A fine-grained grading network for natural products based on dynamic association inference

Abstract

Fine-grained grading of natural products, based on internal defects, plays an important role in food processing. Although X-ray imaging is critical for revealing internal defects due to its capacity to penetrate surfaces, the inherent grayscale and lower contrast make it challenging to extract fine-grained features. To address this, we propose the Fine-Grained Grading Network (FGGN) for classifying the quality grades of multiple natural products from X-ray images. Leveraging a ResNet18 backbone, FGGN enhances feature extraction by employing a hybrid attention module that emphasizes crucial local details by analyzing joint dependencies within tensor dimensions. Moreover, by combining cross-dimensional context encoding and dynamic graph convolution, a dynamic correlation inference module is proposed to construct dynamic graphs in a gated inference pattern, thereby integrating fine-grained information across abstraction levels. Finally, the cross-entropy loss is combined with graph constraint loss to train the network end-to-end. Extensive evaluation of three natural product datasets shows that the Fine-Grained Grading Network achieves state-of-the-art recognition performance, which demonstrates its grading applicability to multiple natural products.