Fish feeding intensity quantification using machine vision and a lightweight 3D ResNet-GloRe network

Feng, Shuangxing; Yang, Xinting; Liu, Yang; Zhao, Zhengxi; Liu, Jintao; Yan, Yujie; Zhou, Chao

doi:10.1016/j.aquaeng.2022.102244

Cited by 22 publications

(10 citation statements)

References 54 publications

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“…But how to extract efficient features as the graph features and construct the graph are the key to achieving this graph classification task. The spatial-temporal characteristics of fish feeding behavior shows great potential in fish appetite assessment (Wei et al, 2021;Feng et al, 2022), therefore, the feature extraction and graph construction in this study are carried out for the representation of these spatial-temporal characteristics.…”

Section: Feature Extraction and Graph Constructionmentioning

confidence: 99%

“…From this, Wei et al (2021) developed a method based on the modified kinetic energy model and customized recurrent neural network (RNN) to comprehensively utilize the spatial-temporal characteristics of fish feeding behavior, which therefore made fish appetite evaluation more accurate and practical. Similarly, by exploiting the spatial-temporal characteristics of fish feeding behavior, Feng et al (2022) also realized the precise quantification of fish appetite resorted to a lightweight 3D ResNet-GloRe network, although a feeding strategy not commonly used in real production was adopted.…”

Section: Introductionmentioning

confidence: 99%

“…In real production, in order to leave enough reaction time for the follow-up feeding control (including feeding strategy adjustment), the sooner of fish appetite assessment, the better. For this, increasing hardware investment seems to be a simplest and most direct solution, nevertheless, this will undoubtedly increase the production costs and affect the economic benefits (Feng et al, 2022). A promising alternative to the above solution is decreasing the time-duration of input data, namely, grading fish appetite using time-limited data particularly the beginning of time series data.…”

Section: Introductionmentioning

confidence: 99%

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Modified kinetic energy feature-based graph convolutional network for fish appetite grading using time-limited data in aquaculture

Wei

et al. 2022

Front. Mar. Sci.

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Feed has the greatest impact on the carbon footprint of the aquaculture, and also determines the water quality in aquaculture to a great extent. Making appropriate feeding control strategies is one of the most effective ways to promote cleaner production as well as fish welfare in aquaculture. Reliable and accurate fish appetite grading especially based on time-limited data is a prerequisite for achieving high-precision and reasonable feeding control in practical production. To date, however, few efforts have been done on this challenge. For these, regarding Micropterus salmoides as the experimental fish, a novel and practical method, based on a modified kinetic energy feature-based graph convolutional network (GCN), was developed in this study. First, graphs were constructed based on the extracted modified kinetic energy features and their temporal correlation. Then, with the help of a series of the convolution and global pooling operations, a GCN model was customized based on the constructed graphs. Following this, the customized GCN model was enriched by the self-attention pooling mechanism and customized network structure. Results show that the proposed GCN-based approach outperforms other typical state-of-the-art methods in fish appetite grading, and the grading accuracy obtained here could be 98.60% using only the first 4.2 seconds as well as the first 8.3 seconds of input data, which is not much different from that (98.89%) using full-length (25 second-long) input data. What’s more, compared to the recurrent neural network (RNN)-based method which performance is closest to our method, the space complexity of the proposed approach here can better satisfy the requirements of real aquaculture, in which the quantity of the trainable parameters here is only 6.4% ~ 31.8% of the RNN-based method. In summary, the proposed modified kinetic energy feature-based GCN approach is favorable for the appetite grading of fish like Micropterus salmoides with time-limited data, which is a promising approach in dealing with feeding control tasks and alleviating the water environmental burden in aquaculture.

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Section: Feature Extraction and Graph Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modified kinetic energy feature-based graph convolutional network for fish appetite grading using time-limited data in aquaculture

Wei

et al. 2022

Front. Mar. Sci.

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“…Feeding is one of the most important variable costs in aquaculture [8]. To overcome the limitations of human-based observation and optimize feeding strategies to reduce costs, many aquaculture factories use automatic feeding machines for fish feeding [6], [14].…”

Section: Related Workmentioning

confidence: 99%

“…However, fish-feeding behaviour is a dynamic and continuous process. Single images are insufficient to capture the context of fish feeding intensity [8]. As an alternative, video-based methods have been proposed to exploit spatial and temporal visual information for FFIA, which offers rich context for capturing fish feeding behaviour.…”

mentioning

confidence: 99%

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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Classifying aquatic organism via evolving residual convolutional neural networks by optimized random vector functional link

Zhou,

Wen,

Liu

et al. 2024

Multimed Tools Appl

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Fish feeding intensity quantification using machine vision and a lightweight 3D ResNet-GloRe network

Cited by 22 publications

References 54 publications

Modified kinetic energy feature-based graph convolutional network for fish appetite grading using time-limited data in aquaculture

Modified kinetic energy feature-based graph convolutional network for fish appetite grading using time-limited data in aquaculture

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

Classifying aquatic organism via evolving residual convolutional neural networks by optimized random vector functional link

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