Feeding intensity assessment of aquaculture fish using Mel Spectrogram and deep learning algorithms

Du, Zhuangzhuang; Meng, Chao; Wang, Qi; Liu, Xiaohang; Xu, Xianbao; Bai, Zhuangzhuang; Sun, Chen-Nan; Wang, Bingxiong; Wang, Shuaixing; Li, Daoliang

doi:10.1016/j.aquaeng.2023.102345

Cited by 10 publications

(4 citation statements)

References 42 publications

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“…The FFIA based on audio was initially proposed by [22], the audio signal is first transformed into acoustic features (i.e., log mel spectrograms) and then fed into a CNN-based model for FFIA. Similar work [38], [39] also demonstrates the feasibility of using audio as model input for FFIA. Compared with vision-based methods, acoustic measurements are more energy-efficient and involve lower computational costs (e.g., energy consumption, data storage cost) [40], [41], which are more suitable for on-device application [23], [42].…”

Section: B Audio-based Ffia Methodsmentioning

confidence: 58%

Fish Feeding Intensity Assessment in Aquaculture: A New Audio Dataset AFFIA3K and a Deep Learning Algorithm

Meng

Liu

Zhao

et al. 2022

2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP)

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Fish feeding intensity assessment (FFIA) aims to evaluate the change of fish appetite during the feeding process, which is potentially useful in industrial aquaculture. Previous methods are mainly based on computer vision techniques. However, these methods are limited by water refraction and uneven illumination. In this paper, we introduce a new approach for FFIA using audio. We create a new audio dataset for FFIA, namely AFFIA3K, which contains 3000 labelled audio clips of different fish feeding intensity (None, Weak, Medium, Strong). We present a deep learning framework for FFIA, where the audio signal is first transformed into acoustic features, i.e. mel spectrogram, then a convolutional neural network (CNN)-based model is used to classify the fish feeding intensity. Experimental results show that our approach achieves an mAP of 0.74 on the test set of AFFIA3K, and considerably outperforms baseline systems. This indicates the potential of our proposed approach in aquaculture applications.

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Section: B Audio-based Ffia Methodsmentioning

confidence: 58%

Fish Feeding Intensity Assessment in Aquaculture: A New Audio Dataset AFFIA3K and a Deep Learning Algorithm

Meng

Liu

Zhao

et al. 2022

2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP)

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“…The growth, health, and sustainability of aquaculture depend on fish-feeding behavior [ 16 , 19 ]. Researchers can improve fish growth by studying their feeding behavior, while reducing waste and environmental impact.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Various studies have applied deep learning techniques to investigate fish feeding habits. Du et al [ 16 ] developed a novel MobileNetV3-SBSC model for analyzing fish feeding behaviors by utilizing a Mel spectrogram—which visually depicts audio frequencies mapped to the human-perceptible Mel scale—and its attributes to categorize three levels of feeding intensity: vigorous, moderate, and none. They achieved 85.9% accuracy, representing substantial progress toward using deep learning to decode aquaculture-related actions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Deep learning models have already been used in environmental sound classification [ 7 , 8 ], image classification [ 9 , 10 ], signal classification [ 11 , 12 ], and many medical applications [ 13 , 14 , 15 ]. Deep learning techniques have been used in aquaculture to address the problems of overfeeding and underfeeding, while simultaneously creating an efficient feeding system [ 16 ]. The ability of deep learning models to identify complex patterns from vast datasets presents an opportunity to improve aquaculture farms.…”

Section: Introductionmentioning

confidence: 99%

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

Iqbal,

Li,

et al. 2024

Animals

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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.

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Soundscape characteristics of RAS tanks holding Atlantic salmon (Salmo salar) during feeding and feed withdrawal

Helberg,

Anichini,

Kolarevic

et al. 2024

Aquaculture

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Feeding intensity assessment of aquaculture fish using Mel Spectrogram and deep learning algorithms

Cited by 10 publications

References 42 publications

Fish Feeding Intensity Assessment in Aquaculture: A New Audio Dataset AFFIA3K and a Deep Learning Algorithm

Fish Feeding Intensity Assessment in Aquaculture: A New Audio Dataset AFFIA3K and a Deep Learning Algorithm

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

Soundscape characteristics of RAS tanks holding Atlantic salmon (Salmo salar) during feeding and feed withdrawal

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