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使用显微镜图像进行番茄生物和非生物应激分类的非编码深度学习模型.

Manoj Choudhary1,2,3, Sruthi Sentil1, Jeffrey B Jones1

  • 1North Florida Research and Education Center, University of Florida, Quincy, FL, United States.

Frontiers in plant science
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概括

本研究介绍了非编码深度学习 (NCDL) 平台,用于使用显微镜图像对番茄植物疾病进行分类. NCDL平台实现了高精度,简化了疾病诊断,并可能有助于管理系统.

关键词:
无生物压力是无生物压力.生物压力是生物压力.没有代码的模型.深度学习是一种深度学习.疾病疾病的疾病疾病的疾病.机器学习是机器学习.显微镜图像中的微观图像.在这里,我们可以看到茄,番茄,番茄.

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科学领域:

  • 植物病理学 植物病理学
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 准确的植物疾病分类传统上需要专家知识和实验室分析.
  • 对植物样本的显微镜检查对于诊断疾病至关重要.

研究的目的:

  • 评估非编码深度学习 (NCDL) 平台的有效性,用于使用显微镜图像对番茄植物疾病进行分类.
  • 评估各种NCDL平台在疾病症状分类方面的表现.

主要方法:

  • 利用诊断验证的番茄植物样本的显微镜图像 (×30).
  • 采用多个NCDL平台,包括亚马逊回认定制标签,Clarifai,可教机器,谷歌AutoML视觉,微软Azure定制视觉和果CreateML.
  • 进行外部验证,以评估模型的稳定性.

主要成果:

  • NCDL平台表现出高性能,平均F1得分在91.6%至98.5%之间.
  • 准确性在各个平台上各不相同,亚马逊Rekognition自定义标签达到99.8%,果CreateML达到87.3%.
  • 外部验证显示,大多数测试的NCDL平台的精度 (≤7%) 略有下降.

结论:

  • NCDL平台提供了一种可行的,准确和高效的方法,用于从显微镜图像中分类番茄植物疾病.
  • 这些模型可以支持用于疾病诊断和管理的移动/网络应用程序的开发.
  • 通过早期分类,NCDL可以提高诊断实验室样本处理的速度和效率.