Normalized Difference Vegetation Index versus Dark Green Colour Index to estimate nitrogen status on bermudagrass hybrid and tall fescue

In recent years digital sensors have been successfully inte-grated on board Unmanned Aerial Vehicles (UAV) to assesscrop vigour, vegetation coverage, and to quantify the‘green-ness’of foliage as indirect measurements of crop nitrogenstatus. The classical approach of precision agriculture hasinvolved the use of multispectral sensors onboard UAVand the development of numerous vegetation indices asso-ciated with vegetation parameters, such as the mostly usedNormalized Difference Vegetation Index (NDVI). However, themain negative issue when dealing with multi and hyper-spectral reflectance measuring tools is their high cost andcomplexity from the operational point of view. As a low-costalternative, vegetation indices derived from Red Green Blue(RGB) cameras have been employed for remote-sensing assess-ment, providing data on different stress conditions and spe-cies. Digital images record information as amounts of RGB lightemitted for each pixel of the image; however, the intensity ofred and blue will often alter how green an image appears. Tosimplify the interpretation of digital colour data, recent studieshave suggested converting RGB values to the more intuitiveHue, Saturation, and Brightness (HSB) colour spectrum, andthen into a single measure of dark green colour, the DarkGreen Color Index (DGCI). In this study, NDVI acquired bya ground-based handheld crop sensor and by a multispectralcamera mounted on board a UAV has been compared withDGCI calculated from images taken with a commercial digitalcamera on board a UAV, trying to quantify the colour ofturfgrass that had received different nitrogen (N) rates. Theobjectives of the trial were to study an affordable easy-to-usetool evaluating the relationship among NDVI, DGCI and leafnitrogen content on turfgrass.