Building a predictive empirical model of salmon filet yield of individual whole fish based on characterisation of external anatomical physiology.

随着人类数量的增长，全球鱼类种群面临越来越大的压力，但由于个体解剖特征的自然变异性，大量鱼类被浪费在加工过程中。我们正在研究一种利用自动机器视觉检测方法的新技术，以帮助鱼类加工设施根据个体的生理条件优化生鱼片的高质量产量。几十年来，世界各地的制造商一直受益于自动化和机器视觉检测工具，以改进工艺和优化产量。这些技术和工具在食品加工业中并没有广泛使用，但如果它们能被改造成用于高内在变异性的生物原料，就会显示出很大的希望。

Global fish populations are under increasing pressure as the human population grows, but a great deal of fish is wasted in processing owing to the natural variability in anatomical characteristics of individuals. We are investigating a novel technique, leveraging automated machine vision inspection methodology, to help fish processing facilities optimize the yield of high quality filets from raw fish based on the physiology of individuals. For decades, manufacturers around the world have benefited from automation and machine vision inspection tools to refine processes and optimize yield. These techniques and tools are not widely used in the food processing industry, but show a great deal of promise if they can be adapted for use on high-inherent-variability biological raw materials.