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The development of small unmanned aerial vehicles (UAVs) and advancements in sensors technology made consumer 17 digital cameras suitable for the remote sensing of vegetation. In this context, monitoring the in-field variability of maize 18 (Zea mays L.), characterized by high nitrogen fertilization rates, with a low-cost color-infrared airborne system could be 19 the basis for a site-specific nitrogen (N) fertilization support system. An experimental field with different N treatments 20 applied to silage maize was monitored during the years 2014 and 2015. Images of the field and reference destructive 21 measurements of above ground biomass (AGB), N concentration in AGB and N uptake were taken at V6 and V9 22 development stages. Classical normalized difference indices and the indices adjusted by crop ground cover were 23 calculated and regressed against the measured variables. Finally, image colorgrams were used to build PLS regression 24 models to explore the potential of band-related information in variable estimation. The best predictors were found to be 25 the ground cover and the adjusted GNDVI: regression equation at V9 resulted in R 2 of 0.7 and RRMSE<25% in 26 external validation. Colorgrams did not improve prediction performances due to the spectral limitations of the camera. 27 Therefore, the feasibility of the method should be tested in future research. In spite of limitations of sensor setup, the 28 modified camera was able to estimate maize AGB due to the very high spatial resolution. Since AGB is a robust proxy 29 of N status, the modified camera could be a promising tool for a low-cost N fertilization support system. 30