Site-specific variable-rate nitrogen application is one of the core operations in precision crop management. The
determination of an appropriate nitrogen application rate relies greatly on the capability of assessing crop nitrogen stress.
A machinery-mounted multispectral imaging sensor has been developed for real-time crop nitrogen deficiency detection on
the sprayer during fertilization operations. While field tests indicated that this image-based sensor was capable of detecting
crop nitrogen deficiency “on the go,” the test results also showed that this sensor was very sensitive to ambient light changes
and needed a considerably long image processing time to extract crop nitrogen deficiency data. To solve these problems, the
research has developed a dynamic calibration method to compensate for ambient illumination variation on crop canopy reflectance,
an image segmentation algorithm to eliminate the soil background noise, and a correlation model to estimate the
SPAD values from the calibrated multispectral crop canopy reflectance. Field validation tests demonstrated that the developed
sensor calibration and image processing algorithms improved the performance of the multispectral sensor on detecting
corn nitrogen stress. Using the modified sensor resulted in a reasonable correlation between the estimated and measured
SPAD values (R2 > 0.72). This research confirmed that it is technically feasible to design a machinery-mounted multispectral
imaging sensor to detect crop nitrogen stress reliably and accurately.