Aquaculture has become essential for global food security and economic growth, but its productivity relies heavily on effective water quality management. Total ammonia nitrogen (TAN) concentration is a key parameter, as high concentrations can harm aquatic life and disrupt cultivation systems. Conventional TAN measurement methods, including colorimetric tests, spectrophotometry, electrochemical techniques, and biological assays, offer high analytical accuracy but remain costly, complex, and often inaccessible for small-scale farmers. To overcome these limitations, this study developed a low-cost, TAN monitoring prototype using image processing techniques integrated with a Raspberry Pi microcontroller. Standard TAN solutions (0-5 mg/L) were prepared to construct regression models based on red (R) green (G) blue (B) color values extracted from captured images. Validation experiments showed that although both the R and G channel models provided strong predictive capability, the G channel model demonstrated superior practical performance. The G channel achieved the coefficient of determination (R²)=0.969, mean absolute error (MAE)=0.535 mg/L, root mean squared error (RMSE)=0.805 mg/L, a sensitivity of 1.397, and a limit of detection (LOD) of 0.215 mg/L, delivering stable and consistent predictions in the concentration range of 0.2-2.0 mg/L. In contrast, the R channel model, despite achieving higher numerical accuracy, exhibited greater drift at higher TAN concentrations, reducing its reliability in operational settings. The prototype system displays TAN values on a liquid crystal display (LCD) screen and sends notifications via the LINE application, enabling aquaculturists to respond promptly to potential water quality risks. By offering an affordable, accessible, and user-friendly alternative to conventional analytical methods, the proposed system supports more effective water quality management and contributes to the sustainable development of aquaculture practices.

This article concentrates on the aeration efficiency of venturi air injectors in aquaculture or wastewater treatment. This study was designed to investigate the location of the dissolved oxygen (DO) sensor installation, with a focus on investigating the aeration mechanism by studying ten variables, including temperature, pH, oxidation-reduction potential (ORP), electrical conductivity, resistivity, total dissolved solids, salinity, pressure, dissolved oxygen, and the effect of changing the position of DO sensor installations in different locations via oxygen transfer. The water temperature was raised due to the heating of the water pump. It ranged from 29.7°C to 32.78°C, with different temperatures resulting in different oxygen solubility. The pH level increased with the rise in oxygen levels due to an increase in OH^– concentration, whereas the ORP decreased when oxygen levels rose, increasing the reduction reaction. A study on the effect of changing the position of DO sensor installations in different locations was discovered using oxygen transfer coefficients (K_La) variables. The K_La values at nozzle depths of 15 cm, 30 cm, and 45 cm were 0.0004±0.0001, 0.00042±0.0001, and 0.00136±0.00013, respectively. Therefore, there is a slight difference of K_La value when changing the position of sensor installations. An inappropriate distance between the DO sensor and nozzle installation is able to cause turbulent flow. This event resulted in an incorrect DO value. Moreover, the installation of the DO sensor too far from the nozzle resulted in a low value of DO.