Mingxia Wang ^1* Ben Zhao ^2* Nan Jiang ^1* Huan Li ^1* Cai Jiumao ^3*

• ¹ Yellow River Conservancy Technical Institute, Zhengzhou, Henan Province, China
• ² Henan Agricultural University, Zhengzhou, Henan Province, China
• ³ Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Beijing, Henan Province, China

Rapid and non-destructive diagnosis of plant nitrogen (N) status is crucial for optimizing N management during the growth of summer maize. This study aimed to evaluate the effectiveness of continuous wavelet analysis (CWA) in estimating the nitrogen nutrition index (NNI), to determine the most suitable wavelet analysis method, and to identify the most sensitive wavelet features across the visible to near-infrared spectrum (325-1025 nm) for accurate NNI estimation.Field experiments were conducted across two sites (Kaifeng and Weishi) during the 2022 and 2023 growing seasons using four summer maize cultivars (XD20, ZD958, DH661, DH605) under varying N application rates (0, 80, 160, 240, 320 kg N ha -1 ). Canopy reflectance spectra and plant samples were collected from the V6 to V12 growth stages.Wavelet features for each spectral band were calculated across different scales using the CWA method, and their relationships with NNI, plant dry matter (PDM), and plant N concentration (PNC) were analyzed using four regression models. The results showed that NNI varied from 0.61 to 1.19 across different N treatments during the V6 to V12 stages, and the Mexican Hat wavelet was identified as the most suitable mother wavelet, achieving an R² value of 0.73 for NNI assessment. The wavelet features derived from the Mexican Hat wavelet were effective in estimating NNI, PDM, and PNC under varying N treatments, with the most sensitive wavelet features identified as 745 nm at scale 7 for NNI, 819 nm at scale 5 for PDM, and 581 nm at scale 6 for PNC using linear regression models. The direct and indirect methods for NNI estimation were compared using independent field datasets. Both methods proved valid for predicting NNI in summer maize, with relative root mean square errors of 10.8% for the direct method and 13.4% for the indirect method. The wavelet feature at 745 nm, scale 7, from the direct method (NNI = 0.14 WF(745 nm, 7) + 0.3) was found to be simpler and more accurate for NNI calculation. These findings provide new insights into the application of the CWA method for precise spectral estimation of plant N status in summer maize.