Sensor technologies provide opportunities to increase the quality and quantity of soils data while introducing new techniques and tools for classrooms. Linear regression models were developed for organic carbon prediction using color data gathered with the Nix Pro™ for dry (R2 = 0.7978, MSPE = 0.0819), and moist soils (R2 = 0.7254, MSPE = 0.1536). A mobile application, the Soil Scanner app, was created to allow for a more soil science dedicated interface that would allow users to create their own database consisting of GPS location and soil color data gathered using the Nix Pro™. The final application produced results in multiple color systems, including Munsell, recorded GPS location, sample depth, moisture conditions, in-field or laboratory settings, and a photograph of the soil sample. All data could then be uploaded to an online database. The GPS location allows for easy integration of data into GIS mapping software for the spatial manipulation of soils data. The application was tested by generating GIS maps showing the gradient of soil color across two field surfaces. The Nix Pro™ color sensor functions as a successful teaching tool and, coupled with the Soil Scanner app, offers a new means of gathering and storing reliable soils data. There is added benefit to having a soil science application that can be updated to include further analysis methods, resulting in an ever growing soils database. A laboratory exercise was developed that introduced students in an entry level soils course to the importance of soil color and the methods used to determine soil color. Students were then asked to determine the color of three soil samples using the Nix Pro™ and the standard Munsell Color Chart before conducting simple statistical analysis and responding to a questionnaire. Responses indicate that the Nix Pro™ was the preferred method of color analysis and students felt the sensor to be a more reliable method than traditional color books
