Torres-Sánchez

  • Assessing Optimal Flight Parameters for Generating Accurate Multispectral Orthomosaicks by UAV to Support Site-Specific Crop Management

    Francisco-Javier Mesas-Carrascosa;Jorge Torres-Sánchez;Inmaculada Clavero-Rumbao;Alfonso García-Ferrer

  • Combinatorial optimization by learning and simulation of Bayesian networks

    Pedro Larrañaga;Ramon Etxeberria;Jose Antonio Lozano;Jose Manuel Peña

  • Assessing UAV-collected image overlap influence on computation time and digital surface model accuracy in olive orchards

    Jorge Torres-Sánchez;Francisca López-Granados;Irene Borra-Serrano;José Manuel Peña

  • UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions

    Ana I. de Castro;Yeyin Shi;Joe Mari Maja;Jose M. Peña

  • Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling

    F. M. Jiménez-Brenes;F. López-Granados;A. I. de Castro;J. Torres-Sánchez

  • 3-D Characterization of Vineyards Using a Novel UAV Imagery-Based OBIA Procedure for Precision Viticulture Applications

    Ana I. de Castro;Francisco Manuel Jiménez-Brenes;Jorge Torres-Sánchez;José M. Peña

  • Mapping the 3D structure of almond trees using UAV acquired photogrammetric point clouds and object-based image analysis

    Jorge Torres-Sánchez;Ana I. de Castro;José M. Peña;Francisco M. Jiménez-Brenes

  • GA-EDA: hybrid evolutionary algorithm using genetic and estimation of distribution algorithms

    J. M. Peña;V. Robles;P. Larrañaga;V. Herves

  • A multicenter study of the early detection of synaptic dysfunction in Mild Cognitive Impairment using Magnetoencephalography-derived functional connectivity

    Fernando Maestú;Jose Maria Peña;Pilar Garcés;Santiago González

  • Dimensionality reduction in unsupervised learning of conditional Gaussian networks

    J.M. Pena;J.A. Lozano;P. Larranaga;I. Inza

  • Advances in Intelligent Data Analysis VI

    A. Fazel Famili;Joost N. Kok;José M. Peña;Arno Siebes

  • Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley

    Victor P. Rueda-Ayala;José M. Peña;Mats Höglind;José M. Bengochea-Guevara

  • An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering

    J. M. Peña;J. A. Lozano;P. Larrañaga

Frequent Co-Authors

Francisca López-Granados
Francisca López-Granados Spanish National Research Council
Pedro Larrañaga
Pedro Larrañaga Technical University of Madrid
Javier DeFelipe
Javier DeFelipe Technical University of Madrid
Jose R. Arribas
Jose R. Arribas Hospital Universitario La Paz
Fernando Maestú
Fernando Maestú Complutense University of Madrid
Jemal H. Abawajy
Jemal H. Abawajy Deakin University
Massimiliano Zanin
Massimiliano Zanin University of the Balearic Islands
Sascha Ossowski
Sascha Ossowski King Juan Carlos University
Joost N. Kok
Joost N. Kok University of Twente
Eero Pekkonen
Eero Pekkonen University of Helsinki

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

Expanding your studies beyond a traditional Computer Science degree can open up many doors. The tech sector values interdisciplinary expertise, especially for those interested in fields like environmental science, engineering, and accelerated learning.

If you're curious about technology’s impact on sustainability, learn more about what can you get with an environmental science degree. Many graduates use computer science skills to tackle problems in climate science or ecological research.

Online learning is a flexible path for today’s busy students. For example, you can earn a degree through an online computer science degree program, some of which offer accelerated options for faster graduation.

Engineering is another exciting option. Students pursuing environmental engineering degrees online will find strong demand for their expertise in both public and private sectors. If you’re considering an advanced degree, explore the cheapest online master's mechanical engineering programs, which can help boost your career with less financial stress.

Best Scientists Citing José M. Peña

Trending Scientists

Recently Published Articles