The development of Machine Learning methods to correct data responses from low-cost sensors to improve agricultural productivity and air quality data accuracy.

开发机器学习方法来纠正低成本传感器的数据响应，以提高农业生产力和空气质量数据的准确性。

• 批准号: 10081002
• 金额: $ 6.24万
• 依托单位: WE CARE 4 AIR LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10081002
• 关键词: development; Machine; Learning; methods; correct

This project is to develop machine learning based methods to normalise the responses of electrochemical and solid state gas sensors used in air quality monitoring for transport, health and agriculture.The proposed models use a wide range of environmental parameters and reference grade trace gas analysers as well as the responses of the electrochemical and solid state sensors as the training sets.The intended result is low cost gas sensors that will report data that with smaller uncertainties. Therefore better data at lower cost.

该项目是开发基于机器学习的方法，以使用于运输，健康和农业的空气质量监控的电化学和固态气体传感器的响应归一化。拟议的模型使用广泛的环境参数，以及参考级别的痕量气体分析仪，以及训练集的电化学和固态传感器的响应。因此，以较低的成本数据更好的数据。