Erick Mata‐Montero

742 total citations
25 papers, 459 citations indexed

About

Erick Mata‐Montero is a scholar working on Ecological Modeling, Plant Science and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Erick Mata‐Montero has authored 25 papers receiving a total of 459 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Ecological Modeling, 10 papers in Plant Science and 9 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Erick Mata‐Montero's work include Species Distribution and Climate Change (10 papers), Smart Agriculture and AI (9 papers) and Plant and animal studies (8 papers). Erick Mata‐Montero is often cited by papers focused on Species Distribution and Climate Change (10 papers), Smart Agriculture and AI (9 papers) and Plant and animal studies (8 papers). Erick Mata‐Montero collaborates with scholars based in Costa Rica, France and United States. Erick Mata‐Montero's co-authors include Hervé Goëau, Pierre Bonnet, Alexis Joly, Julien Champ, Dagoberto Arias‐Aguilar, Titouan Lorieul, Patrick W. Sweeney, Susan J. Mazer, Joel L. Sachs and Pamela S. Soltis and has published in prestigious journals such as SHILAP Revista de lepidopterología, Frontiers in Plant Science and BMC Evolutionary Biology.

In The Last Decade

Erick Mata‐Montero

23 papers receiving 444 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Erick Mata‐Montero Costa Rica 9 232 132 123 93 66 25 459
Jonathan Y. Clark United Kingdom 10 321 1.4× 62 0.5× 94 0.8× 92 1.0× 52 0.8× 19 483
Hervé Goëau France 12 630 2.7× 277 2.1× 282 2.3× 176 1.9× 97 1.5× 44 1.0k
Mauro dos Santos de Arruda Brazil 8 238 1.0× 24 0.2× 237 1.9× 28 0.3× 14 0.2× 11 475
Youjie Zhao China 13 200 0.9× 23 0.2× 78 0.6× 134 1.4× 308 4.7× 51 570
James Cope United Kingdom 4 259 1.1× 21 0.2× 81 0.7× 37 0.4× 32 0.5× 8 402
Antônia Railda Roel Brazil 13 478 2.1× 9 0.1× 83 0.7× 55 0.6× 129 2.0× 48 642
José San Martín Chile 13 164 0.7× 30 0.2× 83 0.7× 154 1.7× 40 0.6× 77 549
Julien Champ France 9 137 0.6× 86 0.7× 86 0.7× 62 0.7× 24 0.4× 14 322
Sue Han Lee Malaysia 7 727 3.1× 19 0.1× 178 1.4× 19 0.2× 35 0.5× 15 860
Ziyuan Hao China 10 252 1.1× 24 0.2× 103 0.8× 21 0.2× 150 2.3× 32 428

Countries citing papers authored by Erick Mata‐Montero

Since Specialization
Citations

This map shows the geographic impact of Erick Mata‐Montero'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 Erick Mata‐Montero with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erick Mata‐Montero more than expected).

Fields of papers citing papers by Erick Mata‐Montero

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Erick Mata‐Montero. 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 Erick Mata‐Montero. The network helps show where Erick Mata‐Montero may publish in the future.

Co-authorship network of co-authors of Erick Mata‐Montero

This figure shows the co-authorship network connecting the top 25 collaborators of Erick Mata‐Montero. A scholar is included among the top collaborators of Erick Mata‐Montero 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 Erick Mata‐Montero. Erick Mata‐Montero is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mata‐Montero, Erick, et al.. (2024). Discovering Diagnostic Features Used by a CNN in Plant Species Identification. Computación y Sistemas. 28(4).
2.
Mata‐Montero, Erick, et al.. (2022). Using Deep Learning to Identify Costa Rican Native Tree Species From Wood Cut Images. Frontiers in Plant Science. 13. 789227–789227. 14 indexed citations
3.
Mata‐Montero, Erick, et al.. (2020). Using a Convolutional Siamese Network for Image-Based Plant Species Identification with Small Datasets. Biomimetics. 5(1). 8–8. 35 indexed citations
4.
Champ, Julien, et al.. (2020). Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots. Applications in Plant Sciences. 8(7). e11373–e11373. 90 indexed citations
5.
Goëau, Hervé, Julien Champ, Susan J. Mazer, et al.. (2020). A new fine‐grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction. Applications in Plant Sciences. 8(6). e11368–e11368. 29 indexed citations
6.
Goëau, Hervé, et al.. (2020). Domain Adaptation in the Context of Herbarium Collections: A submission to PlantCLEF 2020.. 1 indexed citations
7.
Beck, Fabian, et al.. (2020). An expert study on hierarchy comparison methods applied to biological taxonomies curation. PeerJ Computer Science. 6. e277–e277. 1 indexed citations
8.
Goëau, Hervé, et al.. (2020). Segmentación de instancias para detección automática de malezas y cultivos en campos de cultivo. SHILAP Revista de lepidopterología. 2 indexed citations
9.
Goëau, Hervé, et al.. (2019). Accelerating the Automated Detection, Counting and Measurements of Reproductive Organs in Herbarium Collections in the Era of Deep Learning. Biodiversity Information Science and Standards. 3. 5 indexed citations
10.
Lorieul, Titouan, Katelin D. Pearson, Elizabeth R. Ellwood, et al.. (2019). Toward a large‐scale and deep phenological stage annotation of herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Applications in Plant Sciences. 7(3). e01233–e01233. 47 indexed citations
11.
Mata‐Montero, Erick, et al.. (2018). Hidden Biases in Automated Image-Based Plant Identification. abs 1411 1792. 1–9. 11 indexed citations
12.
Mata‐Montero, Erick, et al.. (2018). A 3D Approach for the Visualization and Edition of Hierarchies: The Case of Biological Taxonomies. 22. 1–6. 1 indexed citations
13.
Mata‐Montero, Erick, et al.. (2018). Deep Learning for Forest Species Identification Based on  Macroscopic Images. Zenodo (CERN European Organization for Nuclear Research). 2. e25261–e25261. 1 indexed citations
14.
15.
Mata‐Montero, Erick, et al.. (2018). A Methodological Proposal for Collecting and Creating Macroscopic Photograph Collections of Tropical Woods with Potential for Use in Deep Learning. Biodiversity Information Science and Standards. 2. e25260–e25260. 3 indexed citations
16.
Goëau, Hervé, et al.. (2017). Going deeper in the automated identification of Herbarium specimens. BMC Evolutionary Biology. 17(1). 181–181. 159 indexed citations
17.
Joly, Alexis, et al.. (2017). Automated Herbarium Specimen Identification using Deep Learning. Biodiversity Information Science and Standards. 1. e20302–e20302. 6 indexed citations
18.
Mata‐Montero, Erick, et al.. (2015). A texture and curvature bimodal leaf recognition model for identification of Costa Rican plant species. 1–12. 8 indexed citations
19.
Mata‐Montero, Erick, et al.. (2009). Biovisualizador: Visualizando los anfibios de Costa Rica. SHILAP Revista de lepidopterología. 22(1). 15–23. 1 indexed citations
20.
Mata‐Montero, Erick. (1991). Resilience of partial k‐tree networks with edge and node failures. Networks. 21(3). 321–344. 6 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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