农业大数据学报2024，Vol.6Issue(4)：522-531,10.DOI:10.19788/j.issn.2096-6369.000066
基于空间特征融合ViT的枸杞虫害细粒度分类方法
Spatial Feature Fusion-Based ViT Method for Fine-Grained Classification of Wolfberry Pests
摘要
Abstract
To address the fine-grained pest classification challenge faced in wolfberry cultivation,we propose an agricultural pest fine-grained classification model—Spatial Feature Fusion-based Data Augmented Visual Transformer(ESF-ViT).The model first utilizes the self-attention mechanism to crop images of the foreground targets to enhance image input and supplement more detailed representations.Secondly,it combines the self-attention mechanism with a Graph Convolutional Network(GCN)to extract spatial information from the pest regions,learning the spatial posture features of the pests.To validate the effectiveness of the proposed model,we conducted experimental research on the CUB-200-2011,IP102,and Ningxia wolfberry pest dataset WPIT9K.The experimental results show that the proposed method outperforms the baseline ViT model by 1.83%,2.09%,and 2.01%respectively,and surpasses the existing state-of-the-art pest classification models.The proposed model effectively solves the fine-grained pest image classification problem in the field of agricultural pest recognition,providing a visual model for efficient pest monitoring and early warning.关键词
枸杞/视觉Transformer/细粒度图像分类/空间特征融合/数据增强Key words
wolfberry berry/vision transformer/fine-grained image classification/spatial feature fusion/data augmentation引用本文复制引用
孙露露,刘建平,周国民,王健,刘立波..基于空间特征融合ViT的枸杞虫害细粒度分类方法[J].农业大数据学报,2024,6(4):522-531,10.基金项目
国家自然科学基金项目(32460444),北方民族大学重点科研项目((2023ZRLG12),北方民族大学研究生创新项目(YCX23168). （32460444）
