早期检测受黑暗影响的植物机械反应,使用增强的电信号
Hongping Li1,2, Nikou Fotouhi3, Fan Liu4
1College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong, 030600, Shanxi, China.
Plant methods
|March 27, 2024
概括
研究人员使用少量射击学习和数据增强解码了植物伤口信号,即使使用有限的数据,也实现了高精度. 这有助于通过分析电信号来提前检测作物中的压力.
科学领域:
- 植物电生理学 植物电生理学
- 机器学习在农业中的应用
- 信号处理 信号处理
背景情况:
- 机械工厂损坏会产生用于防御的电信号,这些信号会受到环境因素 (如光线) 的影响.
- 机器学习模型越来越多地用于通过电信号在作物中早期检测压力.
- 低通量数据收集限制了这些模型的训练数据,阻碍了性能.
研究的目的:
- 解码受伤诱导的电信号 (慢波潜力,SWPs) 来自不同光缺的植物.
- 为自动SWP识别和分类开发一个简单的学习工作流程.
- 提高机器学习模型的性能,以使用有限的电信号数据来检测工厂应激.
主要方法:
- 非侵入性电生理学,从植物中收集局部和远部SWPs.
- 使用SWP的整数和第一阶衍生品进行特征提取.
- 通过对抗式自编码器 (AAE) 结构进行数据增强.
- 使用随机森林进行分类,并进行几次射击学习.
主要成果:
- 实现了0.99分类准确性,无论是局部还是系统的SWP.
- 识别完整和一级衍生品作为SWP分类的有效特征.
- 与其他数据增强方法相比,拟议的AAE结构的表现优越.
结论:
- 建立了用于分类受伤诱导的电信号的新特性,区分受黑暗影响的反应.
- 提出了一种数据增强方法来生成虚拟工厂的电信号.
- 这些方法适用于开发使用电信号和处理小规模数据集的作物植物模型.
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