Computer vision in aquaculture: a case study of juvenile fish counting

Babu, Krishna Moorthy; Bentall, Daniel; Ashton, David T.; Puklowski, Morgan; Fantham, Warren; Lin, Harris T.; Tuckey, Nicholas P.L.; Wellenreuther, Maren; Jesson, Linley K.

doi:10.1080/03036758.2022.2101484

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…Obstructions and occlusions of ngerlings, low sharpness from digital images with higher sh density, and variation in the number of fry in the dataset were examples of such limitations. All these factors are challenging in network learning and can interfere with accuracy and other metrics (Kuswantori et al, 2023;Barreiros et al, 2021;Babu et al, 2023). The evaluated model partially overcame all these obstacles.…”

Section: Discussionmentioning

confidence: 99%

Identification and counting of pirapitinga Piaractus brachypomus fingerlings fish using Machine learning with batch normalization

Souza,

Costa,

França

et al. 2024

Preprint

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The identification and counting of fish are relevant tools used for stocking, harvesting, and marketing management of farmed fish. The use of convolutional networks has been used for such purposes, and different approaches have been employed to improve network learning. Batch normalization is one of the techniques that aids in the enhancement and stability of the network, providing greater accuracy. Thus, the objective was to evaluate machine learning in the identification and counting of pirapitinga Piaractus brachypomus fingerlings fish with and without batch normalization. One thousand photographic images of pirapitinga fingerlings were used, labeled through bounding boxes. The training of the adapted convolutional network model was performed, with batch normalization layers inserted at the end of each convolution block. One hundred fifty epochs were established, and batch sizes for normalization were set to 1, 5, 10, and 20. The database training was also conducted without applying normalization for comparison. The evaluation metrics for network performance were precision, recall, and mAP@0.5. The results obtained with the model without the application of the technique were inferior to the models in which batch normalization was applied. The batch size equal to 20 was the model trained with the best performance, showing precision of 96.74%, recall of 95.48%, mAP@0.5 of 97.08%, and accuracy of 98%. It is concluded that batch normalization increases accuracy in the detection and counting of pirapitinga fingerlings in different densities of fish

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Section: Discussionmentioning

confidence: 99%

Identification and counting of pirapitinga Piaractus brachypomus fingerlings fish using Machine learning with batch normalization

Souza,

Costa,

França

et al. 2024

Preprint

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“…Improvements including the implementation of IoT networks to enable the transition from paper-based systems of record keeping to digital systems, including the use of iPads and the operation of software for data input, analysis, and decision making for tasks including but not limited to monitoring, evaluating, and maintaining water quality, and WHS compliance can improve the efficiency and effectiveness of workforce and farm productivity. Additional digital technologies that will assist with the growth and success of the industry include but are not limited to the use of sensors combined with machine learning and augmented reality approaches to inform better decisions with regard to maintaining optimum pond conditions or digital optical counters that remove the human error that persists with manual approaches to counting larvae and animals and assists with efforts to reduce the mortality and stress of animals [34][35][36]. The successful transformation of prawn farms towards a digital future requires the workforce to embrace change in their roles and task performance, with the introduction of new technologies to operate.…”

Section: The Queensland Prawn Farming Contextmentioning

confidence: 99%

Skill Development in Current and Future Workers to Thrive in the Digital Aquaculture Industry

McDonald,

Lovric,

Cosby

2024

Aquaculture Journal

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The digitisation of the agriculture industry provides an opportune context for accelerating sustainable food production. Aquaculture is among the fastest-growing agriculture sectors and is well placed to help address food supply shortages, directly contributing to the achievement of UN Sustainable Development Goal 2. However, the sector currently has inadequate digital capability and enabling conditions to thrive. Social cognitive career theory asserts that career choices and persistence are directly influenced by a person’s thoughts, including their self-efficacy; therefore, the upskilling and reskilling of labour is required to build confidence in their digital capabilities and reduce turnover intentions. Consequently, this study sought to identify the key skills and needs for this workforce to transition to digitally driven ways of working. The results indicated that a range of skills and abilities that enable people to improve their digital capabilities were required. The findings are presented and discussed.

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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]. At the same time, the monitoring of high-density culture fry is important for the whole aquaculture industry [5].…”

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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Computer vision in aquaculture: a case study of juvenile fish counting

Cited by 8 publications

References 32 publications

Identification and counting of pirapitinga Piaractus brachypomus fingerlings fish using Machine learning with batch normalization

Identification and counting of pirapitinga Piaractus brachypomus fingerlings fish using Machine learning with batch normalization

Skill Development in Current and Future Workers to Thrive in the Digital Aquaculture Industry

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

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