Fluororescence Sensor for Early Detection of Nitrogen Deficiency in Maize.

Among all plant nutrients used in agriculture, nitrogen (N) is the least efficient mainly due to its mobility in the soil. Maize is one of the most important crop for world food production and contribute largely on N fertilizers usage.  The possibility of real-time sensing to detect variability in N deficiency within a field would enable variable-rate application of inputs and potentially reduce crop production costs, environmental risks and increase crop yield. Potassium (K), when applied in the right manner, can also lead to higher yield and low cost production. Furthermore, K deficiency can lead to a misinterpretation of proximal sensors data for predicting N variability. Real-time fluorescence sensing technology is fairly new for nutrient variable rate practices, and studies has shown that it can be an increment of information towards better precision nutrient management. The objective of this study was to evaluate if fluorescence sensing platform can detect variability of N and K in crop canopy at early growth stages of maize (prior to V6). Research was conducted under greenhouse conditions and plants were grown in pots with silica sand and supplied with modified Hoagland solution with different N and K rates. Sensor readings were analyzed using ANOVA and Tukey’s HSD test to detect differences among nutrient rates, and linear regression were fitted to estimate the precision of sensor’s indexes. Results showed that all indexes were able to detect N variability prior to V3 growth stage when only N deficiency was present, but couldn’t identify K deficiency as the K treatments didn’t show variability in early stage. Coefficient of determination and root square mean errors were observed on the regression analysis to understand which indexes where the best predictors of N status in maize.