Using a convolutional neural network for fingerling counting: A multi-task learning approach

Gonçalves, Diogo Nunes; Acosta, Plabiany Rodrigo; Ramos, Altina; Osco, Lucas Prado; Furuya, Danielle Elis Garcia; Furuya, Michelle Taís Garcia; Li, Jonathan; Marcato, José; Pistori, Hemerson; Gonçalves, Wesley Nunes

doi:10.1016/j.aquaculture.2022.738334

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Fry counting is the counting of the target number of fry in a given area to aid production decisions [1][2][3]. In aquaculture breeding or production programs, counting the number of fry has a considerable cost in terms of the manpower required [4].…”

Section: Introductionmentioning

confidence: 99%

Fry Counting Method in High-Density Culture Based on Image Enhancement Algorithm and Attention Mechanism

Chen,

Cheng,

Dou

et al. 2024

IEEE Access

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It is important in production to achieve accurate counting and density estimation of highdensity culture fry under the environmental conditions of aquaculture scenarios in an efficient and accurate manner. However, none of the current methods for fry counting works well under the high-density and highoverlap conditions of real aquaculture scenarios. Therefore, in this paper, we propose a high-density farming fry monitoring network model, Super-Resolution GAN Density Estimate Attention Network (SGDAN), which incorporating an image enhancement algorithm and an attention mechanism, and we create a highdensity farming fry dataset (HD-FryDataset) based on the environmental conditions of real aquaculture scenarios. The network model is designed to improve and optimize the targeted subnetworks for several key aspects of high-density fish fry monitoring work. Four subnetworks are included for image optimization, feature extraction, attention, and density map estimation. The experimental results show that the SGDAN network model achieved an average counting accuracy of 97.57% on the high-density culture fry dataset, which was 8.23% and 2.06% higher than those of MCNN and CSRNet, respectively. Additionally, the MAE and RMSE of the model were reduced by 71.9% and 67.3% and by 34.3% and 33.2% compared with those of MCNN and CSRNet, respectively. The model proposed in this paper also has a better ability to generate predictive density maps. The density maps generated by SGDAN have values of the evaluation metrics PNSR and SSIM of 20.33 and 0.933, respectively, which are 3.31 and 0.037 and 2.63 and 0.031 higher than those of MCNN and CSRNet. In general, the network model proposed in this paper outperforms existing network models in two applications: accurate counting of fry and generation of density maps for high-density culture in aquaculture. It also provides a good solution for digitizing the number of fry and visualizing the density of high-density culture in intelligent aquaculture systems.

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Section: Introductionmentioning

confidence: 99%

Fry Counting Method in High-Density Culture Based on Image Enhancement Algorithm and Attention Mechanism

Chen,

Cheng,

Dou

et al. 2024

IEEE Access

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“…Unlike other domains, most current AI technologies related to fish farming primarily focus on monitoring tasks. Even the most recent studies, including those conducted in 2022, utilize AI for tasks such as estimating fish populations or assessing water quality to determine feeding schedules [3,4].…”

Section: Introductionmentioning

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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“…Fry counting refers to the counting of the number of targets in a specific area to aid in production decisions [1], [2], [3]. Its accuracy is very important for scientific decisionmaking, including the scientific feeding, behavioral analysis, transportation, and marketing of fish, along with an assessment of fry survival and culture density control [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Fry Counting Models Based on Attention Mechanism and YOLOv4-Tiny

et al. 2022

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Accurate counting is difficult in the case of large numbers of overlapping and adhering fry. In this study, we propose a lightweight target detection counting method based on deep learning methods that can meet the deployment requirements of edge computing device for automatic fry counting while obtaining a high counting accuracy. We improve the structure of YOLOv4-tiny by embedding different attention mechanisms in the CSP blocks of the backbone network to enhance the feature extraction performance. In addition, the low efficiency of feature fusion in the original model is also addressed by adding different attention mechanisms to the neck network structure to promote the effective fusion of deep feature information with shallow feature information and improve the counting accuracy. The experimental results showed that the six models proposed in this study improved the model accuracy and recall to varying degrees compared with the original YOLOv4-tiny model, while retaining the advantages of the YOLOv4-tiny model in terms of its small number of parameters and fast inference rate. It was also shown that the CBAM(n)-YOLOv4-tiny model obtained by adding the CBAM to the neck network showed the most significant improvement, with an mean average precision (mAP) of 94.45% and a recall of 93.93%.

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Using a convolutional neural network for fingerling counting: A multi-task learning approach

Cited by 6 publications

References 15 publications

Fry Counting Method in High-Density Culture Based on Image Enhancement Algorithm and Attention Mechanism

Fry Counting Method in High-Density Culture Based on Image Enhancement Algorithm and Attention Mechanism

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

Fry Counting Models Based on Attention Mechanism and YOLOv4-Tiny

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