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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Auteurs principaux:	Dian Liu, Yang Cao, Jing Yang, Jianyu Wei, Jili Zhang, Chenglin Rao, Banghong Wu, Dabin Zhang
Format:	Artigo
Langue:	Inglês
Publié:	Nature Portfolio 2024-04-01
Collection:	Scientific Reports
Sujets:	Self-attention mechanism Cyclic consistency adversarial network Tea disease Tobacco roasting PSNR SSIM
Accès en ligne:	https://doi.org/10.1038/s41598-024-59918-3
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