SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN

SM-CycleGAN: crop image data enhancement method based on self-attention mechanism CycleGAN

Abstract Crop disease detection and crop baking stage judgement require large image data to improve accuracy. However, the existing crop disease image datasets have high asymmetry, and the poor baking environment leads to image acquisition difficulties and colour distortion. Therefore, we explore th...

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Detalhes bibliográficos
Main Authors: Dian Liu, Yang Cao, Jing Yang, Jianyu Wei, Jili Zhang, Chenglin Rao, Banghong Wu, Dabin Zhang
Formato: Artigo
Idioma:English
Publicado em: Nature Portfolio 2024-04-01
Colecção:Scientific Reports
Assuntos:
Self-attention mechanism
Cyclic consistency adversarial network
Tea disease
Tobacco roasting
PSNR
SSIM
Acesso em linha:https://doi.org/10.1038/s41598-024-59918-3

Internet

https://doi.org/10.1038/s41598-024-59918-3

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