Potential of RGB-Derived Vegetation Indices as an Alternative to NIR-Based Vegetation Indices to Monitor Nitrogen Status in Maize

Unmanned aerial vehicles (UAVs) are increasingly used for crop monitoring, but their widespread adoption is limited since they often rely on non-standard specialized cameras equipped with near-infrared (NIR) sensors. More affordable and scalable crop monitoring solutions would be enabled, however, if data could be collected using standard RGB sensors. We compared visible-band indices that incorporate blue spectral range (NDGBI and NDRBI) with traditional NIR-based indices (NDVI and GNDVI) for their effectiveness in monitoring maize growth and nitrogen status. UAV multispectral data capture at different maize growth stages was complemented by ground-based spectroradiometer measurements for calibration and validation. Various agronomic and yield variables (including cornstalk NO3–N content, grain yield, grain moisture, number of corncobs, and grain test weight) were recorded to link spectral responses with plant performance and nutritional status. The results show that the overall performance of the RGB-based approach was comparable to that of the NIR-based approach, with the visible-band indices proving to be highly sensitive to physiological stress, chlorophyll degradation, and nitrogen variability in maize. Our findings highlight the potential of the RGB-based indices to complement or even replace specialized NIR-based indices, providing a cost-effective, high-resolution tool for precision agriculture.