Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish

Sensors

Fengli Ge¹  Juan Li²  Wenkai Xu²  Zhaohu Zhu³  Zhongzhi Han³ 

[1] School of Management, Qingdao Agricultural University, Qingdao 266109, China;School of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China;School of Science and Information, Qingdao Agricultural University, Qingdao 266109, China;

关键词: ammonia concentration;    behavior analysis;    deep learning;    Faster R-CNN;    fish;    YOLO-V3;   

DOI  :  10.3390/s20164425

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown