In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …

Intelligence technologies play an important role in increasing product quality and production
efficiency in digital aquaculture. Automatic fish detection will contribute to achieving …

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 …

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 …

In aquaculture, information on fish appetite is of great significance for guiding feeding and
production practices. However, most fish appetite assessment methods are inefficient and …

The present study approaches towards the feasibility of prediction and forecasting of
dissolved oxygen (DO) and biofloc amount using the state of art machine learning …

Automatic classification of different species of fish is important for the comprehension of
marine ecology, fish behaviour analysis, aquaculture management, and fish health …

Scientific studies on species composition and abundance distribution of fishes have
considerable importance to the fishery industry, biodiversity protection, and marine …

This paper proposes a novel crack detection method using the three-stages detection
model. Deep learning technology has been a focus of attention in the field of crack detection; …

Fish classification (FC) is crucial in various domains, including fishery management and
ecological research. Traditional FC methods rely mainly on morphological criteria such as …