Research on growth prediction models for large yellow croaker in aquaculture vessel tanks

Smart fishery is driving aquaculture production methods toward more efficient and sustainable directions. Digital technology has become a key driving force for the modernization of fisheries. Various algorithm models, such as water quality regulation models and feeding control models, have been widely applied in aquaculture scenarios. To optimize the precise management of large yellow croaker raised in deep and offshore vessel compartments, this study constructed a temperature-corrected Logistic growth prediction model based on measured data from the "Conson No.1″ aquaculture vessel. Using the Analytic Hierarchy Process (AHP), temperature was identified as the key environmental factor affecting the growth of tank-reared large yellow croaker on aquaculture vessels, and a temperature response function was incorporated to dynamically regulate the growth rate parameters of the Logistic model.The large yellow croaker was divided into three groups based on initial body weight: low (LW: 100±50 g), medium (MW: 200±50 g), and high (HW: 300±50 g), with model parameters fitted in stages. The results showed that the temperature-corrected model significantly outperformed the traditional Logistic model, with goodness of fit (R²) exceeding 0.88 for all groups and no significant difference between predicted and observed values. The effect of initial body weight stage was significant, with the MW group showing the fastest growth rate, earliest inflection point, and peak daily weight gain of 1.9 g/d; the LW group grew slowly with the latest inflection point; the HW group showed slowing growth. The universal optimal temperature range was 21–23 °C, with the highest growth rate at 22 °C; growth was significantly inhibited at temperatures >24 °C, with the MW group being most sensitive to high temperatures. This model quantifies the dynamic relationship between large yellow croaker growth, temperature, and body weight, providing scientific basis for optimizing feeding strategies, harvest timing, and temperature control in fishing vessel compartment environments, promoting refined and intelligent development of deep-sea aquaculture.