Egg volume estimation based on image processing and computer vision

In chicken egg production line systems, grading based on vision systems is challenging due to ambient light conditions and egg occlusion problems. This study introduces a depth image-based chicken-egg volume estimation system. Two modes of egg configurations on a sorting line were evaluated; single-egg (no occlusion) and multi-eggs (partially occluded, i.e., simple and complex). Contour curvature analysis and k-closest M-circle-center algorithms were used to segment the occluded eggs. Thirteen regression models based on the egg image (single egg) features were trained. The Exponential Gaussian Process Regression outperformed all the explored models with RMSE of 1.175 cm(3) and R-2 of 0.984. The same model estimated the volume of the eggs under partial occlusion at RMSE of 1.080 and 1.294 cm(3) for simple and complex, respectively. This introduced system can be applied as an accurate, consistent, fast, and non-destructive in-line sorting technique of chicken eggs in a production line system.