Advancing nitrogen nutrition index estimation in summer maize using continuous wavelet transform.

Area of Science:

• Agricultural Science
• Plant Physiology
• Remote Sensing

Background:

• Optimizing nitrogen (N) management in summer maize requires rapid, non-destructive methods for assessing plant N status.
• The nitrogen nutrition index (NNI) is a key indicator of plant N status.

Purpose of the Study:

• To evaluate continuous wavelet analysis (CWA) for estimating NNI in summer maize.
• To identify the optimal wavelet method and sensitive spectral features for NNI estimation.
• To compare direct and indirect NNI estimation methods using spectral data.

Main Methods:

• Field experiments with varying N rates across multiple sites and growing seasons.
• Collection of canopy reflectance spectra and plant samples during key growth stages (V6-V12).
• Application of CWA to spectral data to derive wavelet features and analyze relationships with NNI, plant dry matter (PDM), and plant N concentration (PNC) using regression models.

Main Results:

• The Mexican Hat wavelet was the most suitable mother wavelet for NNI assessment (R² = 0.73).
• Wavelet features effectively estimated NNI, PDM, and PNC.
• The most sensitive features were identified at 745 nm (scale 7) for NNI, 819 nm (scale 5) for PDM, and 581 nm (scale 6) for PNC.
• Both direct and indirect NNI estimation methods were valid, with the direct method showing slightly better accuracy (RMSE = 10.8%).

Conclusions:

• CWA, particularly using the Mexican Hat wavelet, is a powerful tool for precise spectral estimation of summer maize N status.
• The identified wavelet feature at 745 nm (scale 7) provides a simple and accurate approach for NNI calculation.
• Findings support improved N management strategies for summer maize.