Abstract
Weight is an important parameter by which the price of whole herring (Clupea harengus) is determined. Current mechanical weight graders are capable of a high throughput but have a relatively low accuracy. For this reason, there is a need for a more accurate high-speed weight estimation of whole herring. A 3-dimensional (3D) machine vision system was developed for high-speed weight estimation of whole herring. The system uses a 3D laser triangulation system above a conveyor belt moving at a speed of 1000 mm/s. Weight prediction models were developed for several feature sets, and a linear regression model using several 2-dimensional (2D) and 3D features enabled more accurate weight estimation than using 3D volume only. Using the combined 2D and 3D features, the root mean square error of cross-validation was 5.6 g, and the worst-case prediction error, evaluated by cross-validation, was ±14 g, for a sample (n = 179) of fresh whole herring. The proposed system has the potential to enable high-speed and accurate weight estimation of whole herring in the processing plants.