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TRiP:一个基于转移学习的病现型识别平台,使用SENet和微服务.

Peisen Yuan1, Ye Xia1, Yongchao Tian1,2

  • 1College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China.

Frontiers in plant science
|February 8, 2024
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概括

准确的水疾病分类对于表型化至关重要. 这项研究引入了一个使用转移学习和SENet与注意力机制的新框架,在识别细菌病变和爆发等疾病时达到95.73%的准确性.

关键词:
这就是SENet的意义.机器学习作为服务微服务框架 微服务框架识别大米病的识别方式转移学习转移学习

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科学领域:

  • 农业科学 农业科学
  • 计算机科学 计算机科学
  • 植物病理学 植物病理学

背景情况:

  • 准确的水疾病分类对于作物表型和管理至关重要.
  • 像细菌炎,爆发,棕色斑点,叶子污垢和tungro等疾病之间的高表型相似性 presents一个重要的识别挑战.

研究的目的:

  • 开发一个有效的框架来识别病现型.
  • 提高识别各种水疾病的准确性和效率.

主要方法:

  • 通过优化为SENet网络预先训练的参数,利用转移学习.
  • 在SENet中整合了注意力机制,以增强功能提取.
  • 开发了一个基于云的平台,使用微服务架构作为一种服务来识别疾病.

主要成果:

  • 在大米疾病分类中达到0.9573的高精度.
  • 证明了拟议框架在捕捉疾病特征方面的有效性.
  • 成功部署了一个功能云平台,用于可访问的病现型识别.

结论:

  • 拟议的框架有效地解决了分类表型相似的病的挑战.
  • 转移学习和注意力机制的整合大大提高了识别准确性.
  • 基于云的微服务平台提供了一个可扩展和用户友好的解决方案,用于识别病.