Study on the Tea Pest Classification Model Using a Convolutional and Embedded Iterative Region of Interest Encoding Transformer

SIMPLE SUMMARY: This paper is mainly based on the tea disease leaves for image classification research, using a combination of convolution, iterative module and transformer in the form of a combination of the traditional convolution for local feature extraction advantage and transformer for global f...

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Detalles Bibliográficos
Autores principales: Zhan, Baishao, Li, Ming, Luo, Wei, Li, Peng, Li, Xiaoli, Zhang, Hailiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376105/
https://www.ncbi.nlm.nih.gov/pubmed/37508446
http://dx.doi.org/10.3390/biology12071017

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376105/
https://www.ncbi.nlm.nih.gov/pubmed/37508446
http://dx.doi.org/10.3390/biology12071017

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