Automatic Counting Method of Fry Based on Computer Vision

Abstract

Nowadays, fish farming plays an essential role in aquaculture. In the process of fry sales, the traditional manual counting method takes a great deal of time. Because the fish are constantly moving in the water, they are difficult to count and are likely to be unnecessarily damaged. With the rise of computer vision, fry counting based on image processing is also developing. It has the advantages of having high accuracy and fast processing speed. The primary counting process is divided into detection, adhesion segmentation, and fry counting. First, we improved the maximum interclass variance threshold selection method, and we extracted the histogram features under different channels to detect the fry; then, a matching segmentation method based on concave and skeleton crossing points was used to segment the adherent fry to improve the counting accuracy, the concaves were detected by CNN. We tested the method on real fry data sets, and the average accuracy reached 97%. Our approach had better accuracy than any existing current fish counting methods. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Automatic Shrimp Fry Counting Method Using Multi-Scale Attention Fusion

“…With the rapid development of artificial intelligence technology, the emerging field of smart aquaculture has emerged, which aims to improve the yield and efficiency of aquaculture through computer vision and deep learning [7,8]. Shrimp fry counting, as a research direction of smart aquaculture [9], is widely favored by researchers and producers for its high efficiency, low cost, and easy operation. With the aid of a terminal device (e.g., a mobile phone) embedded with this method, fishermen do not need to know the specific details of the method; they only need to take an image of the shrimp fry to automatically obtain the number of shrimp fry.…”