AI-powered recognition of Chinese medicinal herbs with semantic structure modeling and gradient-guided enhancement.

Digital image processing and object recognition are fundamental tasks in sensor-driven intelligent systems. This paper proposes a structure-aware artificial intelligence framework tailored for fine-grained recognition of medicinal plant images captured by visual sensors. Compared with recent herbal recognition approaches such as CNN enhanced with attention mechanisms, cross-modal fusion strategies, and lightweight transformer variants, our method advances the field by jointly integrating graph-based structural modeling, a Bidirectional Semantic Transformer for multi-scale dependency optimization, and a Gradient Optimization Module for gradient-guided refinement. Built upon a Swin-Transformer backbone, the proposed framework effectively enhances semantic discriminability by capturing both spatial and channel-wise dependencies and adaptively reweighting class-discriminative features. To comprehensively validate the framework, we perform experiments on two datasets: (i) the large-scale TCMP-300 benchmark with 52,089 images across 300 categories, where our model achieves 90.32% accuracy, surpassing the Swin-Base baseline by 1.11%; and (ii) a self-constructed herbal dataset containing 1,872 images across 7 classes. Although the latter is relatively small and not intended as a large-scale benchmark, it serves as a challenging evaluation scenario with high intra-class similarity and complex backgrounds, on which our model achieves 92.75% accuracy, improving by 1.18%. These results demonstrate that the proposed framework not only advances beyond prior herbal recognition models but also provides robust, and sensor-adaptable solutions for practical plant21 based applications.