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

• Published in: Computers and electronics in agriculture Vol. 197; p. 106934
• Main Authors: Gao, Dehua, Qiao, Lang, An, Lulu, Sun, Hong, Li, MinZan, Zhao, Ruomei, Tang, Weijie, Song, Di
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.06.2022; Elsevier BV
• Subjects: Adaptive sampling; Chlorophyll; Corn; Diagnosis; Discrete Wavelet Transform; Hybrid preprocessing; Iterative methods; Leaf chlorophyll content; Maize; Model accuracy; Nutrition; Optimization; Photosynthesis; Preprocessing; Wavelengths; Wavelengths optimization; Maize; Wavelengths optimization; Leaf chlorophyll content; Hybrid preprocessing
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2022.106934

•LCC was diagnosed by spectral analysis for maize growth monitoring in the filed.•Hybrid preprocessing methods were used to overcome Multi-source interferences.•Wavelengths optimization was proposed to extract features for LCC modeling.•Maps of maize LCC were constructed to provide potentials for fertilization application. 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 multi-source 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.