Abstract: Melanin grading has been widely used in the epidermal layer of live yellow feather broilers. However, it is in high demand to improve the efficiency, cost-saving and susceptibility to lighting conditions. This study aims to explore an intelligent melanin grading (ConvNeXt-WPCA) for the epidermal layer of live yellow feather broilers using the ConvNeXt model. Three key enhancements were proposed to improve the ConvNeXt-WPCA model for the recognition of melanin in broilers. Firstly, the channel weights of the input images were adjusted to treat the uneven distribution of melanin across the RGB channels in the melanin images of yellow feather broilers. The channel with more melanin was then emphasized to extract the melanin features. The reweighting of channels was used to more effectively aggregate the melanin signals for the better classification performance of the deep learning model. Secondly, Depthwise Separable Convolution (DWConv) was replaced with the partial convolution. The computational load and memory access times were reduced to improve the utilization of computational resources. Lastly, the Coordinate Attention (CA) module was introduced to focus on the key skin regions near the chest and anus of yellow feather broiler, thereby improving the classification accuracy of models. At the same time, a dual-light source image acquisition device was designed to efficiently and simultaneously collect images under both normal and polarized lighting conditions. Sufficient data was available for model training and performance evaluation. There was a minimum impact of lighting conditions on grading. Furthermore, the potential application of polarized light was also explored in the tasks of melanin grading. The results demonstrated that the ConvNeXt-WPCA model was improved by 9.68 percentage points in the grading accuracy rate of the melanin image dataset for the yellow feather broiler under natural light, compared with the standard ConvNeXt model. A final recognition accuracy rate reached 89.03%. The grading accuracy rate was improved by 15.26 percentage points in the polarized light ones, with a final recognition accuracy rate of 98.87%. Moreover, both the parameters and floating-point volume were reduced. In conclusion, the melanin image grading of the yellow feather broiler epidermal layer under polarized light conditions was superior to that under natural light. The high recognition accuracy was achieved in the ConvNeXt-WPCA melanin grading for the epidermal layer of yellow feather broilers. This finding can provide a theoretical basis and technical support for the practical application of intelligent melanin grading in the epidermal layer of yellow feather broilers. Significant implications were obtained for the accurate melanin grading of broilers in poultry industry. The ConvNeXt-WPCA model improved the accuracy of melanin grading with the required computational resources, indicating the practical and efficient solution to real-world applications. The model can also be further optimized for the application in other poultry species.