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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.

The shell-closing strength (SCS) of oysters is the main parameter for physiological activities. The aim of this study was to evaluate the applicability of SCS as an indicator of live oyster health. This study developed a flexible pressure sensor system with polydimethylsiloxane (PDMS) as the substrate and reduced graphene oxide (rGO) as the sensitive layer to monitor SCS in live oysters (rGO-PDMS). In the experiment, oysters of superior, medium and inferior grades were selected as research objects, and the change characteristics of SCS were monitored at 4 °C and 25 °C. At the same time, the time series model was used to predict the survival rate of live oyster on the basis of changes in their SCS characteristics. The survival times of superior, medium and inferior oysters at 4 °C and 25 °C were 31/25/18 days and 12/10/7 days, respectively, and the best prediction accuracies for survival rate were 89.32%/82.17%/79.19%. The results indicate that SCS is a key physiological indicator of oyster survival. The dynamic monitoring of oyster vitality by means of flexible pressure sensors is an important means of improving oyster survival rate. Superior oysters have a higher survival rate in low-temperature environments, and our method can provide effective and reliable survival prediction and management for the oyster industry.

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