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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Bibliografiska uppgifter

Huvudupphov:	Dian Liu, Yang Cao, Jing Yang, Jianyu Wei, Jili Zhang, Chenglin Rao, Banghong Wu, Dabin Zhang
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	Nature Portfolio 2024-04-01
Serie:	Scientific Reports
Ämnen:	Self-attention mechanism Cyclic consistency adversarial network Tea disease Tobacco roasting PSNR SSIM
Länkar:	https://doi.org/10.1038/s41598-024-59918-3
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