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将U-Net神经网络应用于Puccinia sorghi-玉米病理系统

Katerina L Holan¹, Charles H White², Steven A Whitham¹

¹Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA 50014.

|January 28, 2024

概括

使用U-Net模型的机器学习可以准确量化由Puccinia sorghi引起的玉米叶病. 多样化的培训数据提高了模型性能,减少了对可靠植物疾病分析的注释需求.

科学领域:

植物病理学植物病理学
计算机视觉计算机视觉
机器学习机器学习

背景情况:

传统的植物疾病分析是缓慢和劳动密集的.
手动注释培训数据是植物病理学机器学习的一个瓶.

研究的目的:

开发和评估U-Net卷积神经网络模型,以量化Puccinia sorghi在玉米叶上的发生率.
评估培训数据集的大小和多样性对模型性能的影响.

主要方法:

使用U-Net卷积神经网络进行图像分析.
训练模型使用不同数量的随机选择和时间过程疾病数据.
评估模型性能,使用交叉与工会和F1分数的交叉.

主要成果:

随着训练数据的增加,模型性能有所改善,但速度正在下降.
多样化的培训数据集提高了模型的准确性,并减少了注释要求.
只有48个图像的模型实现了地面真实复制.

结论:

U-Nets可以准确量化植物疾病症状,解决植物病理学的现实挑战.
这种方法为手动疾病评估提供了更快,更可靠的替代方案.
优化训练数据策略可以显著提高机器学习模型的效率.

关键词:

普奇尼亚 sorghi 的意思.它们是Pucciniales.齐亚梅斯 (Zea mays) 是一种植物.玉米的常见生.菌类生的生是一种真菌.机器学习是机器学习.玉米玉米玉米是一种植物疾病表型化植物疾病表型化

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