Journal of Agricultural Big Data >
Spatial Feature Fusion-Based ViT Method for Fine-Grained Classification of Wolfberry Pests
Received date: 2024-09-09
Accepted date: 2024-10-14
Online published: 2024-12-02
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.
SUN LuLu, LIU JianPing, ZHOU GuoMin, WANG Jian, LIU LiBo . Spatial Feature Fusion-Based ViT Method for Fine-Grained Classification of Wolfberry Pests[J]. Journal of Agricultural Big Data, 2024 , 6(4) : 522 -531 . DOI: 10.19788/j.issn.2096-6369.000066
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