Convolutional neural network-based portable computer vision system for freshness assessment of crayfish (Prokaryophyllus clarkii).

Abstract

Developing novel techniques for freshness assessment are of the utmost importance in yield and trade of aquatic products. The crayfish (Prokaryophyllus clarkii) is one of the most popular freshwater products in China, and its food safety should be a serious concern. In this study, a convolutional neural network (CNN)-based portable computer vision system for freshness assessment of crayfish method was proposed. A portable microscope was utilized to collect the microscopic images of crayfish with different freshness levels. The convolutional neural network was constructed and then optimized to extract features from the microscopic images. For the pictures from the portable microscope, the prediction accuracies of freshness could be 86.5% and 83.3% when the optimized networks were applied. The results indicate that the convolutional neural network-based portable computer vision system may provide an alternative way for the freshness assessment in the crayfish industrial chain. PRACTICAL APPLICATION: Portable computer vision system was constructed by a portable microscope connected to a mobile phone. The freshness of crayfish could be rapidly assessed by analyzing the pictures of crayfish using the system. The convolutional neural network-based portable computer vision system may provide an alternative way for the freshness assessment in the crayfish industrial chain.