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240529s2024 xx |||||o 00| ||eng c |
| 024 |
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|a 10.1111/nph.19857
|2 doi
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|a pubmed24n1477.xml
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|a (DE-627)NLM372955584
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|a (NLM)38807290
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Gimenez, Betty
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A user-friendly method to get automated pollen analysis from environmental samples
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|c 2024
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 20.06.2024
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|a Date Revised 20.07.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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| 520 |
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|a © 2024 The Authors. New Phytologist © 2024 New Phytologist Foundation.
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|a Automated pollen analysis is not yet efficient on environmental samples containing many pollen taxa and debris, which are typical in most pollen-based studies. Contrary to classification, detection remains overlooked although it is the first step from which errors can propagate. Here, we investigated a simple but efficient method to automate pollen detection for environmental samples, optimizing workload and performance. We applied the YOLOv5 algorithm on samples containing debris and c. 40 Mediterranean plant taxa, designed and tested several strategies for annotation, and analyzed variation in detection errors. About 5% of pollen grains were left undetected, while 5% of debris were falsely detected as pollen. Undetected pollen was mainly in poor-quality images, or of rare and irregular morphology. Pollen detection remained effective when applied to samples never seen by the algorithm, and was not improved by spending time to provide taxonomic details. Pollen detection of a single model taxon reduced annotation workload, but was only efficient for morphologically differentiated taxa. We offer guidelines to plant scientists to analyze automatically any pollen sample, providing sound criteria to apply for detection while using common and user-friendly tools. Our method contributes to enhance the efficiency and replicability of pollen-based studies
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Mediterranean vegetation monitoring
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| 650 |
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4 |
|a YOLOv5
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| 650 |
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4 |
|a artificial intelligence
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| 650 |
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4 |
|a automated pollen analysis
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| 650 |
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4 |
|a deep learning
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| 650 |
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4 |
|a detection errors
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| 650 |
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4 |
|a environmental real‐world samples
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| 650 |
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4 |
|a guidelines
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| 700 |
1 |
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|a Joannin, Sébastien
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pasquet, Jérôme
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Beaufort, Luc
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gally, Yves
|e verfasserin
|4 aut
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| 700 |
1 |
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|a de Garidel-Thoron, Thibault
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Combourieu-Nebout, Nathalie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bouby, Laurent
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Canal, Sandrine
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ivorra, Sarah
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Limier, Bertrand
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Terral, Jean-Frédéric
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Devaux, Céline
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Peyron, Odile
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t The New phytologist
|d 1979
|g 243(2024), 2 vom: 01. Juli, Seite 797-810
|w (DE-627)NLM09818248X
|x 1469-8137
|7 nnns
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| 773 |
1 |
8 |
|g volume:243
|g year:2024
|g number:2
|g day:01
|g month:07
|g pages:797-810
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| 856 |
4 |
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|u http://dx.doi.org/10.1111/nph.19857
|3 Volltext
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|d 243
|j 2024
|e 2
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|h 797-810
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