A hybrid model of CNN+ViT of the recognition for Spodoptera frugiperda larval instar stages

Mingyang Wang; Ying Lu; Quanyuan Xu; Tianzi Xiang; Jumei Chang; Hanrui Zhang

doi:10.1117/12.3025899

8 April 2024 A hybrid model of CNN+ViT of the recognition for Spodoptera frugiperda larval instar stages

Mingyang Wang, Ying Lu, Quanyuan Xu, Tianzi Xiang, Jumei Chang, Hanrui Zhang

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130901D (2024) https://doi.org/10.1117/12.3025899
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Spodoptera frugiperda (Fall armyworm, FAW) is one of the major agricultural pests in the world. It is of great significance for ensuring crop yield to accurately recognize FAW larval instars. This research proposes a hybrid model of Convolutional Neural Network (CNN) and Vision Transformer (ViT) for the recognition of FAW larval instars. The model uses CNN to extract local features of larval images and ViT to capture global image dependencies, achieving effective recognition of larval instars. To verify the model proposed in this paper, an image dataset of 5,006 images balancing the larval instars which are from the 1st to the 6th of FAWs is established. The proposed model achieved recognition accuracy of 99.60% and 92.11% respectively in the two cases of using and not using pre-training. The results show that the proposed model achieves higher recognition accuracy than AlexNet, VGG16, ResNet50, ResNet101, and ViT-B. Moreover, it does not rely on pre-trained weights and performs well on relatively small datasets. The research in this paper provides a new technique of the recognition for FAW instars and also provides a reference for other agricultural pest image recognition tasks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingyang Wang, Ying Lu, Quanyuan Xu, Tianzi Xiang, Jumei Chang, and Hanrui Zhang "A hybrid model of CNN+ViT of the recognition for Spodoptera frugiperda larval instar stages", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130901D (8 April 2024); https://doi.org/10.1117/12.3025899

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KEYWORDS

Data modeling

Education and training

Performance modeling

Visual process modeling

Agriculture

Transformers

Deep learning

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