  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%.
