Abstract: "Wuchang Rice" (GB/T 19266-2008) is protected by geographical indications. However, counterfeiting is common due to the significant variation in rice quality between the core producing areas and other regions. Therefore, further distinguishing core production areas within Wuchang Rice’s geographical indication product is crucial for its development, protection, and enhancement. This research focuses on Minle Township, one of the core production areas of fragrant rice in Wuchang region. This research selected fragrant rice (variety: Dahuaxiang No.2, also known as Wuyou Rice No.4) from Minle Township and compared it with the same variety from other non-Minle Township areas within Wuchang region. Polarized light images of rice grains were collected, comprising a dataset of 30000 rice grain images. Subsequently, the study employed three deep learning methods, namely DenseNet, GoogleNet, and ResNet50, which were suitable for image classification with minimal phenotypic differences, to discriminate between rice grains from Minle Township and those from non-Minle Township areas within Wuchang region. Utilizing the confusion matrix values of the models, accuracy, precision, recall, and F₁ value were calculated. The results revealed that DenseNet model performed optimally, achieving a test accuracy of 0.991. GoogleNet model followed with a test accuracy of 0.966, while ResNet50 model exhibited good performance on the training set but poor performance on the test set, showing signs of overfitting, with a test accuracy of 0.948. Although visually similar, polarized images of rice grains of the same variety from Minle Township and non-Minle Township areas within Wuchang region were effectively distinguished through the collection of polarized information images and the utilization of complex deep learning models. Therefore, the combination of polarized imaging technology and deep learning methods is a feasible approach for authenticating fragrant rice from the core production areas in Wuchang region.