Predicting Nitrogen Efficiencies in Mature Maize with Parametric Models Employing In-Season Hyperspectral Imaging

Abstract

Overuse of nitrogen (N), an essential nutrient in food production systems, can lead to health issues and environmental degradation. Two parameters related to N efficiency, N Conversion Efficiency (NCE) and N Internal Efficiency (NIE), measure the amount of total biomass or grain produced, respectively, per unit of N in the plant. Utilizing remote sensing to improve these efficiency measures may positively impact the stewardship of agricultural N use in maize (Zea mays L.) production. We investigated in-season hyperspectral imaging for prediction of end-season whole-plant N concentration (pN), NCE, and NIE, using partial least squares regression (PLSR) models. Image data were collected at two mid-season growth stages (V16/V18 and R1/R2) from manned aircraft and unmanned aerial vehicles for three site years of 5 to 9 maize hybrids grown under 3 N treatments and 2 planting densities. PLSR models resulted in accurate predictions for pN at R6 (R2 = 0.73; R2 = 0.68) and NCE at R6 (R2 = 0.71; R2 = 0.73) from both imaging times. Additionally, the PLSR models based on the R1 images, the second imaging, accurately distinguished the highest and lowest ranked hybrids for pN and NCE across N rates. Neither timepoint resulted in accurate predictions for NIE. Genotype selection efficiency for end-season pN and NCE was increased through the use of the in-season PLSR imaging models, potentially benefiting early breeding screening methods.

Keywords

• nitrogen efficiency
• partial least squares regression
• machine learning
• hyperspectral remote sensing
• nitrogen conversion efficiency
• nitrogen internal efficiency