###### Abstract  
Duck egg quality classification is critical in farms, hatcheries, and salted egg processing plants, where cracked eggs must be identified before further processing or distribution. However, duck eggs present a unique challenge due to their white eggshells, which make cracks difficult to detect visually. In current practice, human inspectors use standard white light for crack detection, and many researchers have focused primarily on improving detection algorithms without addressing lighting limitations. Therefore, this paper presents duck egg crack detection using an adaptive convolutional neural network (CNN) model ensemble with multi-light channels. We began by developing a portable crack detection system capable of controlling various light sources to determine the optimal lighting conditions for crack visibility. A total of 23,904 images were collected and evenly distributed across four lighting channels (red, green, blue, and white), with 1494 images per channel. The dataset was then split into 836 images for training, 209 images for validation, and 449 images for testing per lighting condition. To enhance image quality prior to model training, several image pre-processing techniques were applied, including normalization, histogram equalization (HE), and contrast-limited adaptive histogram equalization (CLAHE). The Adaptive MobileNetV2 was employed to evaluate the performance of crack detection under different lighting and pre-processing conditions. The results indicated that, under red lighting, the model achieved 100.00% accuracy, precision, recall, and F1-score across almost all pre-processing methods. Under green lighting, the highest accuracy of 99.80% was achieved using the image normalization method. For blue lighting, the model reached 100.00% accuracy with the HE method. Under white lighting, the highest accuracy of 99.83% was achieved using both the original and HE methods. C 2025 15 7960 1 2025 15 7960  
cracks in the shell of duck eggs before making salted and preserved eggs [3] is crucial for several reasons; for example, if the shell is cracked, the egg white and yolk can leak out, leading to loss of product and flavor. Also cracked eggs can become a breeding ground for bacteria, increasing the risk of spoilage and foodborne illness. An intact shell provides a barrier against contaminants. Maintaining the integrity of the shell contributes to a better final product. An intact shell helps preserve the desired texture and appearance of the preserved eggs, resulting in a more appealing product for the market. Many used to identify cracks by listening to the sound, and the technique has been widely developed in research; for example, [PERSON] et al. [4] conducted a study on duck eggshell crack detection by nondestructive sonic measurement and analysis. So, the sound measurement enables a simple and quantitative method for duck egg crack detection and classification, with overall accuracy rates of the calibration and prediction models using five frequencies of bandwidth (1500, 5000, 6000, 8500, and 10,000 Hz) of 89.7% and 87.6%, respectively. [PERSON] et al. [5] presented the sequenced wave signal extraction and classification algorithm for duck egg crack detection. The signal was captured from cracks, the air cell membrane, and shell texture, with 92.5% and 93.1% accuracy for duck eggs and salted duck eggs, respectively. [PERSON] et al. [6] conducted a study using a transfer learning approach for duck egg quality classification based on its shell visual using ResNet-50. The experiment was separated into three classes, namely Balut/Penoy, salted egg, and table egg. The model accuracy averaged 83%. [PERSON] et al. [7] demonstrated a method for detecting cracks in hen and duck eggs using acoustic resonance analysis, achieving a crack detection accuracy of 95.5%. However, numerous research studies regarding eggshell crack detection using AI and image processing utilized chicken eggs [8] as well. [PERSON] et al. [9] presented eggshell crack detection using deep convolutional neural networks.