一个多功能,机器学习增强的射频光谱传感器,用于开发智能农业中干部水化监测系统
Oumaima Afif1, Leonardo Franceschelli1, Eleonora Iaccheri2,3
1Department of Electrical, Electronic and Information Engineering, Guglielmo Marconi-University of Bologna, Via Dell'Università, 50, 47521 Cesena, Italy.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
一个新的微波传感系统使用散射参数 (S参数) 来进行非侵入式监测. 这种紧的电池驱动设备集成机器学习,用于实时检测环境变量,如木材水化.
科学领域:
- 微波工程 微波工程
- 传感器技术 传感器技术
- 智能农业 智能农业
背景情况:
- 自动无线电频率 (RF) 分散参数采集对于非侵入性监测至关重要.
- 现有的系统往往缺乏紧性和独立的实地部署能力.
- 计算平台的整合增强了数据处理和分析潜力.
研究的目的:
- 开发一个独立的,紧的微波传感系统,用于自动获取S参数.
- 允许对外部物质和环境变量进行非侵入性监测.
- 通过嵌入式机器学习来证明系统对实时分析的能力.
主要方法:
- 纳米VNA和树派零W的集成用于S参数 (50kHz-4.4GHz) 记录.
- 实现由单个电池供电的双重录制模式 (手动和自动).
- 在基于Linux的系统架构中嵌入机器学习算法.
主要成果:
- 成功开发了一种紧的,电池驱动的微波传感系统.
- 证明了自动化S参数数据收集的能力.
- 通过RF补丁天线和ML分析验证了系统在绿木水化检测方面的潜力.
结论:
- 开发的系统为RF分散参数采集提供了灵活和便携式的解决方案.
- 嵌入式ML算法可促进环境变量的自动化实时分析.
- 该系统在智能农业和非侵入性材料监测领域的应用方面显示出显著的前景.
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