Research on water quality detection integrating spectral analysis and automated control

Water quality conditions have a significant impact on the aquaculture environment, however, high cost, low efficiency, and lack of portable testing equipment limit the field application of water quality testing. In this paper, a highly integrated, cost-effective, and efficient water quality testing device is designed and fabricated based on spectral analysis and automated control technology. The device maintains high stability under complex environments and long-time operating conditions, and realizes simultaneous multi-parameter detection and automation of the detection process through IoT control and embedded design, thus facilitating timely monitoring and intervention in the fisheries environment. Using low concentrations of ammonia nitrogen and nitrite as research objects, the data collected by a multi-channel spectral sensor was used to classify the water health level, and the experimental results showed that the accuracy of the device's detection was as high as 96.8 percent, while the manufacturing cost was only $60. Overall, this interdisciplinary approach combining spectral analysis, device development, and machine learning provides a promising solution for efficient water quality monitoring and management in aquaculture.