Decisions in agriculture are frequently based on weather. With an increase in the availability and affordability of off-the-shelf weather stations, farmers able to acquire localised weather information. However, with uncertainty in the sensor and installation quality, farmers are at risk of making poor decisions based on incorrect data. We present an automated approach to perform quality control on weather sensors. Our approach uses time-series modelling and data fusion with Bayesian principles to provide predictions with uncertainty quantification. These predictions and uncertainty are used to estimate the validity of a sensor observation. We test on temperature, wind, and humidity data and achieve error hit rates above 80% and false negative rates below 11%.

翻译：农业决策往往依赖于天气信息。随着现成气象站的可获得性和经济性不断提高，农民能够获取本地化的天气数据。然而，由于传感器和安装质量存在不确定性，农民可能基于错误数据做出不当决策。我们提出了一种自动化方法，用于对气象传感器进行质量控制。该方法结合时间序列建模、数据融合与贝叶斯原理，提供带有不确定性量化的预测结果。这些预测及不确定性被用于评估传感器观测值的有效性。我们在温度、风速和湿度数据上进行了测试，错误命中率超过80%，假阴性率低于11%。