FN Archimer Export Format
PT J
TI Pearl shape classification using deep convolutional neural networks from Tahitian pearl rotation in Pinctada Margaritifera
BT 
AF Edeline, Paul-Emmanuel
   Leclercq, Mickaël
   LE LUYER, Jeremy
   Chabrier, Sébastien
   Droit, Arnaud
AS 1:1,2;2:1;3:3;4:2;5:1;
FF 1:;2:;3:PDG-RBE-RMPF;4:;5:;
C1 Département de médecine moléculaire, Faculté de Médecine, Université Laval, Canada
   Géopole du Pacifique Sud, Université de Polynésie Française, France
   Institut Français de Recherche pour l’Exploitation de la Mer, Vairao, Tahiti, French Polynesia
C2 UNIV LAVAL, CANADA
   UNIV POLYNESIE FRANCAISE, FRANCE
   IFREMER, FRANCE
SI TAHITI
SE PDG-RBE-RMPF
UM EIO
IN WOS Ifremer UMR
   DOAJ
   copubli-france
   copubli-univ-france
   copubli-int-hors-europe
IF 4.6
TC 0
UR https://archimer.ifremer.fr/doc/00841/95275/103033.pdf
   https://archimer.ifremer.fr/doc/00841/95275/103034.pdf
   https://archimer.ifremer.fr/doc/00841/95275/104205.pdf
   https://archimer.ifremer.fr/doc/00841/95275/104206.pdf
LA English
DT Article
AB Tahitian pearls, artificially cultivated from the black-lipped pearl oyster Pinctada margaritifera, are renowned for their unique color and large size, making the pearl industry vital for the French Polynesian economy. Understanding the mechanisms of pearl formation is essential for enabling quality and sustainable production. In this paper, we explore the process of pearl formation by studying pearl rotation. Here we show, using a deep convolutional neural network, a direct link between the rotation of the pearl during its formation in the oyster and its final shape. We propose a new method for non-invasive pearl monitoring and a model for predicting the final shape of the pearl from rotation data with 81.9% accuracy. These novel resources provide a fresh perspective to study and enhance our comprehension of the overall mechanism of pearl formation, with potential long-term applications for improving pearl production and quality control in the industry. 
PY 2023
PD AUG
SO Scientific Reports
SN 2045-2322
PU Nature Research
VL 13
IS 1
UT 001049345000032
DI 10.1038/s41598-023-40325-z
ID 95275
ER

EF