Multisensor monitoring and water quality prediction for live ornamental fish transportation based on artificial neural network

Abstract

The microenvironment of live ornamental fish transportation is a significant source of fish mortality. The transportation time and density of fish have a significant impact on water quality. The previous studies measured water quality parameters with traditional and non‐real‐time methods, which cannot give the abrupt changing patterns during live transportation of ornamental fish. In this study, water quality key parameters in the microenvironment of goldfish were monitored using a multisensor box during the simulation transportation experiment. According to the findings, nitrite was not identified, and pH remained within the acceptable goldfish range even after 48 h of monitoring, indicating that these factors did not affect the goldfish physiology during commercial transport. The collected data were correlated with physiological health and fish behaviour to identify the most impactful parameters. Data sets went through a data screening process (data correction, filtering); ammonia nitrogen and dissolved oxygen fed into the multilayer neural network and regression analysis, with time and density as input variables. The neural network successfully predicts the dissolved oxygen and ammonia nitrogen with mean absolute error (MAE) of 0.2306 and 0.00775. The regression model achieved good prediction accuracy for only ammonia nitrogen, with a mean absolute error (MAE) of 0.00484 and a relative average error of 4.58%. However, for future studies, the prediction model must account that the distinct physiology of the transported fish will result in distinct changes in water quality.

A Water Quality Prediction Method Based on Deep LSTM Network

“…High-quality sample data is the basis for ensuring the performance and generalization ability of neural networks. While obtaining water quality data, the raw data obtained have problems such as noise and missing values [11]. Wavelet threshold denoising method is used to preprocess the sample data to reduce the impact of monitoring noise on model training.…”

Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand

“…AI assumes a pivotal role in developing predictive models that anticipate changes in water quality before they occur. It can be helpful in analyzing the historical data on water quality and other factors such as weather patterns and feeding schedules ( Saeed et al, 2022 ). Besides, AI algorithms can predict the likelihood of changes in water quality and provide early warnings to farmers.…”

Aplikasi Kontrol Kualitas Air dan Pakan Untuk Peningkatan Produktivitas Budidaya Betta Fish

“…Budidaya ikan hias saat ini mulai berkembang pesat dalam membantu meningkatkan nilai ekonomi dan rekreasi pertanian (1). Budidaya perikanan atau aquakulture adalah budidaya organisme akuatik termasuk ikan, moluska, krustasea, dan tanaman air [2].…”