Augmenting Aquaculture Efficiency through Involutional Neural Networks and Self-Attention for Oplegnathus Punctatus Feeding Intensity Classification from Log Mel Spectrograms

Abstract

Simple Summary Managing fish feeding well is important for both making fish farming better and keeping aquatic environments healthy. By looking at the sounds fish make, this study suggests a new way to learn about how they eat. We turn these sounds into pictures and use advanced computer methods to figure out the different ways people eat. Our method uses a strong deep learning model that can correctly group the eating habits of a certain type of fish, which helps us figure out how much and how often they eat. With a 97% success rate, this method shows a lot of promise for better running fish farms and protecting marine ecosystems. In the future, researchers might be able to add more types of data to this method, which could give us even more information about how to farm fish sustainably and manage ecosystems. Abstract Understanding the feeding dynamics of aquatic animals is crucial for aquaculture optimization and ecosystem management. This paper proposes a novel framework for analyzing fish feeding behavior based on a fusion of spectrogram-extracted features and deep learning architecture. Raw audio waveforms are first transformed into Log Mel Spectrograms, and a fusion of features such as the Discrete Wavelet Transform, the Gabor filter, the Local Binary Pattern, and the Laplacian High Pass Filter, followed by a well-adapted deep model, is proposed to capture crucial spectral and spectral information that can help distinguish between the various forms of fish feeding behavior. The Involutional Neural Network (INN)-based deep learning model is used for classification, achieving an accuracy of up to 97% across various temporal segments. The proposed methodology is shown to be effective in accurately classifying the feeding intensities of Oplegnathus punctatus, enabling insights pertinent to aquaculture enhancement and ecosystem management. Future work may include additional feature extraction modalities and multi-modal data integration to further our understanding and contribute towards the sustainable management of marine resources.