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核桃叶棕斑病的精确分类方法,集成层次特征选择和动态多尺度卷积.

Yuting Wei1,2, Debin Zeng2, Liangfang Zheng2

  • 1College of Information Engineering, Tarim University, Alaer, China.

Frontiers in plant science
|October 20, 2025
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概括

一个新的CogFuse-MobileViT模型通过有效处理模糊的病变边缘和复杂的特征,改善了核桃叶棕色斑点疾病分级. 这一进步提高了智能农业对植物疾病诊断的精度.

关键词:
适应性的多尺度扩展卷积.棕色斑点病 (Ophiognomonia leptostyla) 是一种常见的疾病.疾病分类疾病分类.边缘特征 感知 感知层次化的特征选择特征选择.胡果和胡果是一个很好的选择.

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科学领域:

  • 植物病理学 植物病理学
  • 计算机视觉 计算机视觉
  • 智能农业 智能农业

背景情况:

  • 核桃叶棕色斑点,由*Ophiognomonia leptostyla*引起,是核桃种植的主要威胁.
  • 精确的植物疾病分类对于智能农业至关重要,但由于模糊的病变边缘和复杂的特征提取而受到挑战.

研究的目的:

  • 开发一种先进的模型,精确分类核桃叶棕斑病.
  • 解决当前疾病分类方法的局限性,特别是关于模糊的病变边缘和特征提取效率.

主要方法:

  • 提出了一种新的疾病分级方法,集成层次特征选择和自适应的多尺度扩展卷积.
  • 开发了CogFuse-MobileViT模型,其中包括三个关键模块:层次特征选模块 (HFSM),边缘特征聚焦模块 (ECFM) 和自适应多尺度扩展卷积融合模块 (AMSDIDCM).

主要成果:

  • 在测试组中,CogFuse-MobileViT模型实现了86.61%的准确性.
  • 与标准的MobileViTv3模型相比,显示了7.8个百分点的改进.
  • 在实验评估中明显优于其他主流疾病分类模型.

结论:

  • 在CogFuse-MobileViT模型有效地解决了模糊边缘和低效的特征提取在核桃叶棕色斑点疾病分级的挑战.
  • 为精确分类提供可靠的技术解决方案,并在智能农业中对智能植物疾病诊断具有实际价值.