Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn

The efficiency of side-dressing, a more efficient of nitrogen application method than uniform application in either late Fall or early Spring, relies heavily on the capability of nitrogen deficiency detection on a sprayer. To determine the site-specific yield potential for corn, multi-spectral image analysis including aerial- and ground-based images has been used. Some acceptable calibration relationships between the multi-spectral reflectance and SPAD readings have been found from previous study. In sunny weather conditions there was a shadow in the image made by corn leaf itself. This research investigated the shadow effect on the image for detecting corn nitrogen deficiency based on corn canopy reflectance information. The results indicated that the reflectance of red channel in shadow area showed strong inverse correlation, so the vegetation index NDVI using red and NIR channels also showed strong correlation (R2 = 77) compared to the whole leaf and bright area. And the reflectance (green and red) and vegetation index(G_NDVI, NDVI, and ratio) in shadow area showed more consistent correlations than others using these image analysis methods.

► Corn nitrogen deficiency based on crop canopy reflectance information from G, R, and NIR bands of light spectra.
► The shadow effect in image on sunny weather condition (a shadow made by corn leave itself).
► Analyzing this shadow effect in the image.
► The efficiency of multi-spectral nitrogen deficiency sensor was improved.