###### Abstract
The proposed CNN had 95.38% accuracy using 64-bit images; in addition, they also suggested a deep transfer learning approach for the detection of cracks on eggs [10]. [PERSON] et al. [11] introduced real-time ConvNext-based U-Net with feature infusion for egg microcrack detection. The crack results for Crack-IoU were 65.51% for cracks smaller than 20 um, and for even smaller cracks (<5 um), the crack results for Crack-IoU were 60.76% and, for MioU, were 80.22%. [PERSON] et al. [12] conducted submillimeter crack detection of eggs by an improved light source by enhancing crack information using a laser light source together with fixed-threshold and adaptive-threshold methods to filter noise. [PERSON] et al. [13] proposed a method for identifying cracks on eggshells using machine vision. The method is effective for complicated egg surfaces with dark spots and invisible micro-cracks, achieving a cracked egg recognition rate of 92.5%. [PERSON] et al. [14] presented an artificial intelligence-based determination of cracks in eggshells using sound signals. Their study shows the potential of SVM and ANN to significantly improve the efficiency of egg crack detection in automated systems. [PERSON] et al. [15] reported on the identification of cracks in egg shells using computer vision and hough transform. The output of this identification and classification of intact and cracked eggs reached 90.1% accuracy, with 0.7 s average processing time. There are some applications of real-time monitoring [16] and classification of eggs based on an industrial concept. [PERSON] [17] developed a real-time system based on a continuous rotating system by combining retrained CNN and BiLSTM. This proposed solution was able to classify cracked, bloody, dirty, and robust eggs with good computation performances. [PERSON] et al. [18] presented an image processing and computer vision of eggshell crack detection to minimize unexpected noise from the image. [PERSON] et al. [19] obtained the classification of overlapping eggs based on image processing. Crack detection using deep learning [20; 21] is a significant area of research and application, not only in the agriculture industry but also in fields such as civil engineering,infrastructure maintenance, and material science. Several applications have been developed using deep learning models to detect cracks in various materials, including concrete [22], steel [23], and even pipelines [24; 25]. For example, [PERSON] et al. [26] evaluated the performance of crack detection on concrete structures using deep CNN-based models (VGG-16, VGG-19, ResNet-50, and Inception V3). [PERSON] et al. [27] proposed an ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement. The proposed method is efficient for both crack detection and measurement, offering significant improvements in precision, recall, and F1-score compared to public datasets. [PERSON] et al. [28] proposed an improved YOLOv5 detection model that efficiently detects cracks in real time as an early warning system for coal mine safety. The reviewed literature highlights that duck egg crack detection has been explored using various methods, including acoustic techniques, image processing, and transfer learning. However, several limitations and research gaps remain, such as sensitivity to environmental noise, limited accuracy of deep learning models, low processing efficiency, and uncontrolled lighting conditions. To address these gaps, this paper presents a comparative analysis of multi-light sources in deep learning-based duck egg crack detection by developing a controlled lighting environment that simulates different light frequencies. Subsequently, adaptive CNN models were used to evaluate and compare the performance and accuracy of crack detection. The models were assessed using a confusion matrix as well as evaluation metrics, including accuracy, precision, recall, and F1-score.