Milan Šulc

About

Milan Šulc is a scholar working on Plant Science, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Milan Šulc has authored 14 papers receiving a total of 137 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Plant Science, 5 papers in Computer Vision and Pattern Recognition and 3 papers in Artificial Intelligence. Recurrent topics in Milan Šulc’s work include Smart Agriculture and AI (8 papers), Spectroscopy and Chemometric Analyses (3 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Milan Šulc is often cited by papers focused on Smart Agriculture and AI (8 papers), Spectroscopy and Chemometric Analyses (3 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Milan Šulc collaborates with scholars based in Czechia, Denmark and Japan. Milan Šulc's co-authors include Jiřı́ Matas, Jacob Heilmann‐Clausen, Thomas Stjernegaard Jeppesen, Yash Patel, Dmytro Mishkin, Tobias Guldberg Frøslev, Alexis Joly, Hervé Goëau, Pierre Bonnet and Nikolay Chumerin and has published in prestigious journals such as Frontiers in Plant Science, Sensors and Plant Methods.

In The Last Decade

Co-authorship network of co-authors of Milan Šulc i

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Fields of papers citing papers by Milan Šulc

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Milan Šulc. 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 Milan Šulc. The network helps show where Milan Šulc may publish in the future.

Countries citing papers authored by Milan Šulc

Since Specialization

Citations

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

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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