Publication Details

Abstract

Good water quality, therefore, becomes an essential requirement for successful aquaculture, as it directly influences the growth of fish. Currently, in water quality analysis, water is manually sampled. This process is often tedious. Moreover, it is not suitable for decision support. With the aim of resolving these problems, this paper presents an intelligent web-based system, named HydroHealth, for water quality analysis in an effortless manner.
The proposed system, besides water quality analysis, also assists in determining appropriate fish species, identifying issues, and offering solutions. It applies simple rule-based reasoning with data analysis to provide accurate water quality analysis in an understandable format. Moreover, it includes interactive visualizations, which make it easier for users to comprehend the analysis. Unlike the majority of the existing deep learning-based systems, the proposed HydroHealth system emphasizes the importance of being lightweight, efficient, and easy to deploy without compromising the usability of the system. The experimental results prove that the proposed system can efficiently assess the quality of the water and make appropriate recommendations. The simple and efficient design of the proposed system makes it appropriate for real-time usage even in scenarios with low computational power. The proposed HydroHealth system provides an effective and user-friendly solution for improving modern aquaculture practices.