JoWUA

Abstract

The properties and characteristics of good water are essential for human and environmental health. Producers continue to voice growing concerns over the increasing surplus of urea, pollution of water systems, and potential contamination of water consumed by people. The traditional methods for analyzing urea are cumbersome, time-consuming, and costly and therefore do not meet the practical needs of water quality control. Deep learning-based urea detection has emerged as a feasible solution, utilizing artificial intelligence (AI) and machine learning (ML) algorithms. This paper presents an optimization approach for deep learning-assisted urea detection in water quality monitoring. This work will then be summarized by comparing the current deep-learning methods for urea detection, detailing their strengths and limitations. Some of the optimization techniques explored to boost performance and enhance the speed of deep learning models include transfer learning, hyperparameter tuning, and feature reduction. This can improve centroid detection efficiency and save training time and computational resources. This paper presents a smart water quality monitoring system featuring a deep learning-based urea estimation approach integrated into a sensitivity co-optimization framework. Experimental results on real knowledge kill demonstrate the effectiveness of the proposed method, providing high accuracy and fast detection in the meantime. In this regard, this work represents a step forward in the optimization-based solution known as deep learning-assisted urea detection, which can be applied in practice as a replacement for traditional methods to control water quality.