Early-stage fertilised egg viability detection based on machine vision

1. In the early stages of incubation, challenges arise in the intelligent recognition of multiple eggs on the incubation tray and in achieving consistent high-throughput detection. To address these issues, a method was proposed using a monochrome camera to capture transillumination images of eggs. This work examined factors affecting image consistency, such as light source intensity, imaging uniformity and egg positioning and developed a correction algorithm for non-uniform light intensity in the captured images.2. On day 0 of incubation, images of the egg tray and fertilised eggs were acquired. After applying median filtering, Laplacian sharpening and fixed-threshold segmentation, the egg regions from the images were extracted. These regions were then converted into labelled images for circular fitting, with the fitted circles contracted inward by 10 pixels to define the target egg region as the template for viability detection.3. Using these template images, egg regions from days 5 to 9 of incubation were extracted and four greyscale features derived; mean, maximum, minimum and standard deviation, and four texture features; energy, correlation, homogeneity and contrast were used as input parameters for classification models using Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and a custom Convolutional Neural Network (CNN).4. The CNN model demonstrated the best performance, achieving 99% accuracy on day 8, with Precision, Recall and F1 scores of 0.99, 1.00 and 0.99 for viable embryos, respectively. For non-viable and infertile eggs, Precision, Recall and F1 scores were 1.00, 0.95 and 0.98, respectively. The optimal detection time was determined to be day 6, with an accuracy of 95%, which was one day earlier than the optimal manual inspection time.5. These findings showed that using a monochrome camera with image processing and classification models could enable high-throughput, early-stage viability detection of fertilised eggs. This can be used as technical support for the development of automated detection systems.