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

Author:

Iqbal Usama1,Li Daoliang2ORCID,Du Zhuangzhuang3,Akhter Muhammad4,Mushtaq Zohaib5ORCID,Qureshi Muhammad Farrukh6ORCID,Rehman Hafiz Abbad Ur7

Affiliation:

1. National Innovation Center for Digital Fishery, Beijing 100083, China

2. Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, Beijing 100083, China

3. Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China

4. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

5. Department of Electrical, Electronics and Computer Systems, University of Sargodha, Sargodha 40100, Pakistan

6. Department of Electrical and Computer Engineering, Riphah International University, Islamabad 44000, Pakistan

7. School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada

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.

Funder

creation and application of a green and efficient intelligent factory for aquaculture

key technology research; and the creation of digital fishery intelligent equipment

Publisher

MDPI AG

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