Image-Based Shrimp Aquaculture Monitoring
Shrimp farming is a growing industry, and automating certain processes within aquaculture tanks is becoming increasingly important to improve efficiency. This paper proposes an image-based system designed to address four key tasks in an aquaculture tank with <i>Penaeus vannamei</i>: esti...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/248 |
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| author | Beatriz Correia Osvaldo Pacheco Rui J. M. Rocha Paulo L. Correia |
| author_facet | Beatriz Correia Osvaldo Pacheco Rui J. M. Rocha Paulo L. Correia |
| author_sort | Beatriz Correia |
| collection | DOAJ |
| description | Shrimp farming is a growing industry, and automating certain processes within aquaculture tanks is becoming increasingly important to improve efficiency. This paper proposes an image-based system designed to address four key tasks in an aquaculture tank with <i>Penaeus vannamei</i>: estimating shrimp length and weight, counting shrimps, and evaluating feed pellet food attractiveness. A setup was designed, including a camera connected to a Raspberry Pi computer, to capture high-quality images around a feeding plate during feeding moments. A dataset composed of 1140 images was captured over multiple days and different times of the day, under varying lightning conditions. This dataset has been used to train a segmentation model, which was employed to detect and filter shrimps in optimal positions for dimensions estimation. Promising results were achieved. For length estimation, the proposed method achieved a mean absolute percentage error (MAPE) of 1.56%, and width estimation resulted in a MAPE of 0.15%. These dimensions were then used to estimate the shrimp’s weight. Shrimp counting also yielded results with an average MAPE of 7.17%, ensuring a satisfactory estimation of the population in the field of view of the image sensor. The paper also proposes two approaches to evaluate pellet attractiveness, relying on a qualitative analysis due to the challenges of defining suitable quantitative metrics. The results were influenced by environmental conditions, highlighting the need for further investigation. The image capture and analysis prototype proposed in this paper provides a foundation for an adaptable system that can be scaled across multiple tanks, enabling efficient, automated monitoring. Additionally, it could also be adapted to monitor other species raised in similar aquaculture environments. |
| format | Article |
| id | doaj-art-f43f07328ad2461baf42efa7bb942ded |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-f43f07328ad2461baf42efa7bb942ded2025-01-10T13:21:21ZengMDPI AGSensors1424-82202025-01-0125124810.3390/s25010248Image-Based Shrimp Aquaculture MonitoringBeatriz Correia0Osvaldo Pacheco1Rui J. M. Rocha2Paulo L. Correia3Instituto de Telecomunicações (IT), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, PortugalInstituto de Engenharia Eletrónica e Informática (IEETA), Departamento de Eletrónica, Telecomunicações e Informática, Universidade de Aveiro, 3810-193 Aveiro, PortugalRiaSearch Lda., 3870-168 Murtosa, PortugalInstituto de Telecomunicações (IT), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, PortugalShrimp farming is a growing industry, and automating certain processes within aquaculture tanks is becoming increasingly important to improve efficiency. This paper proposes an image-based system designed to address four key tasks in an aquaculture tank with <i>Penaeus vannamei</i>: estimating shrimp length and weight, counting shrimps, and evaluating feed pellet food attractiveness. A setup was designed, including a camera connected to a Raspberry Pi computer, to capture high-quality images around a feeding plate during feeding moments. A dataset composed of 1140 images was captured over multiple days and different times of the day, under varying lightning conditions. This dataset has been used to train a segmentation model, which was employed to detect and filter shrimps in optimal positions for dimensions estimation. Promising results were achieved. For length estimation, the proposed method achieved a mean absolute percentage error (MAPE) of 1.56%, and width estimation resulted in a MAPE of 0.15%. These dimensions were then used to estimate the shrimp’s weight. Shrimp counting also yielded results with an average MAPE of 7.17%, ensuring a satisfactory estimation of the population in the field of view of the image sensor. The paper also proposes two approaches to evaluate pellet attractiveness, relying on a qualitative analysis due to the challenges of defining suitable quantitative metrics. The results were influenced by environmental conditions, highlighting the need for further investigation. The image capture and analysis prototype proposed in this paper provides a foundation for an adaptable system that can be scaled across multiple tanks, enabling efficient, automated monitoring. Additionally, it could also be adapted to monitor other species raised in similar aquaculture environments.https://www.mdpi.com/1424-8220/25/1/248image-based shrimp monitoring systemaquaculture systemobject detection and segmentationRaspberry Pishrimp length estimationshrimp width estimation |
| spellingShingle | Beatriz Correia Osvaldo Pacheco Rui J. M. Rocha Paulo L. Correia Image-Based Shrimp Aquaculture Monitoring Sensors image-based shrimp monitoring system aquaculture system object detection and segmentation Raspberry Pi shrimp length estimation shrimp width estimation |
| title | Image-Based Shrimp Aquaculture Monitoring |
| title_full | Image-Based Shrimp Aquaculture Monitoring |
| title_fullStr | Image-Based Shrimp Aquaculture Monitoring |
| title_full_unstemmed | Image-Based Shrimp Aquaculture Monitoring |
| title_short | Image-Based Shrimp Aquaculture Monitoring |
| title_sort | image based shrimp aquaculture monitoring |
| topic | image-based shrimp monitoring system aquaculture system object detection and segmentation Raspberry Pi shrimp length estimation shrimp width estimation |
| url | https://www.mdpi.com/1424-8220/25/1/248 |
| work_keys_str_mv | AT beatrizcorreia imagebasedshrimpaquaculturemonitoring AT osvaldopacheco imagebasedshrimpaquaculturemonitoring AT ruijmrocha imagebasedshrimpaquaculturemonitoring AT paulolcorreia imagebasedshrimpaquaculturemonitoring |