Among the background of developments in automation and intelligence, machine learning
technology has been extensively applied in aquaculture in recent years, providing a new …

The rapid emergence of deep learning (DL) technology has resulted in its successful use in
various fields, including aquaculture. DL creates both new opportunities and a series of …

Features are the vital factor for image classification in the field of machine learning. The
advancement of deep convolutional neural network (CNN) shows the way for identification …

In aquaculture, the real-time detection and monitoring of feed pellet consumption is an
important basis for formulating scientific feeding strategies that can effectively reduce feed …

The world is struggling to solve a devastating biodiversity loss that not only affects the
extinction of treasured species and irreplaceable genetic variation, but also jeopardizes the …

The ocean is an important ecosystem, and aquatic animals play an important role in the
biological world, especially in aquaculture. How to accurately and intelligently recognise …

Monitoring the growth conditions and behavior of fish will enable scientific management,
reduce the threat of losses caused by disease and stress. Traditional monitoring methods …

Computer vision has been applied to fish recognition for at least three decades. With the
inception of deep learning techniques in the early 2010s, the use of digital images grew …

Abstract Each year, 1.3 billion tons of food is lost due to spoilage or loss in the supply chain,
accounting for approximately one third of global food production. This requires a …

Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the
security and robustness of the deployed algorithms need to be guaranteed. The security …