Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish
Ammonia can be produced by the respiration and excretion of fish during the farming process, which can affect the life of fish. In this paper, to research the behavior of fish under different ammonia concentration and make the corresponding judgment and early warning for the abnormal behavior of fis...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/16/4425 |
| _version_ | 1797559488881885184 |
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| author | Wenkai Xu Zhaohu Zhu Fengli Ge Zhongzhi Han Juan Li |
| author_facet | Wenkai Xu Zhaohu Zhu Fengli Ge Zhongzhi Han Juan Li |
| author_sort | Wenkai Xu |
| collection | DOAJ |
| description | Ammonia can be produced by the respiration and excretion of fish during the farming process, which can affect the life of fish. In this paper, to research the behavior of fish under different ammonia concentration and make the corresponding judgment and early warning for the abnormal behavior of fish, the different ammonia environments are simulated by adding the ammonium chloride into the water. Different from the existing methods of directly artificial observation or artificial marking, this paper proposed a recognition and analysis of behavior trajectory approach based on deep learning. Firstly, the three-dimensional spatial trajectories of fish are drawn by three-dimensional reconstruction. Then, the influence of different concentrations of ammonia on fish is analyzed according to the behavior trajectory of fish in different concentrations of ammonia. The results of comparative experiments show that the movement of fish and vitality decrease significantly, and the fish often stagnates in the water of containing ammonium chloride. The proposed approach can provide a new idea for the behavior analysis of animal. |
| first_indexed | 2024-03-10T17:46:07Z |
| format | Article |
| id | doaj.art-ac0089f74cbd4d6387ab4699fc7c631a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T17:46:07Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ac0089f74cbd4d6387ab4699fc7c631a2023-11-20T09:30:50ZengMDPI AGSensors1424-82202020-08-012016442510.3390/s20164425Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for FishWenkai Xu0Zhaohu Zhu1Fengli Ge2Zhongzhi Han3Juan Li4School of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaSchool of Science and Information, Qingdao Agricultural University, Qingdao 266109, ChinaSchool of Management, Qingdao Agricultural University, Qingdao 266109, ChinaSchool of Science and Information, Qingdao Agricultural University, Qingdao 266109, ChinaSchool of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaAmmonia can be produced by the respiration and excretion of fish during the farming process, which can affect the life of fish. In this paper, to research the behavior of fish under different ammonia concentration and make the corresponding judgment and early warning for the abnormal behavior of fish, the different ammonia environments are simulated by adding the ammonium chloride into the water. Different from the existing methods of directly artificial observation or artificial marking, this paper proposed a recognition and analysis of behavior trajectory approach based on deep learning. Firstly, the three-dimensional spatial trajectories of fish are drawn by three-dimensional reconstruction. Then, the influence of different concentrations of ammonia on fish is analyzed according to the behavior trajectory of fish in different concentrations of ammonia. The results of comparative experiments show that the movement of fish and vitality decrease significantly, and the fish often stagnates in the water of containing ammonium chloride. The proposed approach can provide a new idea for the behavior analysis of animal.https://www.mdpi.com/1424-8220/20/16/4425ammonia concentrationbehavior analysisdeep learningFaster R-CNNfishYOLO-V3 |
| spellingShingle | Wenkai Xu Zhaohu Zhu Fengli Ge Zhongzhi Han Juan Li Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish Sensors ammonia concentration behavior analysis deep learning Faster R-CNN fish YOLO-V3 |
| title | Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish |
| title_full | Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish |
| title_fullStr | Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish |
| title_full_unstemmed | Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish |
| title_short | Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish |
| title_sort | analysis of behavior trajectory based on deep learning in ammonia environment for fish |
| topic | ammonia concentration behavior analysis deep learning Faster R-CNN fish YOLO-V3 |
| url | https://www.mdpi.com/1424-8220/20/16/4425 |
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