María Culman

681 total citations · 1 hit paper
11 papers, 479 citations indexed

About

María Culman is a scholar working on Plant Science, Ecology and Environmental Engineering. According to data from OpenAlex, María Culman has authored 11 papers receiving a total of 479 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Plant Science, 5 papers in Ecology and 4 papers in Environmental Engineering. Recurrent topics in María Culman's work include Smart Agriculture and AI (8 papers), Date Palm Research Studies (6 papers) and Remote Sensing and LiDAR Applications (4 papers). María Culman is often cited by papers focused on Smart Agriculture and AI (8 papers), Date Palm Research Studies (6 papers) and Remote Sensing and LiDAR Applications (4 papers). María Culman collaborates with scholars based in Colombia, Belgium and Brazil. María Culman's co-authors include Juan Aranda, Jairo Alejandro Gómez Escobar, Luis Tobòn, Diana Teresa Parra Sánchez, Stephanie Delalieux, Kristof Van Tricht, Claudio M. de Farias, Jesús Talavera López, Lammert Kooistra and Laurent Tits and has published in prestigious journals such as Remote Sensing, Computers and Electronics in Agriculture and Agricultural Water Management.

In The Last Decade

María Culman

11 papers receiving 456 citations

Hit Papers

Review of IoT applications in agro-industrial and environ... 2017 2026 2020 2023 2017 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
María Culman Colombia 7 216 127 94 65 62 11 479
George Salahas Greece 6 365 1.7× 126 1.0× 113 1.2× 81 1.2× 72 1.2× 9 700
Dimitrios Loukatos Greece 13 223 1.0× 93 0.7× 101 1.1× 34 0.5× 43 0.7× 45 574
Pedro Gonçalves Portugal 12 173 0.8× 169 1.3× 102 1.1× 83 1.3× 80 1.3× 68 717
Manuel Jiménez Buendía Spain 15 212 1.0× 65 0.5× 68 0.7× 46 0.7× 59 1.0× 37 549
Aglaia Liopa-Tsakalidi Greece 12 508 2.4× 130 1.0× 116 1.2× 88 1.4× 81 1.3× 31 891
Jairo Alejandro Gómez Escobar Colombia 6 152 0.7× 129 1.0× 97 1.0× 19 0.3× 62 1.0× 12 461
Jirapond Muangprathub Thailand 9 323 1.5× 114 0.9× 105 1.1× 42 0.6× 97 1.6× 33 669
Poojan Patel India 5 362 1.7× 51 0.4× 50 0.5× 86 1.3× 70 1.1× 6 700
Néstor Lucas Martínez Spain 9 310 1.4× 86 0.7× 33 0.4× 74 1.1× 46 0.7× 15 672
Shabir Ahmad Sofi India 9 248 1.1× 128 1.0× 68 0.7× 55 0.8× 50 0.8× 45 571

Countries citing papers authored by María Culman

Since Specialization
Citations

This map shows the geographic impact of María Culman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by María Culman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites María Culman more than expected).

Fields of papers citing papers by María Culman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by María Culman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by María Culman. The network helps show where María Culman may publish in the future.

Co-authorship network of co-authors of María Culman

This figure shows the co-authorship network connecting the top 25 collaborators of María Culman. A scholar is included among the top collaborators of María Culman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with María Culman. María Culman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
1.
Delalieux, Stephanie, et al.. (2023). Red Palm Weevil Detection in Date Palm Using Temporal UAV Imagery. Remote Sensing. 15(5). 1380–1380. 11 indexed citations
2.
Culman, María, Stephanie Delalieux, Bart Beusen, & Ben Somers. (2023). Automatic labeling to overcome the limitations of deep learning in applications with insufficient training data: A case study on fruit detection in pear orchards. Computers and Electronics in Agriculture. 213. 108196–108196. 4 indexed citations
3.
Culman, María, et al.. (2021). Deep learning for sub-pixel palm tree classification using spaceborne Sentinel-2 imagery. 13. 14–14. 3 indexed citations
4.
Culman, María, Stephanie Delalieux, & Kristof Van Tricht. (2020). Individual Palm Tree Detection Using Deep Learning on RGB Imagery to Support Tree Inventory. Remote Sensing. 12(21). 3476–3476. 41 indexed citations
5.
Culman, María, Stephanie Delalieux, & Kristof Van Tricht. (2020). Palm Tree Inventory From Aerial Images Using Retinanet. 12 indexed citations
6.
Culman, María, et al.. (2019). Using agrometeorological data to assist irrigation management in oil palm crops: A decision support method and results from crop model simulation. Agricultural Water Management. 213. 1047–1062. 22 indexed citations
7.
Culman, María, et al.. (2017). PalmNET: An open-source wireless sensor network for oil palm plantations. 783–788. 7 indexed citations
8.
Escobar, Jairo Alejandro Gómez, et al.. (2017). A case study on monitoring and geolocation of noise in urban environments using the internet of things. 1–6. 5 indexed citations
9.
Tobòn, Luis, et al.. (2017). Review of IoT applications in agro-industrial and environmental fields. Computers and Electronics in Agriculture. 142. 283–297. 358 indexed citations breakdown →
10.
11.
Culman, María, et al.. (2014). Quality evaluation of energy consumed in flow regulation method by speed variation in centrifugal pumps. IOP Conference Series Materials Science and Engineering. 59. 12011–12011. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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