Characterization of a Smartphone Application to Predict Leaf N Concentration in Maize.

Rhezali A., L.C. Purcell, and T.L. Roberts. Dept. of Crop, Soil, and Environmental Sciences. University of Arkansas. Fayetteville, AR.

Abstract

Nitrogen (N) fertilization is essential in maximizing maize (Zea Mays L.) yield. Using N efficiently requires the adoption of precise and accurate methods that can help producers determine the correct rates of N fertilizer to be applied. The dark green color index (DGCI) technology might be an effective tool to estimate maize N status. The objective of this study was to characterize leaf N concentration with greenness measurements as determined by DGCI using a camera and a smartphone application (GreenIndex Spectrum Tech) and to determine the sources leading to the discrepancies if DGCI values of the app differ from those made using the camera. Measurements of DGCI were made under laboratory conditions and were compared with N concentration of the entire leaf blade. Field experiments were conducted at two sites during summer of 2013 with N rates ranging from 0 to 360 kg N ha^-1. Camera DGCI values and Leaf N were closely associated (r²=0.70**, 0.85**), but the relationship between GreenIndex DGCI and leaf N showed more variability (0.40*, 0.67**). It was hypothesized that variability between the GreenIndex DGCI and leaf N is because camera measurements of DGCI use the whole leaf while the app determines DGCI from only the center portion of the leaf. Nitrogen concentration increased from the bottom to the top of the maize leaf, which decreased the r² between GreenIndex DGCI and total leaf N. Results support the need to choose appropriate leaf portion by the GreenIndex DGCI technology to predict leaf N concentration in maize.