Recognition of adherent polychaetes on oysters and scallops using Microsoft Azure Custom Vision

Abstract

Oyster and scallop cultures have high growth rates in the Korean aquaculture industry. However, their production is declining because of the manual selection of polychaete-adherent oysters and scallops. In this study, an artificial intelligence model for automatic selection of polychaetes was developed using Microsoft Azure Custom Vision to improve the productivity of oysters and scallops. A camera booth was built to capture images of oysters and scallops from various angles. Polychaetes in the images were tagged. Transfer learning available with Custom Vision was performed on the acquired images. By repeating the training and evaluation, the number of training images was increased by analyzing the precision, recall, and mean average precision using the Compact [S1] and General [A1] domains of Custom Vision. This paper presents the artificial intelligence model developed for the automatic selection of polychaete-adherent oysters and scallops as well as the optimal model development method using Microsoft Azure Custom Vision.

Keywords

• object detection
• transfer learning
• aquaculture
• custom vision
• azure