High growth rates of Asian seabass (
            <i>Lates calcarifer</i>
            Bloch, 1790) fry reared using a demand feeder with an image processing system for detecting fish behaviour

Mukai, Yukinori; Taha, Zahari; Ismail, Khairul Muttaqin; Tan, Nai Han; Razman, Mohd Azraai Mohd; Jizat, Jessnor Arif Mat

doi:10.1111/are.15380

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Some sensors are used for quantifying fish behaviours (Lorena et al, 2018), especially feeding activity (Mukai et al, 2021). They include mechanical switches, infrared light sensor and water quality sensors.…”

Section: Sensorsmentioning

confidence: 99%

Nonintrusive and automatic quantitative analysis methods for fish behaviour in aquaculture

Liu

Bienvenido

Yang

et al. 2022

Aquaculture Research

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In aquaculture, accurate and automatic quantification of fish behaviour can provide useful data input for production management and decision-making. In recent years, with the focus on fish welfare, it has become urgent to study and use nondestructive quantitative methods of fish behaviour in aquaculture. In this paper, based on the literature of the past 30 years, nonintrusive and automatic quantitative methods for fish behaviour are analysed. Firstly, several important fish behaviours in aquaculture are listed, and the quantification of fish behaviour is summarized in four stages: detection, tracking, feature extraction and behaviour recognition. Then, nonintrusive methods of fish behaviour quantification, through machine vision, acoustics and sensors, and their advantages and disadvantages are also compared and discussed in detail. It is concluded that the combination of multiple methods and deep learning is a key technology for fish behaviour quantification, which has gradually become a popular focus of research and application in recent years. This review can be used as a reference to improve fish behaviour quantification in future, so as to create a more effective and economic technical method.

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Section: Sensorsmentioning

confidence: 99%

Nonintrusive and automatic quantitative analysis methods for fish behaviour in aquaculture

Liu

Bienvenido

Yang

et al. 2022

Aquaculture Research

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“…They can be integrated with computer software that records data related to feeding behaviour (time, frequency and eating patterns) (Benhaïm et al ., 2017; Fortes‐Silva et al ., 2018; Kitagawa et al ., 2015; Mattos et al ., 2016; Reis et al ., 2019). A great benefit of a self‐feeding system is its accurate supply of feed according to the demand of the animals, thus reducing food losses and decreasing impacts on water quality (Mukai et al ., 2021; Stewart et al ., 2012). This feeding system has been developed to prevent underfeeding and overfeeding, with animals ingesting food according to their nutritional needs (Covès et al ., 2006).…”

Section: Introductionmentioning

confidence: 99%

“…This feeding system has been developed to prevent underfeeding and overfeeding, with animals ingesting food according to their nutritional needs (Covès et al ., 2006). In this system, fish inevitably need to learn the relationship between activation and food availability, and this learning period varies among species (Mukai et al ., 2021).…”

Section: Introductionmentioning

confidence: 99%

Feeding behaviour, locomotion rhythms and blood biochemistry of the neotropical red‐tail catfish (Phractocephalus hemioliopterus)

Guilherme

Ribeiro

Prado

et al. 2023

Journal of Fish Biology

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The study evaluated the feeding behaviour of Phractocephalus hemioliopterus through the animals' ability to adapt to the self-feeding system, their preferred feeding times and locomotor activity, as well as the blood biochemistry of juveniles fed in a light/ dark cycle. The study was carried out through two experiments, the first of which contained two phases. In experiment 1phase I, 24 juveniles (35.28 ± 0.62 g) were distributed in eight 48 l tanks. The tanks were equipped with a self-feeding system

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CFFI-Vit: Enhanced Vision Transformer for the Accurate Classification of Fish Feeding Intensity in Aquaculture

Liu,

Becerra,

Bienvenido-Barcena

et al. 2024

JMSE

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The real-time classification of fish feeding behavior plays a crucial role in aquaculture, which is closely related to feeding cost and environmental preservation. In this paper, a Fish Feeding Intensity classification model based on the improved Vision Transformer (CFFI-Vit) is proposed, which is capable of quantifying the feeding behaviors of rainbow trout (Oncorhynchus mykiss) into three intensities: strong, moderate, and weak. The process is outlined as follows: firstly, we obtained 2685 raw feeding images of rainbow trout from recorded videos and classified them into three categories: strong, moderate, and weak. Secondly, the number of transformer encoder blocks in the internal structure of the ViT was reduced from 12 to 4, which can greatly reduce the computational load of the model, facilitating its deployment on mobile devices. And finally, a residual module was added to the head of the ViT, enhancing the model’s ability to extract features. The proposed CFFI-Vit has a computational load of 5.81 G (Giga) Floating Point Operations per Second (FLOPs). Compared to the original ViT model, it reduces computational demands by 65.54% and improves classification accuracy on the validation set by 5.4 percentage points. On the test set, the model achieves precision, recall, and F1 score of 93.47%, 93.44%, and 93.42%, respectively. Additionally, compared to state-of-the-art models such as ResNet34, MobileNetv2, VGG16, and GoogLeNet, the CFFI-Vit model’s classification accuracy is higher by 6.87, 8.43, 7.03, and 5.65 percentage points, respectively. Therefore, the proposed CFFI-Vit can achieve higher classification accuracy while significantly reducing computational demands. This provides a foundation for deploying lightweight deep network models on edge devices with limited hardware capabilities.

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High growth rates of Asian seabass ( Lates calcarifer Bloch, 1790) fry reared using a demand feeder with an image processing system for detecting fish behaviour

Cited by 3 publications

References 24 publications

Nonintrusive and automatic quantitative analysis methods for fish behaviour in aquaculture

Nonintrusive and automatic quantitative analysis methods for fish behaviour in aquaculture

Feeding behaviour, locomotion rhythms and blood biochemistry of the neotropical red‐tail catfish (Phractocephalus hemioliopterus)

CFFI-Vit: Enhanced Vision Transformer for the Accurate Classification of Fish Feeding Intensity in Aquaculture

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