An Enhanced Particle Swarm Optimized RBF Model for Precise Fish Population Estimation in Cage Farming

In cage aquaculture, precise estimation of fish biomass is critically important for determining appropriate feeding strategies and evaluating production capacity. Currently, prevailing fish counting approaches heavily rely on acoustic or optical technologies. However, the accuracy and reliability of the obtained data are largely compromised by factors such as fish occlusion and water turbidity in practical cage farming environments. To address this limitation, this study proposed a novel method for estimating fish population size deduced from dynamic feeding information, based on the model integrated environmental and biological factors, feed intake and biomass. A 10-week feeding experiment was carried out to collect multidimensional data including feed intake, growth parameters, and environmental variables to construct a dataset correlating feeding amount with primary influential factors. Herein a bioenergetics-informed radial basis function neural network, optimized via particle swarm optimization (BE-PSO-RBF), was developed based on those empirical data. Model validation using 47 independent test samples showed that the hybrid model achieved a mean absolute error (MAE) of 26.82, a root mean square error (RMSE) of 35.62, and a mean absolute percentage error (MAPE) of 4.14%, confirming its robust generalization performance. These findings suggest that feed-intake-based population estimation may provide a feasible complementary approach for fish population assessment under cage farming conditions similar to those investigated in this study.