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Current palynological analysis in Europe is a slow and laborious process which provides pollen information in a weekly-cycle basis. In this paper, we describe a system that allows to locate and classify, in a single step, the pollen grains present in standard glass microscope slides. Besides, processing the samples in the z-axis allows us to increase the probability of detecting grains compared to solutions based on one image per sample. Our system has been trained to recognise 11 pollen types, achieving 97.6\u00a0% success rate locating grains, of which 96.3\u00a0% are also correctly identified (0.956 macro\u2013F1 score), and with a 2.4\u00a0% grains lost. Our results indicate that deep learning provides a robust framework to address automated identification of various pollen types, facilitating their daily measurement.<\/jats:p>","DOI":"10.1007\/s11042-024-18450-2","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T06:02:20Z","timestamp":1707285740000},"page":"72097-72112","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Automated multifocus pollen detection using deep learning"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9203-7289","authenticated-orcid":false,"given":"Ram\u00f3n","family":"Gallardo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos J.","family":"Garc\u00eda-Orellana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Horacio M.","family":"Gonz\u00e1lez-Velasco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Garc\u00eda-Manso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafael","family":"Tormo-Molina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miguel","family":"Mac\u00edas-Mac\u00edas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eugenio","family":"Abeng\u00f3zar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,7]]},"reference":[{"key":"18450_CR1","doi-asserted-by":"publisher","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Man\u00e9 D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Vi\u00e9gas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: Large-scale machine learning on heterogeneous systems. https:\/\/doi.org\/10.5281\/zenodo.4724125","DOI":"10.5281\/zenodo.4724125"},{"key":"18450_CR2","doi-asserted-by":"publisher","unstructured":"Arias DG, Cirne MVM, Chire JE, Pedrini H (2017) Classification of pollen grain images based on an ensemble of classifiers. 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