Canopy imaging is a non-invasive phenotyping approach to 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. The system uses a Python-based program to collect and analyze images automatically. The multi-spectral imaging system separates plant from background using the chlorophyll fluorescence image and generates normalized difference vegetation index (NDVI) and anthocyanin content index (ACI) images and histograms, providing quantitative, spatially-resolved information. We verified that these indexes correlate strongly with leaf chlorophyll and anthocyanin concentration. The low cost of the system can make this imaging technology widely available.