Prediction of maize chlorophyll nitrogen content using visible and near-infrared spectroscopy

Abstract

Chlorophyll quantification in summer maize (Zea mays L.) leaves is the focus of this research, specifically at the jointing phenological stage. Based on field experiments and the correlation between canopy spectral characteristics and chlorophyll during typical growth stages, the spectral reflectance of corn leaf samples was determined using an ASD FieldSpec Pro spectrometer with a wavelength range of 350-2500 nm. Variations in spectral reflectance patterns were examined across different chlorophyll concentrations. The reflectance spectra underwent Savitzky-Golay 9-point smoothing, followed by preprocessing with MSC and SNV. Subsequently, first-derivative, second-derivative, and reciprocal logarithmic transformations were applied. PLSR was employed to establish optimal spectral estimation models for chlorophyll. The results provide a theoretical foundation and technical guidance for non-destructive crop growth monitoring and precision nitrogen management. Reflectance spectra processed with Savitzky-Golay 9-point smoothing combined with different transformations significantly improved the signal-to-noise ratio. Derivative transformations enhanced the correlation between spectral data and corn leaf chlorophyll content. Using highly correlated combination bands substantially improved model stability and predictive capability. For PLSR models, the optimal approach involved MSC processing of smoothed spectra followed by second-derivative transformation, achieving Rc²=0.9799, RMSEC=3.3027, and SEC=3.3225. The prediction models developed using various analytical approaches exhibited robust consistency and precise performance, facilitating efficient chlorophyll level assessment in large-scale maize fields.

Bangladesh J. Bot. 54(3): 875-883, 2025 (September) Special