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

随着人口的增长，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差异，大量鱼类在加工过程中被浪费。我们正在研究一种新技术，利用自动机器视觉检测方法，帮助鱼类加工设施根据个体的生理机能优化生鱼的高质量鱼片产量。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，以改进流程并优化产量。这些技术和工具在食品加工业中没有广泛使用，但如果它们能够适用于高固有变异性的生物原料，则显示出很大的前景。

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.