Diagnosis of maize chlorophyll content based on hybrid preprocessing and wavelengths optimization

Leaf chlorophyll content (LCC) is one of important indicators for photosynthesis evaluation and crop nutrition diagnosis. For establishing an accuracy LCC diagnosis model, this study focused on reduce influences of multisource interferences which resulted from external noises and internal multi-compositions. A systematic strategy with pipelines methods of hybrid preprocessing and wavelengths optimization was proposed to overcome such complicated interferences. Maize datasets of canopy reflectance and LCC measurements were collected under six fertilization treatments and three replications in three growth stages (V6, V9, and V12) in 2019 summer maize. Hybrid preprocessing was proposed by cascading preprocessing methods of discrete wavelet transformation (DWT) smoothing, multiplicative scatter correction (MSC), second derivative (D2). Optimization of sensitive wavelength was conducted to reduce the influences of multi-components overlapping. Methods of competitive adaptive reweighted sampling (CARS), iterative retaining informative variable (IRIV), and variable iterative space shrinkage space (VISSA) combined with partial least square (PLS) were used to explore and validate spectral responses traits of multi-components overlapping. The results of VISSA-PLS showed superior potentials for diagnose maize LCC with accuracy of RP2 of 0.76 and RMSEP of 2.45. The modeling results indicated that the systematic solution of DWT-MSC-D2-VISSA-PLS could improve the performance of LCC diagnosis. It provided application potentials for accurate diagnosis of maize growth status and lay foundations for topdressing recommendations.