Hand-held chlorophyll meters or leaf-clip-type sensors indirectly and instantaneously measure leaf N content. They can provide an N nutrition index (NNI) value that is crucial information for adjusting the amount of N fertilizer to the actual N status of the plant. Although these measurements are non-invasive and non-destructive, they require numerous repetitions at the canopy scale. The objective of this work was to explore the potential of visible images to predict nitrogen status in winter wheat crops from estimating NNI and to compare these results with those deduced from classical methods. Based on a dark green colour index (DGCI), which combines hue, saturation and brightness, a normalized DGCI (nDGCI) was proposed as the ratio between the measurements of the study microplot and those of the over-fertilized microplot. The methodology was performed on winter wheat microplots with a nitrogen gradient. Half of the microplots were grown with a single cultivar (LG Absalon) and the other half with a mixture of four wheat cultivars. The impact of optical device (digital camera or smartphone), the white balance (Manual or Automatic), the crop growth stage (two-nodes or heading) and cultivars (single or mixed) on the relationship between (DGCI, nDGCI) and NNI was evaluated. The results showed a close correlation between the nDGCI values and the NNI_NTester values, especially on a single cultivar (LG Absalon; R2 = 0.73 up to 0.91 with smartphone). It suggested that the relationship is highly sensitive to the wheat cultivar. This approach with no specific calibration of images is promising for the estimation of N requirements in wheat field.