Studies have shown that normalized difference vegetation index (NDVI) from ground-based active remote sensors is highly related with leaf N content in maize (Zea mays). Remotely sensed NDVI imagery can provide valuable information about in-field N variability in maize and significant linear relationships between sensor NDVI and maize grain yield have been found suggesting that an N recommendation algorithm based on NDVI could optimize N application. Therefore, a study was conducted using the two most prominent ground-based active sensors (NTech’s GreenSeeker™ red and Holland Scientific’s Crop Circle™ amber) to develop an N recommendation algorithm for each sensor for use at the V12 maize growth stage. Each sensor’s NDVI N recommendation algorithm calculated unbiased N recommendations suggesting that the methodology of algorithm development was valid as was the estimate of required N at maize growth stage V12 and the algorithms developed for each sensor calculated very similar N recommendations. The integration of ground-based sensors and the appropriate N application algorithms into an on-the-go fertilizer application system would increase the spatial accuracy of N application on fields that are spatially variable if these algorithms are shown to be stable over time and space.