Data/hora: 01/06/2024 09:52:54

Provedor de dados: 	5	
País: 	France	
Título: 	Optimisation de l’identification et du dénombrement du microphytoplancton avec le système couplé de numérisation et d’analyse d’images FlowCAM / ZooPhytoImage (système innovant). Action 9 – Livrable 2. Mise en oeuvre opérationnelle de l’outil FlowCAM-Zoo/PhytoImage dans le cadre de la surveillance REPHY. Résultats sur 12- 18 mois. Rapport final, février 2016	
Autores: 	Neaud-masson, Nadine; Lefebvre, Alain; Belin, Catherine; Gauthier, Emilie; Huguet, Antoine; Szidon, Antoine; Lefebvre, Arnaud; Cuvelliez, Rémi.	
Data: 	2016	
Ano: 	2016	
Palavras-chave: 	Phytoplancton; FlowCAM; Zoo/PhytoImage; Numérisation; Analyse d’images; Set d’apprentissage; Outil de reconnaissance; Architecture de données; Bancarisation; Quadrige; Phytoplankton; FlowCAM; Zoo/PhytoImage; Scanning; Image analysis; Training set; Recognition tool; Data architecture; Banking; Quadrige.	
Resumo: 	This deliverable includes several documents that reflect the operational implementation of the tool FlowCAM / ZooPhytoImage in the framework of REPHY. Among these documents, three reports make an assessment of the entire year 2015, which is not the case with other documents (Master 2 report issued in spring 2015, and minutes of meetings over the year). This summary will be limited to the findings of the three reports providing an updated view of the implementation of the tool. Work completed in 2015 for the operational implementation. The many scans performed in 2015 with the three FlowCAM present at Ifremer (Boulogne‐sur‐Mer, Arcachon and Nantes) allowed, one hand to feed the training sets of the tool FlowCAMZoo/PhytoImage, the other test its routine use. The time required for scanning all water samples collected in the Rephy Observation (37 points sampled every two weeks) was evaluated at 4.9 months, it remains to evaluate the processing time for scans with the software Zoo / PhytoImage. Specific training sets for distinct coastlines Channel and the Atlantic were performed in 2015 to improve recognition tool. The training set optimized for the Atlantic now includes 7388 pictures in 62 groups with an overall error rate of 26.3%. Improvements are still possible: indeed, recognition performance assessment tools offered by the Zoo/PhytoImage software can identify actions to improve the optimization of a set. The exploitation of results of performance analysis has been particularly used to control the Dinophysis group: in this set optimized for the Atlantic, this group has a 15.5% error rate and gets 80% of true positives, which is very promising and could be further improved. A comparison of Dinophysis abundances results obtained with the microscope and with the optimized tool was also conducted. The results of the test sample shows that the abundances obtained with the tool are underestimated. However, the nonparametric Wilcoxon test does not demonstrate a significant difference in count Dinophysis between the two methods, when pursuing the error corrections to manual validation of all the erroneous particles up to about 5 %. Delivery of the latest version of Zoo/PhytoImage v5.4, late 2015, now led to have an operational reconnaissance tool for routine analysis of samples collected on the Atlantic coast in the framework of REPHY. An illustrated catalog of images of a training set, for users of Zoo/PhytoImage, presents a sample of images of each class representing the training set Channel‐Atlantic 4X, and also the classes whose abundances insufficient to be integrated into the set, for which the collection of new images is expected. Outlook and conclusions of a study on the banking data from Zoo/PhytoImage. The treatment of phytoplankton images via Zoo/PhytoImage software, products extensive data (identification, abundance, biovolume, and other metrics) on the particles of the samples. An expertise study was conducted to propose a technical architecture of the entire acquisition chain, and to define data integration requirements in Quadrige database. In terms of architecture, a shared network drive is currently allocated to these data: the study showed the relevance of this architecture for current needs. However, the current storage will be soon insufficient. The shared computing equipment project (supercomputer) and large volume data management appointed DATARMOR, being specified within Ifremer, was defined as the target for backup data horizon of a few years. In terms of banking in Quadrige, it was decided to only bank information on the scale of phytoplancton taxa, not on the scale of the particle. Thus, the information on particles not belonging to the phytoplankton taxa will not be banked in Quadrige but remain stored in the server for possible future use or re‐sets. It will be the same for metrics not offering immediate interest on phytoplankton particles: the precise list of useful metric to be defined, and statistical parameters to open bank accounts still need to be validated (at least median and minimum and maximum values, for each metric describing the set of particles associated with the same taxon). Practically, the results files produced by Zoo/PhytoImage (.RData) will be processed by an R script in .csv files, and then, via a Talend job, in Quadrilabo files directly integrated into Quadrige. The Quadrige repository must be completed to integrate the parameters corresponding to the new metrics, the methods of analysis, etc. All this will feed into the Quadrige3 specifications.	
Tipo: 	Text	
Idioma: 	Francês	
Identificador: 	http://archimer.ifremer.fr/doc/00363/47413/47409.pdf

http://archimer.ifremer.fr/doc/00363/47413/	
Formato: 	application/pdf	
Fonte: 	2016 Onema, Ifremer

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