FN Archimer Export Format PT J TI Automatic recognition of flow cytometric phytoplankton functional groups using convolutional neural networks BT AF Fuchs, Robin Thyssen, Melilotus Creach, Véronique Dugenne, Mathilde Izard, Lloyd Latimier, Marie Louchart, Arnaud Marrec, Pierre Rijkeboer, Machteld Grégori, Gérald Pommeret, Denys AS 1:1,2;2:2;3:3;4:4;5:5;6:6;7:7,8;8:9;9:10;10:2;11:1,11,12,13; FF 1:;2:;3:;4:;5:;6:PDG-ODE-DYNECO-PELAGOS;7:PDG-ODE-LITTORAL-LERBL;8:;9:;10:;11:; C1 Aix Marseille Univ, CNRS, I2M Marseille ,France Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO Marseille ,France Cefas, Suffolk, UK Department of Oceanography University of Hawai'i at Manoa Honolulu Hawai'i ,USA Sorbonne Université, CNRS, IRD, MNHN, Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN‐IPSL) Paris ,France IFREMER, DYNECO PELAGOS Plouzane, France Department of Integrative Marine Ecology Stazione Zoologica Anton Dohrn, Villa Comunale Naples, Italy IFREMER, Laboratoire Environnement and Ressources Boulogne‐sur‐Mer ,France Graduate School of Oceanography University of Rhode Island Narragansett Rhode Island, USA Laboratory for Hydrobiological Analysis, Rijkswaterstaat (RWS) Lelystad ,The Netherlands Université Claude Bernard Lyon 1 Villeurbanne ,France ISFA Lyon ,France Laboratoire de Sciences Actuarielle et Financière (SAF) Lyon, France C2 UNIV AIX MARSEILLE, FRANCE UNIV AIX MARSEILLE, FRANCE CEFAS, UK UNIV HAWAII MANOA, USA UNIV SORBONNE, FRANCE IFREMER, FRANCE STAZ ZOOL ANTON DOHRN, ITALY IFREMER, FRANCE UNIV RHODE ISL, USA RWS, NETHERLANDS UNIV LYON, FRANCE ISFA, FRANCE UNIV LYON, FRANCE SI BREST BOULOGNE SE PDG-ODE-DYNECO-PELAGOS PDG-ODE-LITTORAL-LERBL IN WOS Ifremer UPR copubli-france copubli-europe copubli-univ-france copubli-int-hors-europe IF 2.7 TC 5 UR https://archimer.ifremer.fr/doc/00775/88654/94352.pdf https://archimer.ifremer.fr/doc/00775/88654/94353.pdf LA English DT Article CR FUMSECK SWINGS BO Téthys II Marion Dufresne AB The variability of phytoplankton distribution has been unraveled by high-frequency measurements. Such a resolution can be approached by automated pulse-shape recording flow cytometry (AFCM) operating at hourly sampling resolution. AFCM records morphological and physiological traits as single-cell optical pulse shapes that can be used to classify cells into phytoplankton functional groups (PFGs). However, the associated manual post-processing of the data coupled with the increasing size and number of datasets is time-consuming and error-prone. Machine learning models are increasingly used to run automatic classification. Yet, most of the existing methods either present a long training process, need to manually design features from the raw optical pulse shapes, or are dedicated to images only. In this study, we present a convolutional neural network (CNN) to classify several PFGs using AFCM pulse shapes. The uncertainties of manual classification were first estimated by comparing experts' recognition of six PFGs. Consensual particles from the manual PFG classification were used to train and validate the CNN. The CNN obtained competitive performances compared to other models used in the literature and remained robust across several sampling areas, and instrumental hardware and settings. Finally, we assessed the ability of this classifier to predict phytoplankton counts at a Mediterranean coastal station and from a cruise in the South-West Indian Ocean, providing a comparison with the manual classification over 3-month periods and a 2h frequency. These promising results strengthen the near real-time observation of PFGs, especially required with the increasing use of AFCM in monitoring research programs. PY 2022 PD JUN SO Limnology And Oceanography-methods SN 1541-5856 PU Wiley VL 20 IS 7 UT 000804784300001 BP 387 EP 399 DI 10.1002/lom3.10493 ID 88654 ER EF