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克利维亚生物传感器:基于电生理学信号的土壤水分识别,具有深度学习

Ji Qi¹, Chenrui Liu², Qiuping Wang²

¹School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, China; College of Mechatronics, Changchun Polytechnic, Changchun, 130033, China.

Biosensors & bioelectronics

|June 27, 2024

概括

这项研究表明,植物可以通过电信号检测土壤水分来充当生物传感器. 一个新的轻量级AI模型,PlantNet,准确地分类这些信号,为先进的环境监测铺平了道路.

科学领域:

植物电生理学植物电生理学
环境生物传感环境生物传感
机器学习用于生物系统.

背景情况:

植物在响应环境变化时产生电信号,表明作为生物传感器的潜力.
在将工厂的电信号与特定的环境数据相关联时存在挑战,特别是土壤湿度梯度.
现有的信号分类方法是计算密集型的.

研究的目的:

在不同的土壤湿度条件下记录和创建clivia电信号数据集.
开发和评估一种轻量级的卷积神经网络 (CNN),用于分类工厂的电信号.
推进植物生物传感器的开发,用于环境监测.

主要方法:

记录了受不同土壤湿度梯度影响的clivia植物的电信号.
编制了这些电信号的数据集,用于机器学习.
开发并训练了一种新的轻量级CNN模型,命名为PlantNet,用于信号分类.

主要成果:

植物网模型实现了高分类准确度 (99.26%),精度 (99.31%),回忆 (92.26%) 和F1得分 (99.21%).
与传统的CNN相比,PlantNet表现出卓越的性能,计算资源消耗显著降低 (0.17M参数,7.17MB大小,14.66M FLOP).
成功创建了一个强大的数据集,用于在土壤湿度梯度下对工厂电信号进行分类.

结论:

这项研究证实了植物作为有效的生物传感器来检测土壤水分的潜力.
开发的轻量级CNN模型为分类植物生物传感器信号提供了有效的解决方案.
这些发现为扩展植物生物传感器以检测其他环境因素,如污染物 (臭氧,PM2.5,VOCs) 提供了基础.

关键词:

生物传感器是一种生物传感器.深度学习是一种深度学习.轻量级网络轻量级的网络.工厂电信号分类工厂电信号分类植物电生理学植物电生理学

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