Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming

Abstract

The development of better monitoring technologies, the early combat of outbreaks, massive mortality, and promoting sustainability are challenges that the aquaculture industry still faces, and the development of solutions for this is an open problem. In this paper, focusing our attention on monitoring technologies as a promising solution to these issues, we report a Gaussian distribution model for detecting dangerous operating conditions in industrial fish farming. This approach allows us to indicate through a 2D image visualization when fish production is under normal, warning, or dangerous operating conditions. Furthermore, our proposed method has promising possibilities for application in the most varied fields of science, given that the mathematical procedure described allows us to discover the fundamental statistical structure of physical, chemical, and biological systems governed by laws of a probabilistic nature.

Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers

“…Moreover, the implications of this method extend beyond cage aquaculture, offering potential applications for advanced decision making across scientific fields. The statistical analysis unveils data patterns within various physical, chemical, and biological systems [9].…”

Recent Advancements in Deep Learning Frameworks for Precision Fish Farming Opportunities, Challenges, and Applications

“…Te aquaculture business still confronts obstacles such as the development of improved monitoring systems, the early detection of outbreaks, enormous mortality, and encouraging sustainability, and fnding answers to these issues is a work in progress. Te Gaussian distribution model has been proposed by Silva et al [24] for detecting harmful operating circumstances in commercial fsh farming. Tis study focuses on monitoring technology as a possible solution to these concerns.…”

“…Artifcial intelligence, machine learning using online data, and lowcost computing have all been important components of a smart decision system. Aquaculture has been improved by incorporating current technology of information, namely, the IoT, big data, or health data, and AI along with cloud services are to name a few, in order to realize an autonomous fshing production [24]. Te current study examines machine learning techniques and their applications in aquaculture throughout the previous fve years.…”