Total of 1072 Asian seabass or barramundi (Lates calcarifer) were harvested at two different locations in Queensland, Australia. Each fish was digitally photographed and weighed. A subsample of 200 images (100 from each location) were manually segmented to extract the fish-body area (S in cm²), excluding all fins. After scaling the segmented images to 1mm per pixel, the fish mass values (M in grams) were fitted by a single-factor model (M=aS^1.5, a=0.1695 )achieving the coefficient of determination (R²) and the Mean Absolute Relative Error (MARE) of R²=0.9819 and MARE=5.1%, respectively. A segmentation Convolutional Neural Network (CNN) was trained on the 200 hand-segmented images, and then applied to the rest of the available images. The CNN predicted fish-body areas were used to fit the mass-area estimation models: the single-factor model, M=aS^1.5, a=0.170, R²=0.9819, MARE=5.1%; and the two-factor model, M= aS^b, a=0.124, b=0.155, R²=0.9834, MARE=4.5%.