Real-time estimation of olive flounder growth in indoor aquaculture using cameras combined with a grid

Abstract

Estimating fish growth in real time has many benefits for indoor aquaculture farms, such as saving labor time and costs, reducing water pollution during feeding, improving feeding activity and determining when to harvest. Hence, this study proposed a visual-information-based method for measuring olive flounder (Paralichthys olivaceus) length, using accurate growth tracking for efficient aquaculture management. Using two cameras, an light-emitting-diode (LED) grid was placed at the bottom of the water tank to measure fish length. The pixels unit from the fish length in the captured image was converted to centimeters based on the relationship of a pre-built dataset. A total of 180 lengths were calculated using images captured by the cameras. The average length of each fish acquired from the cameras was calculated separately, and Lagrange's interpolating polynomial algorithm was implemented to calculate the overall length of each fish. This method reduced the computational complexity, and results were obtained more rapidly and in a user-friendly environment. The power model generated the length–weight relationship, which allowed us to estimate the body weight of each olive flounder based on the length. The proposed approach enabled us to calculate the length of olive flounders with a highly accurate R² of 0.995.