Fast segment anything model applied in a low-cost camera-based system for oyster growth monitoring
The increase in the global population has been accompanied by an increase in demand for animal protein, consequently driving the growth of the aquaculture sector as a sustainable solution to satisfy this demand. Observing the growth of animals in farm environments is vital to assess the efficiency o...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2024-12-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0233879 |
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| author | Vinícius Batista Vitorino Biazi Andreia C. M. Rodrigues Amadeu M. V. M. Soares Carlos Marques |
| author_facet | Vinícius Batista Vitorino Biazi Andreia C. M. Rodrigues Amadeu M. V. M. Soares Carlos Marques |
| author_sort | Vinícius Batista |
| collection | DOAJ |
| description | The increase in the global population has been accompanied by an increase in demand for animal protein, consequently driving the growth of the aquaculture sector as a sustainable solution to satisfy this demand. Observing the growth of animals in farm environments is vital to assess the efficiency of feeding methods, identify health problems or environmental stressors, and help implement sustainable practices. The application of cameras enables remote monitoring of various environments and activities in real time, providing quick insights, improving security measures, and facilitating decision-making based on the collected data. This paper proposes a low-cost camera system and an image processing-based strategy to monitor and measure the size of Pacific oysters inside a tank. The fast segment anything model algorithm is implemented in the Python programming language to perform image segmentation and generate object masks for subsequent processing. The size of oysters is obtained by measuring the distance between the parallel lines of the bounding box that is generated surrounding them. The largest absolute errors and relative errors for the estimates were 3 mm and 5.13%, respectively, when compared to measurements made manually. Furthermore, the width and height have standard deviations of 0.6 and 0.3 mm, respectively, indicating low data dispersion and good repeatability of the algorithm. Since the accuracy of the human eye in estimating a dimension without the aid of a distance measuring instrument tends to be lower than that obtained by the proposed tool, it is believed that the approach can significantly contribute to the management of oyster farm production, providing strategic insights based on animal growth for producers. |
| format | Article |
| id | doaj-art-5fcf76cf1fd84a239bcf4f8307896254 |
| institution | DOAJ |
| issn | 2158-3226 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-5fcf76cf1fd84a239bcf4f83078962542025-08-20T02:56:31ZengAIP Publishing LLCAIP Advances2158-32262024-12-011412125122125122-610.1063/5.0233879Fast segment anything model applied in a low-cost camera-based system for oyster growth monitoringVinícius Batista0Vitorino Biazi1Andreia C. M. Rodrigues2Amadeu M. V. M. Soares3Carlos Marques4CICECO and Physics Department, University of Aveiro, 3810-193 Aveiro, PortugalCICECO and Physics Department, University of Aveiro, 3810-193 Aveiro, PortugalCESAM—Centre for Environmental and Marine Studies and Department of Biology, University of Aveiro, 3810-193 Aveiro, PortugalCESAM—Centre for Environmental and Marine Studies and Department of Biology, University of Aveiro, 3810-193 Aveiro, PortugalCICECO and Physics Department, University of Aveiro, 3810-193 Aveiro, PortugalThe increase in the global population has been accompanied by an increase in demand for animal protein, consequently driving the growth of the aquaculture sector as a sustainable solution to satisfy this demand. Observing the growth of animals in farm environments is vital to assess the efficiency of feeding methods, identify health problems or environmental stressors, and help implement sustainable practices. The application of cameras enables remote monitoring of various environments and activities in real time, providing quick insights, improving security measures, and facilitating decision-making based on the collected data. This paper proposes a low-cost camera system and an image processing-based strategy to monitor and measure the size of Pacific oysters inside a tank. The fast segment anything model algorithm is implemented in the Python programming language to perform image segmentation and generate object masks for subsequent processing. The size of oysters is obtained by measuring the distance between the parallel lines of the bounding box that is generated surrounding them. The largest absolute errors and relative errors for the estimates were 3 mm and 5.13%, respectively, when compared to measurements made manually. Furthermore, the width and height have standard deviations of 0.6 and 0.3 mm, respectively, indicating low data dispersion and good repeatability of the algorithm. Since the accuracy of the human eye in estimating a dimension without the aid of a distance measuring instrument tends to be lower than that obtained by the proposed tool, it is believed that the approach can significantly contribute to the management of oyster farm production, providing strategic insights based on animal growth for producers.http://dx.doi.org/10.1063/5.0233879 |
| spellingShingle | Vinícius Batista Vitorino Biazi Andreia C. M. Rodrigues Amadeu M. V. M. Soares Carlos Marques Fast segment anything model applied in a low-cost camera-based system for oyster growth monitoring AIP Advances |
| title | Fast segment anything model applied in a low-cost camera-based system for oyster growth monitoring |
| title_full | Fast segment anything model applied in a low-cost camera-based system for oyster growth monitoring |
| title_fullStr | Fast segment anything model applied in a low-cost camera-based system for oyster growth monitoring |
| title_full_unstemmed | Fast segment anything model applied in a low-cost camera-based system for oyster growth monitoring |
| title_short | Fast segment anything model applied in a low-cost camera-based system for oyster growth monitoring |
| title_sort | fast segment anything model applied in a low cost camera based system for oyster growth monitoring |
| url | http://dx.doi.org/10.1063/5.0233879 |
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