Merging Manual and Automated Egg Candling: A Safety and Social Solution

Eggs should comply with strict quality control processes. The first step of the quality process is egg candling analysis. Egg candling is a non-destructive procedure that consists on applying light against an egg to detect abnormalities. This process is usually done manually at small and medium poultry factories. Manual egg candling is prone to human mistakes and can cause health problems to the workers. It is necessary to implement an automated process. The following study briefly describes a device that merges automated and manual egg candling analysis. Furthermore, it goes beyond the design and describes valid solutions regarding occupational safety and malnutrition that can emerge due to the implementation of this innovative design

Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms

This study explores the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset using augmented images. The training results demonstrated that all models achieved high accuracy, indicating their ability to accurately learn and classify chicken eggs’ fertility state. However, when evaluated on the testing set, variations in accuracy and performance were observed. VGG16 achieved a high accuracy of 0.9803 on the testing set but had challenges in accurately detecting fertile eggs, as indicated by a NaN sensitivity value. ResNet50 also achieved an accuracy of 0.98 but struggled to identify fertile and non-fertile eggs, as suggested by NaN values for sensitivity and specificity. However, InceptionNet demonstrated excellent performance, with an accuracy of 0.9804, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.9615 for identifying non-fertile eggs. MobileNet achieved an accuracy of 0.9804 on the testing set; however, it faced challenges in accurately classifying the fertility status of chicken eggs, as indicated by NaN values for both sensitivity and specificity. While the models showed promise during training, variations in accuracy and performance were observed during testing. InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. Further optimization and fine-tuning of the models are necessary to address the limitations in accurately detecting fertile and non-fertile eggs. This study highlights the potential of CNN-Transfer Learning for nondestructive fertility detection and emphasizes the need for further research to enhance the models’ capabilities and ensure accurate classification