Model prediction of chlorophyll and fresh biomass in cereal grasses based on aerial images

The purpose of the study was to characterize the 18 varieties of cereals (wheat, barley, triticale) and evaluation of a model to estimate the chlorophyll content and fresh biomass on the basis of aerial images. For this, from digital images taken with a drone (DJI Phantom) were extracted the spectral data in RGB system. On their basis, have been calculated the normalized values (rgb), and the values of the HSB system, and subsequently were been determined the indices INT, NDI and DGCI. The content of chlorophyll (Chl) correlated to a higher level with the DGCI index (r = 0.846, p<0.01, F = 40.21), and with fresh biomass (r = 0.772, p<0.01, F = 23.60). Analyzing various types of regression functions (polynomial functions of grade I, II or III, hyperbolic, or exponential functions) describing the relationship between DGCI and Chl, the correlation coefficient values were approximately equal (0.84) and the Sigf values being in all cases less than 0.001.