Quantifying spatial and temporal variability in plant stress has precision agriculture applications in controlling variable rate irrigation and variable rate nutrient application. One approach to plant stress detection is crop canopy temperature measurement by the use of thermographic or radiometric methods, generally in the long wave infrared (LWIR) wavelength range. A confounding factor in LWIR canopy temperature estimation is eliminating the effect of the soil background in the image. One approach to this is time series capture of canopy temperature using single point radiometric sensors, coupled with algorithms to estimate the influence of soil on the measurement. Another approach is imaging a crop canopy using a LWIR imager paired with a visible spectrum camera covering an overlapping field, whereby canopy temperature measurements can be compiled from infrared pixels while eliminating non-crop components from the field of view using reflectance data from the visible image. In this research we developed such a multi-sensor system utilizing a miniaturized LWIR camera paired with a RGB imager. This instrument was designed to have a low enough cost to be able to deploy multiple sensors throughout a field. It is capable of automatic data logging, creating multiple data points throughout a field for the purpose of identifying variability.