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一个边缘转移学习方法,用于校准土壤电导传感器
Yun-Wei Lin1, Yi-Bing Lin1,2,3,4,5,6, Ted C-Y Chang7
1College of Artificial Intelligence, National Yang Ming Chiao Tung University, Tainan 711, Taiwan.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
传感器Talk3使用边缘设备上的机器学习来重新校准智能农业中的电导率 (EC) 传感器. 这种方法显著提高了准确性,并使现场人工智能培训成为具有成本效益的农业智能.
科学领域:
- 农业技术 农业技术
- 机器学习 机器学习
- 传感器网络 传感器网络
背景情况:
- 电导率 (EC) 传感器对于智能农业至关重要,但会遭受漂移,需要重新校准.
- 现有的EC传感器校准方法通常依赖于标准传感器和基于云的处理,这可能是昂贵和低效的.
研究的目的:
- 开发一种高效的基于边缘的机器学习方法,用于在智能农业中重新校准EC传感器.
- 实现现场人工智能培训和转移EC传感器校准的学习,减少对云基础设施的依赖.
主要方法:
- 提出了SensorTalk3,一个由XGBOOST和Random Forest模型组成的组合,可在Raspberry Pi等边缘设备上执行.
- 集成的土壤温度和湿度传感器数据作为校准的关键特征.
- 开发了一种双传感器检测解决方案,用于确定重新校准需求.
主要成果:
- 传感器Talk3实现了平均绝对百分比误差 (MAPE) 低至1.738%,比原始传感器的7.792%误差有显著改善.
- 即使使用未校准的湿度和温度传感器 (误差≤8.3%),也可以实现精确的EC校准.
- 在边缘节点成功展示了现场人工智能培训和转移学习.
结论:
- 在智能农业中,SensorTalk3为EC传感器重新校准提供了具有成本效益和准确性的解决方案.
- 基于边缘的AI培训和转移学习代表了现场数据处理的重大进步.
- 拟议的方法通过提高传感器数据可靠性来提高农业智能.
关键词:
物联网 (IoT) 的物联网 (IoT) 的物联网.随机的森林 随机的森林在XGBOOST中使用XGBOOST.人工智能的人工智能是人工智能.电导率是指电导的电导率.农业传感器 农业传感器传感器校准 传感器校准更多相关视频
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