Preliminary Results on the Use of Dark Green Colour Index (DGCI) to Evaluate the Nutritional Status of Camelina Sativa (L.) Crantz

Nowadays, the use of digital radiometric sensors in agriculture is a booming sector. Their ability to acquire quantitative and qualitative data on crops, combined with their ever-increasing versatility, which has made it possible to upload them onto UAV (Unmanned Aerial Vehicle), has made these technologies increasingly used within the most innovative agricultural realities. The most widely used sensors in the field of Precision Agriculture (PA) are certainly the multi- and hyper-spectral, which, being able to acquire data even outside the visible spectrum, allow the algebraic combination of characteristic spectral bands for the definition of the Vegetation Indices (VIs), useful for highlighting some specific properties of the vegetation (biomass of the canopy, absorbed radiation, chlorophyll content, etc.). The Normalized Difference Vegetation Index (NDVI), in this sense, appears to be one of the most used VIs for monitoring the nutritional status of the crop, as it has proven to be very reliable and acquirable in a quickly way. On the other hand, to calculate this index, it is necessary to use a multispectral camera, which, in addition to its high cost, requires a certain specific know-how both in the acquisition and processing phases, which often make these instruments not very accessible to small and medium-sized farms. Some recent studies, however, have identified a possible low-cost alternative (1, 2) which involves the use of Red Green and Blue (RGB) images, that can be acquired by any normal camera. This technique is based on the conversion of the RGB values of each pixel into the Hue, Saturation and Brightness (HSB) values, in order to improve the quantification of the green of the image (2), to then calculate a new VIs, the Dark Green Colour Index (DGCI). Nevertheless, if this index is widely used on the turfgrass, its application on field crops is being explored on a limited number of crops. For this reason, this work aims to evaluate the correlation of this index with the NDVI on a crop of Camelina Sativa (L.) Crantz. Specifically, the values of the two indices acquired via UAV will be compared to be able to evaluate the two techniques already at an operational level.