A system and method for fish behavior recognition in unmanned fishery based on underwater robot

Abstract

The invention relates to the technical field of intelligent aquaculture data analysis, and discloses an underwater robot-based fish swarm behavior recognition system and method in an unmanned fishery. The echo signal is filtered to obtain the first signal, and then the activity coefficient is calculated by using the first signal calculation scheme, and then the abnormal rate of the fish pond is calculated. In a complex fish farm environment, after measuring and feeding fish groups, the fish activity in the pond can be accurately identified, and the state of the fish pond or the health state of the fish group can be scientifically analyzed according to the change of its activity, which greatly improves the abnormality. The accuracy and reliability of the identification greatly reduce the risk of economic loss caused by the breeding user or operator due to the fault of abnormal identification of fish, and have a beneficial impact on the utility rate of resources and energy.

Description

technical field [0001] The invention belongs to the technical field of data collection, relates to the technical field of intelligent aquaculture data analysis, and in particular relates to an underwater robot-based fish swarm behavior recognition system and method for unmanned fisheries. Background technique [0002] In the fish farming industry, the farmed fish are easily affected by external factors, including bacteria, viruses or other species and other uncertain factors, which will cause the fish to appear abnormal. Its growth and development speed can lead to pan-ponds in severe cases. However, if the health status of fish or the status of the pond is judged only by human vision or experience, it is often impossible to distinguish subtle differences, resulting in the discovery of abnormality too late and resulting in irreparable economic losses; therefore, a scientific and effective method is urgently needed. The health of the fish school or the state of the pond can ...

AI Technical Summary

Problems solved by technology

However, if the health status of fish or the status of ponds is judged only by human vision or experience, it is often impossible to distinguish subtle differences, resulting in too late discovery of abnormalities and irreparable economic losses; therefore, there is an urgent need for a scientific and effective method. Identify the health of the fish or the state of the pond to avoid the risk of various abnormalities of the fish. The current existing technical method is to detect the activity of the fish through diagnostic vibration, computer vision and other methods. The detection accuracy is low and the response is slow. The accuracy and reliability are not high due to various interference factors

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