Machine vision based intelligent system for artificially reared fish condition monitoring

Abstract

In the context of sustainable aquaculture, it is essential for fish farms to adopt objective monitoring and control systems tailored to the intricate technological processes inherent to artificial fish farming. The delicate balance of ecosystem conditions is vulnerable to disturbances that can emanate from various sources, including parasitic infestations and pathogenic outbreaks. Such disruptions pose a considerable threat to the viability of fishery operations, thereby necessitating the integration of intelligent systems capable of continuously monitoring and regulating the environmental and biological parameters of aquaculture.This paper expounds upon the functional capabilities of an advanced monitoring system rooted in machine vision technologies. By employing these cutting-edge methodologies, the system offers an array of functionalities designed to enhance user experience across different categories of stakeholders involved in aquaculture. Furthermore, the findings elucidated herein pave the way for the creation of formal models that delineate both processes and objects crucial to artificial fish farming. These models serve as a foundational basis for developing sophisticated software solutions and refining technical specifications, ultimately contributing to the resilience and efficiency of fish farming practices. Through these advancements, the industry can aspire to mitigate risks and bolster sustainability in aquaculture operations.