Intelligent fish feeding based on machine vision: A review

Zhang, Lu; Li, Bin; Sun, Xiaobing; Hong, Qingyang; Duan, Qingling

doi:10.1016/j.biosystemseng.2023.05.010

Cited by 16 publications

(3 citation statements)

References 104 publications

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“…The application of machine vision technology in aquaculture is an important recent research topic. In particular, the application of machine vision in the field of feed distribution contributes to the advancement of increasing production efficiency, reducing feed costs, and automating feed distribution [33]. Predicting food rations using traditional mapping methods is a very difficult problem.…”

Section: Related Workmentioning

confidence: 99%

An Automated Fish-Feeding System Based on CNN and GRU Neural Networks

Son,

Jeong

2024

Sustainability

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AI plays a pivotal role in predicting plant growth in agricultural contexts and in creating optimized environments for cultivation. However, unlike agriculture, the application of AI in aquaculture is predominantly focused on diagnosing animal conditions and monitoring them for users. This paper introduces an Automated Fish-feeding System (AFS) based on Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs), aiming to establish an automated system akin to smart farming in the aquaculture sector. The AFS operates by precisely calculating feed rations through two main modules. The Fish Growth Measurement Module (FGMM) utilizes fish data to assess the current growth status of the fish and transmits this information to the Feed Ration Prediction Module (FRPM). The FRPM integrates sensor data from the fish farm, fish growth data, and current feed ration status as time-series data, calculating the increase or decrease rate of ration based on the present fish conditions. This paper automates feed distribution within fish farms through these two modules and verifies the efficiency of automated feed distribution. Simulation results indicate that the FGMM neural network model effectively identifies fish body length with a minor deviation of less than 0.1%, while the FRPM neural network model demonstrates proficiency in predicting ration using a GRU cell with a structured layout of 64 × 48.

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Section: Related Workmentioning

confidence: 99%

An Automated Fish-Feeding System Based on CNN and GRU Neural Networks

Son,

Jeong

2024

Sustainability

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“…Computer vision technology has advanced rapidly in recent years, offering a nondestructive and rapid method for detecting fish behavior [3]. The use of computer vision for fish behavior analysis has become a prominent research area.…”

Section: Introductionmentioning

confidence: 99%

DDEYOLOv9: Network for Detecting and Counting Abnormal Fish Behaviors in Complex Water Environments

Li,

Hu,

Zhang

et al. 2024

Fishes

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Accurately detecting and counting abnormal fish behaviors in aquaculture is essential. Timely detection allows farmers to take swift action to protect fish health and prevent economic losses. This paper proposes an enhanced high-precision detection algorithm based on YOLOv9, named DDEYOLOv9, to facilitate the detection and counting of abnormal fish behavior in industrial aquaculture environments. To address the lack of publicly available datasets on abnormal behavior in fish, we created the “Abnormal Behavior Dataset of Takifugu rubripes”, which includes five categories of fish behaviors. The detection algorithm was further enhanced in several key aspects. Firstly, the DRNELAN4 feature extraction module was introduced to replace the original RepNCSPELAN4 module. This change improves the model’s detection accuracy for high-density and occluded fish in complex water environments while reducing the computational cost. Secondly, the proposed DCNv4-Dyhead detection head enhances the model’s multi-scale feature learning capability, effectively recognizes various abnormal fish behaviors, and improves the computational speed. Lastly, to address the issue of sample imbalance in the abnormal fish behavior dataset, we propose EMA-SlideLoss, which enhances the model’s focus on hard samples, thereby improving the model’s robustness. The experimental results demonstrate that the DDEYOLOv9 model achieves high Precision, Recall, and mean Average Precision (mAP) on the “Abnormal Behavior Dataset of Takifugu rubripes”, with values of 91.7%, 90.4%, and 94.1%, respectively. Compared to the YOLOv9 model, these metrics are improved by 5.4%, 5.5%, and 5.4%, respectively. The model also achieves a running speed of 119 frames per second (FPS), which is 45 FPS faster than YOLOv9. Experimental results show that the DDEYOLOv9 algorithm can accurately and efficiently identify and quantify abnormal fish behaviors in specific complex environments.

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“…Due to the inability to automatically adapt to the needs of fish, it may cause problems of insufficient or excessive feed supply. Both insufficient and excessive feed supply can have negative impacts on fish growth, increase breeding costs, and cause pollution to the breeding water area (Zhang et al, 2023). Therefore, feeding not only affects the growth and health of fish, but also has a profound impact on the economic efficiency of aquaculture.…”

Section: Introductionmentioning

confidence: 99%

A real-time feeding decision method based on density estimation of farmed fish

Zhao,

Wu,

Liu

et al. 2024

Front. Mar. Sci.

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With the global population growth and increasing demand for high-quality protein, aquaculture has experienced rapid development. Fish culture management and feed supply are crucial components of aquaculture. Traditional baiting management relies on experiential judgment and regular observation, which often leads to inefficient baiting practices and wastage. To address these issues, intelligent bait casting decisions have emerged. Leveraging advanced artificial intelligence algorithms, intelligent bait casting decisions can overcome most drawbacks of traditional bait management and enhance breeding efficiency. However, most of the current intelligent baiting decisions are focused on using methods such as image processing and target detection to identify different feeding actions and patterns. These methods do not discuss based on video streams and do not consider the changes in fish behavior during the baiting process. Therefore, we proposed a real-time analysis method based on the density distribution of fish feeding behavior (FishFeed). Firstly, this method upgrades the input mechanism, not only handling static images but also capable of real-time video stream analysis. Secondly, by evaluating the fish school density distribution through a new intelligent baiting strategy, this method can monitor the feeding behavior of fish school during the baiting process in real time. Finally, we constructed a dataset for fish school density analysis (DlouFishDensity) that includes a wealth of video and image frames, providing a valuable resource for research. Experimental results indicate that our algorithm outperforms MCNN, improving MAE by 1.63 and 1.35, MSE by 1.92 and 1.58, and reducing prediction time by 2.56 seconds on the same dataset. By implementing real-time analysis of fish feeding behavior density distribution, our method offers a more efficient and effective approach to baiting management in aquaculture, contributing to improved breeding efficiency and resource utilization.

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Intelligent fish feeding based on machine vision: A review

Cited by 16 publications

References 104 publications

An Automated Fish-Feeding System Based on CNN and GRU Neural Networks

An Automated Fish-Feeding System Based on CNN and GRU Neural Networks

DDEYOLOv9: Network for Detecting and Counting Abnormal Fish Behaviors in Complex Water Environments

A real-time feeding decision method based on density estimation of farmed fish

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