Canopy imaging is a good phenotyping approach to non-invasively quantify parameters such as canopy size, stress symptoms, and pigment concentrations. Unlike destructive measurements, canopy imaging is fast and easy. However, analysis of the images can be time consuming. To facilitate large-scale use of imaging, the cost of imaging systems needs to be reduced and the analysis needs to be automated. We developed low-cost imaging systems using a Raspberry Pi microcomputer, equipped with a monochrome camera and filter, at a total hardware cost of ~$500. The latest version of our imaging system takes images under blue, green, red, and infra-red light, as well as images of chlorophyll fluorescence. Images taken under red, green, and blue light can be combined to generate color images. Other colors of light can be easily added, if desired. The imaging system is easily implemented in controlled environment agriculture and can be adapted for use in field settings. We will demonstrate examples of simple imaging techniques and automated image analysis using the Python programing language. The multi-spectral imaging system generates normalized difference vegetative index (NDVI) and anthocyanin content index (ACI) images and histograms, providing quantitative, spatially-resolved information.