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<p>Sugarcane, an important tropical/subtropical crop, suffers yield and quality losses from foliar diseases. Traditional manual identification methods are inefficient and often lead to overuse of pesticides. Recent advancements in deep learning, especially convolutional neural networks, show promise for plant disease recognition. However, challenges such as limited field data and complex backgrounds persist. This research proposes an integrated “YOLOv10n-seg-p6 + CycleGAN” framework for mobile-based sugarcane disease identification. First, YOLOv10n-seg-p6 is employed to accurately segment sugarcane leaf regions and eliminate background interference; then, the segmented pure leaf images are fed into CycleGAN to transform healthy leaves into diseased ones with realistic pathological features, thereby achieving high-quality data augmentation and sample balancing to enhance the performance of the classification model. Experimental results demonstrate enhanced authenticity of synthetic samples compared to traditional CycleGAN approaches, with the average FID score decreasing by 4.61 (from 74.85 to 70.24). Swin Transformer classifier trained on enhanced data achieves 98.2% test accuracy, outperforming models trained on original or standard CycleGAN-generated data. The mobile solution, tested under 30 concurrent user requests, ensures stable field deployment, offering efficient, real-time disease diagnosis for farmers.</p>

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Integrating YOLOv10n-seg-p6 Segmentation and CycleGAN Adversarial Augmentation for Smartphone-Based Precision Diagnosis of Sugarcane Leaf Diseases

  • 期刊论文

  • 发表日期：

    2025年8月13日

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  • Jiasheng Chen,
  • Hongwei Li,
  • Shunsheng Zhang,
  • Tao Wu

摘要

Sugarcane, an important tropical/subtropical crop, suffers yield and quality losses from foliar diseases. Traditional manual identification methods are inefficient and often lead to overuse of pesticides. Recent advancements in deep learning, especially convolutional neural networks, show promise for plant disease recognition. However, challenges such as limited field data and complex backgrounds persist. This research proposes an integrated “YOLOv10n-seg-p6 + CycleGAN” framework for mobile-based sugarcane disease identification. First, YOLOv10n-seg-p6 is employed to accurately segment sugarcane leaf regions and eliminate background interference; then, the segmented pure leaf images are fed into CycleGAN to transform healthy leaves into diseased ones with realistic pathological features, thereby achieving high-quality data augmentation and sample balancing to enhance the performance of the classification model. Experimental results demonstrate enhanced authenticity of synthetic samples compared to traditional CycleGAN approaches, with the average FID score decreasing by 4.61 (from 74.85 to 70.24). Swin Transformer classifier trained on enhanced data achieves 98.2% test accuracy, outperforming models trained on original or standard CycleGAN-generated data. The mobile solution, tested under 30 concurrent user requests, ensures stable field deployment, offering efficient, real-time disease diagnosis for farmers.