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

点击翻译按钮获取中文摘要

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.