## 4 Conclusions  
This paper presents a deep learning approach for detecting cracks in duck eggs by applying transfer learning on MobileNetV2 and introducing an adaptive version of the model designed for four different lighting channels: red, green, blue, and white. Image pre-processing techniques, specifically CLAHE and histogram equalization (HE), were applied to enhance image quality. Additionally, a model comparison was conducted to identify the most suitable architecture for the dataset. The original MobileNetV2 model was evaluated under all four lighting conditions using three pre-processing methods: raw images (no pre-processing), CLAHE, and HE. Under red lighting, both CLAHE and HE yielded the highest scores, reaching 99.80% in accuracy, precision, recall, and F1-score. For green lighting, HE performed best, achieving 98.22% in both accuracy and F1-score, while the other methods showed lower performance. Under blue lighting, the model performed best with raw images, achieving 99.55% across all evaluation metrics. Similarly, under white lighting, raw images again delivered the best performance, with 98.34% accuracy, whereas CLAHE resulted in the lowest performance. To further improve performance and accuracy, an adaptive version of MobileNetV2 was introduced through fine-tuning. The model was tested under the same four lighting conditions with the same pre-processing methods. Under red lighting, all pre-processing techniques achieved 100% across all evaluation metrics. For green lighting, the original and normalization methods provided the highest scores at 99.60%, while HE and CLAHE performed slightly worse, with CLAHE producing the lowest values. Under blue lighting, HE achieved 100%, while normalization resulted in the lowest accuracy at 98.22%. For white lighting, both the original and HE methods achieved the highest accuracy at 99.83%, with normalization and CLAHE trailing slightly behind. These findings highlight the importance of selecting appropriate pre-processing methods tailored to each lighting condition to enhance model accuracy and reliability, especially for tasks that rely on subtle visual differences. Moreover, the experiment was conducted in a real-world duck egg processing facility, demonstrating the practical reliability of the system and earning trust from both researchers and business stakeholders. Future work may expand on this foundation by scaling the algorithm for full integration into industrial duck egg processing lines. This advancement \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Light Color** & **Pre-Processing** & **Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline \multirow{4}{*}{Red} & Original & \multirow{4}{*}{\begin{tabular}{c} **100.00** \\ **Normalization** \\ **CLAHE** \\ \end{tabular} } & **100.00** & **100.00** & **100.00** & **100.00** \\  & & **100.00** & **100.00** & **100.00** \\  & & **100.00** & **100.00** & **100.00** \\  & & CLAHE & 99.60 & 99.59 & 99.61 & 99.60 \\ \hline \multirow{4}{*}{Green} & Original & \multirow{4}{*}{\begin{tabular}{c} **99.60** \\ **Normalization** \\ **HE** \\ \end{tabular} } & 99.60 & 99.58 & 99.63 & 99.60 \\  & & **99.80** & **99.79** & **99.81** & **99.80** \\  & & & 99.01 & 99.02 & 99.01 \\  & & CLAHE & Adaptive & 98.42 & 98.44 & 98.42 \\ \hline \multirow{4}{*}{Blue} & Original & \multirow{4}{*}{\
\begin{tabular}{c} **99.78** \\ **Normalization** \\ **HE** \\ \end{tabular} } & 99.78 & 99.77 & 99.79 & 99.78 \\  & & 98.22 & 98.25 & 98.25 & 98.22 \\  & & **100.00** & **100.00** & **100.00** & **100.00** \\  & & 99.33 & 99.32 & 99.34 & 99.33 \\ \hline \multirow{4}{*}{White} & Original & \multirow{4}{*}{
\begin{tabular}{c} **99.83** \\ **Normalization** \\ **HE** \\ \end{tabular} } & **99.83** & 99.83 & **99.84** & **99.83** \\  & & 99.50 & 99.50 & 99.51 & 99.50 \\ \cline{1-1}  & & **99.83** & **99.84** & 99.83 & **99.83** \\ \cline{1-1}  & & 99.50 & 99.51 & 99.50 & 99.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Output of the experiments with adaptive MobileNetV2.