Multi-Scale Cross-Domain Augmentation of Tea Datasets via Enhanced Cycle Adversarial Networks

Taojie Yu, Jianneng Chen (), Zhiyong Gui, Jiangming Jia, Yatao Li, Chennan Yu and Chuanyu Wu
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Taojie Yu: School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
Jianneng Chen: School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
Zhiyong Gui: School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
Jiangming Jia: School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
Yatao Li: School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
Chennan Yu: School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
Chuanyu Wu: Zhejiang Ocean University, Zhoushan 316022, China

Agriculture, 2025, vol. 15, issue 16, 1-27

Abstract: To tackle phenotypic variability and detection accuracy issues of tea shoots in open-air gardens due to lighting and varietal differences, this study proposes Tea CycleGAN and a data augmentation method. It combines multi-scale image style transfer with spatial consistency dataset generation. Using Longjing 43 and Zhongcha 108 as cross-domain objects, the generator integrates SKConv and a dynamic multi-branch residual structure for multi-scale feature fusion, optimized by an attention mechanism. A deep discriminator with more conv layers and batch norm enhances detail discrimination. A global–local framework trains on 600 × 600 background and 64 × 64 tea shoots regions, with a restoration-paste strategy to preserve spatial consistency. Experiments show Tea CycleGAN achieves FID scores of 42.26 and 26.75, outperforming CycleGAN. Detection using YOLOv7 sees mAP rise from 73.94% to 83.54%, surpassing Mosaic and Mixup. The method effectively mitigates lighting/scale impacts, offering a reliable data augmentation solution for tea picking.

Keywords: CycleGAN; tea shoots; data augmentation; style transfer; multi-scale feature (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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