Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system
The penaeid shrimp farming industry is experiencing rapid growth. To reduce costs and labour, automation techniques such as counting and size estimation are increasingly being adopted. Feeding based on the degree of stomach fullness can significantly reduce food waste and water contamination. Theref...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2268878 |
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| author | Yu-Kai Lee Bo-Yi Lin Tien-Hsiung Weng Chien-Kang Huang Chen Liu Chih-Chin Liu Shih-Shun Lin Han-Ching Wang |
| author_facet | Yu-Kai Lee Bo-Yi Lin Tien-Hsiung Weng Chien-Kang Huang Chen Liu Chih-Chin Liu Shih-Shun Lin Han-Ching Wang |
| author_sort | Yu-Kai Lee |
| collection | DOAJ |
| description | The penaeid shrimp farming industry is experiencing rapid growth. To reduce costs and labour, automation techniques such as counting and size estimation are increasingly being adopted. Feeding based on the degree of stomach fullness can significantly reduce food waste and water contamination. Therefore, we propose an intelligent shrimp farming system that includes shrimp detection, measurement of approximated shrimp length, shrimp quantity, and two methods for determining the degree of digestive tract fullness. We introduce AR-YOLOv5 (Angular Rotation YOLOv5) in the system to enhance both shrimp growth and the environmental sustainability of shrimp farming. Our experiments were conducted in a real shrimp farming environment. The length and quantity are estimated based on the bounding box, and the level of stomach fullness is approximated using the ratio of the shrimp´s digestive tract to its body size. In terms of detection performance, our proposed method achieves a precision rate of 97.70%, a recall rate of 91.42%, a mean average precision of 94.46%, and an F1-score of 95.42% using AR-YOLOv5. Furthermore, our stomach fullness determined method achieves an accuracy of 88.8%, a precision rate of 91.7%, a recall rate of 90.9%, and an F1-score of 91.3% in real shrimp farming environments. |
| format | Article |
| id | doaj-art-bb3e26fdb6f5435fbbad592ed1622c95 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-bb3e26fdb6f5435fbbad592ed1622c952025-08-20T02:00:24ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.22688782268878Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming systemYu-Kai Lee0Bo-Yi Lin1Tien-Hsiung Weng2Chien-Kang Huang3Chen Liu4Chih-Chin Liu5Shih-Shun Lin6Han-Ching Wang7Providence UniversityProvidence UniversityProvidence UniversityNational Taiwan UniversityClarkson UniversityProvidence UniversityNational Taiwan UniversityNational Cheng Kung UniversityThe penaeid shrimp farming industry is experiencing rapid growth. To reduce costs and labour, automation techniques such as counting and size estimation are increasingly being adopted. Feeding based on the degree of stomach fullness can significantly reduce food waste and water contamination. Therefore, we propose an intelligent shrimp farming system that includes shrimp detection, measurement of approximated shrimp length, shrimp quantity, and two methods for determining the degree of digestive tract fullness. We introduce AR-YOLOv5 (Angular Rotation YOLOv5) in the system to enhance both shrimp growth and the environmental sustainability of shrimp farming. Our experiments were conducted in a real shrimp farming environment. The length and quantity are estimated based on the bounding box, and the level of stomach fullness is approximated using the ratio of the shrimp´s digestive tract to its body size. In terms of detection performance, our proposed method achieves a precision rate of 97.70%, a recall rate of 91.42%, a mean average precision of 94.46%, and an F1-score of 95.42% using AR-YOLOv5. Furthermore, our stomach fullness determined method achieves an accuracy of 88.8%, a precision rate of 91.7%, a recall rate of 90.9%, and an F1-score of 91.3% in real shrimp farming environments.http://dx.doi.org/10.1080/09540091.2023.2268878aquaculturecomputer visiondeep learningintelligent systemsize estimationstomach fullness levels |
| spellingShingle | Yu-Kai Lee Bo-Yi Lin Tien-Hsiung Weng Chien-Kang Huang Chen Liu Chih-Chin Liu Shih-Shun Lin Han-Ching Wang Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system Connection Science aquaculture computer vision deep learning intelligent system size estimation stomach fullness levels |
| title | Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system |
| title_full | Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system |
| title_fullStr | Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system |
| title_full_unstemmed | Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system |
| title_short | Counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system |
| title_sort | counting and measuring the size and stomach fullness levels for an intelligent shrimp farming system |
| topic | aquaculture computer vision deep learning intelligent system size estimation stomach fullness levels |
| url | http://dx.doi.org/10.1080/09540091.2023.2268878 |
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