Research on the Evaluation Method of Eggshell Dark Spots Based on Machine Vision

Abstract

Dark spots, which are widely present in different species of eggs, not only significantly affect the appearance and reduce the commercial value of eggs, but also increase the safety hazards of edible eggs in view of that Salmonella can easily penetrate the eggshell at the location of dark spots. During the first 5 days after egg production, it is difficult to identify and evaluate dark spots on the eggshell surface under natural lighting conditions. Therefore, it is a great challenge to automatically classify commercial eggs according to the amount of dark spots at the initial stage. In this paper, a method based on machine vision was proposed for identifying and evaluating eggshell dark spots. First, the K-means clustering algorithm was used to segment the individual egg image on the production line in order to obtain the complete eggshell surface area; then, the unsharp masking method was used to enhance the dark-spot features so as to realize the recognition of dark spots; and finally, quantitative evaluation was conducted according to the amount of dark spots on the eggshell surface and the ratio of the dark-spot projected area. Our experimental results show that the proposed method is able to quickly and accurately calculate the distribution of dark spots and the ratio of the dark-spot projected area. Specifically, the processing speed of dark-spot image is 1 frame/0.5s, which is 960 times faster than the speed of manual marking (1 frame/480s), and the detection capacity of the experimental device is 3600 eggs/h. It provides an automated method for quantitatively examining dark spots on eggshells, a scientific tool for conducting further research on the formation mechanism of dark spots, as well as a technical means for the high-throughput online examination of egg quality.

Keywords

• eggshell
• dark spot
• evaluation
• machine vision
• K-means