Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂)
In aquaculture, the assessment of fish feeding intensity is an essential indicator of fish appetite, which is significant for guiding feeding and optimizing fish production. In the factory-based recirculating water high-density aquaculture environment, images have disadvantages such as uneven contra...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Aquaculture Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352513425000274 |
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| author | Haijing Qin Yunchen Tian Jianing Quan Xueqi Cong Qingfei Li Jinzhu Sui |
| author_facet | Haijing Qin Yunchen Tian Jianing Quan Xueqi Cong Qingfei Li Jinzhu Sui |
| author_sort | Haijing Qin |
| collection | DOAJ |
| description | In aquaculture, the assessment of fish feeding intensity is an essential indicator of fish appetite, which is significant for guiding feeding and optimizing fish production. In the factory-based recirculating water high-density aquaculture environment, images have disadvantages such as uneven contrast and blurring, and there are difficulties in manually extracting image features. Although the deep learning-based fish feeding intensity assessment model has higher recognition accuracy and better robustness, the conventional differentiation of feeding intensity usually relies on manual experience to divide the feeding intensity dataset, which is subjective and uncertain, and the annotation is observed by the aquaculture experienced personnel to increase the labor and time cost. In order to solve these problems, this study constructs a benchmark dataset of the feeding intensity of pearl gentian groupers in a factory circulating water environment, which is divided into feeding fish groups and fish aggregation areas by training Unet semantic segmentation network and compares standard clustering algorithms through clustering evaluation indexes to maximally select the optimal clustering method and the number of clusters that are suitable for this paper's dataset. In addition, training on this benchmark dataset proposes an improved feeding intensity evaluation network, which achieves a good balance in prediction accuracy and parameter memory and offers the possibility of subsequent deployment of the model on mobile. Dataset address: https://github.com/Qhj-123/fig-dataset |
| format | Article |
| id | doaj-art-1647fa1c3e8c4b489bd21e2c7b31fbe5 |
| institution | Kabale University |
| issn | 2352-5134 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Aquaculture Reports |
| spelling | doaj-art-1647fa1c3e8c4b489bd21e2c7b31fbe52025-02-06T05:12:24ZengElsevierAquaculture Reports2352-51342025-03-0140102641Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂)Haijing Qin0Yunchen Tian1Jianing Quan2Xueqi Cong3Qingfei Li4Jinzhu Sui5College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300384, ChinaCollege of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300384, China; Key Laboratory of Smart Breeding (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Tianjin Agricultural University, Tianjin 300384, China; Tianjin Key Laboratory of Aquatic Ecology and Aquaculture, Tianjin 300384, China; Corresponding authors at: College of Computer and Information Engineering Tianjin Agricultural University, Tianjin 300384, China.College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300384, China; Key Laboratory of Smart Breeding (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Tianjin Agricultural University, Tianjin 300384, China; Corresponding authors at: College of Computer and Information Engineering Tianjin Agricultural University, Tianjin 300384, China.College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300384, ChinaCollege of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300384, ChinaCollege of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300384, ChinaIn aquaculture, the assessment of fish feeding intensity is an essential indicator of fish appetite, which is significant for guiding feeding and optimizing fish production. In the factory-based recirculating water high-density aquaculture environment, images have disadvantages such as uneven contrast and blurring, and there are difficulties in manually extracting image features. Although the deep learning-based fish feeding intensity assessment model has higher recognition accuracy and better robustness, the conventional differentiation of feeding intensity usually relies on manual experience to divide the feeding intensity dataset, which is subjective and uncertain, and the annotation is observed by the aquaculture experienced personnel to increase the labor and time cost. In order to solve these problems, this study constructs a benchmark dataset of the feeding intensity of pearl gentian groupers in a factory circulating water environment, which is divided into feeding fish groups and fish aggregation areas by training Unet semantic segmentation network and compares standard clustering algorithms through clustering evaluation indexes to maximally select the optimal clustering method and the number of clusters that are suitable for this paper's dataset. In addition, training on this benchmark dataset proposes an improved feeding intensity evaluation network, which achieves a good balance in prediction accuracy and parameter memory and offers the possibility of subsequent deployment of the model on mobile. Dataset address: https://github.com/Qhj-123/fig-datasethttp://www.sciencedirect.com/science/article/pii/S2352513425000274Assessment of feeding intensityBenchmark datasetReal culture environmentUnetKmeans |
| spellingShingle | Haijing Qin Yunchen Tian Jianing Quan Xueqi Cong Qingfei Li Jinzhu Sui Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) Aquaculture Reports Assessment of feeding intensity Benchmark dataset Real culture environment Unet Kmeans |
| title | Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) |
| title_full | Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) |
| title_fullStr | Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) |
| title_full_unstemmed | Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) |
| title_short | Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) |
| title_sort | benchmark dataset on feeding intensity of the pearl gentian grouper epinephelus fuscoguttatus♀ e lanceolatus♂ |
| topic | Assessment of feeding intensity Benchmark dataset Real culture environment Unet Kmeans |
| url | http://www.sciencedirect.com/science/article/pii/S2352513425000274 |
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