Few-shot learning based on dual-attention mechanism for orchid species recognition

With the advancement of technology and improvements in cultivation techniques coupled with growing market demand, the global orchid market continues to expand, giving the orchid industry extremely high research and commercial value. However, because of the wide variety of orchid species, relying solely on human visual recognition or traditional paper-based data for comparison is time-consuming and labor-intensive, making orchid species recognition challenging. Deep learning technology has brought significant advancements to the field of image recognition. However, publicly available orchid datasets in the real world are scarce, and manually collecting a large amount of data incurs high costs. In situations with limited data, training deep learning models on a large scale is extremely difficult. To address these issues, this study employed the few-shot learning method, enabling the training of highly effective models under limited data conditions. This study proposes a few-shot learning and diffusion model data augmentation method based on dual-attention mechanisms for orchid species recognition. In the data preprocessing stage, stable diffusion technology was used to generate additional images to augment the original dataset. In addition, this study adopted ResNet34 as the backbone network through transfer learning mechanisms and utilized prototypical network classification algorithms for model training. The experimental results demonstrate that the proposed model achieves accuracies of 84.23% and 93.32% on 5-way 1-shot and 5-way 5-shot tasks, respectively. Furthermore, the generalizability of the proposed model was validated on the Omniglot and CIFAR-FS public datasets. The experimental results also indicate that the method proposed in this study can reduce reliance on large amounts of data and improve training efficiency.