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

Autor: Usama Iqbal, Daoliang Li, Zhuangzhuang Du, Muhammad Akhter, Zohaib Mushtaq, Muhammad Farrukh Qureshi, Hafiz Abbad Ur Rehman
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Animals, Vol 14, Iss 11, p 1690 (2024)
Druh dokumentu: article
ISSN: 2076-2615
DOI: 10.3390/ani14111690
Popis: 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.
Databáze: Directory of Open Access Journals
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