Scholarlink

<p>Accurate frost detection on leaf surfaces is critical for agricultural monitoring, yet existing methods struggle with segmentation errors caused by complex backgrounds (blurred, soil, weeds) and subtle frost-leaf texture differences. To address this, we propose MCGE-Frost, a multi-component gradient enhancement method that integrates color space analysis with gradient fusion theory. The algorithm extracts gradient features from individual color channels (HSV, Lab), applies adaptive weighting to enhance frost-leaf boundary contrast, and employs morphological filtering to suppress background noise. Experiments on leaf images demonstrate that MCGE-Frost achieves a total algorithmic error segmentation rate of 3.29%, significantly outperforming ExG (8.63%), OTSU (8.98%), and HSV (11.98%). The method reduces computational complexity by 40% compared to deep learning-based approaches while maintaining robustness across diverse backgrounds. MCGE-Frost achieves 0.8&#xa0;s/image processing on GPU-accelerated systems, balancing accuracy and efficiency for edge deployment. Additionally, it improves the intelligence of frost quantification with minor manual calibration. This advancement supports real-time frost monitoring in precision agriculture, providing actionable insights for frost protection and crop management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-component gradient enhancement for accurate frost detection and quantification on leaf surfaces

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月6日

查看全文
  • Huan Song,
  • Lijun Wang,
  • Yongguang Hu,
  • Jie Yang,
  • Jinxing Niu

摘要

Accurate frost detection on leaf surfaces is critical for agricultural monitoring, yet existing methods struggle with segmentation errors caused by complex backgrounds (blurred, soil, weeds) and subtle frost-leaf texture differences. To address this, we propose MCGE-Frost, a multi-component gradient enhancement method that integrates color space analysis with gradient fusion theory. The algorithm extracts gradient features from individual color channels (HSV, Lab), applies adaptive weighting to enhance frost-leaf boundary contrast, and employs morphological filtering to suppress background noise. Experiments on leaf images demonstrate that MCGE-Frost achieves a total algorithmic error segmentation rate of 3.29%, significantly outperforming ExG (8.63%), OTSU (8.98%), and HSV (11.98%). The method reduces computational complexity by 40% compared to deep learning-based approaches while maintaining robustness across diverse backgrounds. MCGE-Frost achieves 0.8 s/image processing on GPU-accelerated systems, balancing accuracy and efficiency for edge deployment. Additionally, it improves the intelligence of frost quantification with minor manual calibration. This advancement supports real-time frost monitoring in precision agriculture, providing actionable insights for frost protection and crop management.