Abstract
Eggshell moist spots are a common eggshell defect that reduces consumer purchase acceptance of eggs. Current detection methods typically use single-sided dark-field imaging to assess the ratio of sum of spot areas to sum of shell area (RSS), but overestimate moist spot severity compared to consumer perception under natural lighting, leading to commercial waste. Therefore, accurate detection methods aligning with consumer visual perception are crucial for optimizing commercial egg grading. This study aimed to verify the representativeness of single-side imaging and to develop and validate a bright-field automated identification method to ensure the accuracy of RSS. 510 pink-shell eggs were detected. First, the symmetry of moist spots on eggshell was assessed. Results showed the distribution of moist spots on both sides of the image bounded by the long axis of the egg was significantly symmetrical for both under dark-field (r = 0.979, P < 0.001) and bright-field (r = 0.952, P < 0.001), confirming single-side imaging representativeness. Second, a bright-field automated method was established using optimized threshold, background subtraction, and feature filters. Comparison of the RSS by bright-field images (RSSb) and dark-field images (RSSd) revealed a significant difference (P < 0.001). This indicates that dark-field imaging could not accurately reflect the true RSS under bright-field conditions. The limitations of RSSd were further analyzed using segmented linear regression. The results showed that when the severity of eggshell moist spots was high, RSSd was greater than 7.12 %, which was significantly correlated with the RSSb (r = 0.969, P < 0.001). However, when the severity of eggshell moist spots was low, RSSd was less than 7.12 %, the correlation became smaller (r = 0.498, P < 0.001), and RSSd could not evaluate the true RSS. This meant that the old method based on dark-field images could not accurately reflect the degree of eggshell moist spots and bright-field image method should be used in future.