Estimation of canopy nitrogen nutrient status in lodging maize using unmanned aerial vehicles hyperspectral data

Rapid and non-destructive monitoring of the temporal dynamic changes in canopy nitrogen in lodging maize is essential to explore the influence on plant nutrient transport and yield loss. This study aims to monitor the canopy nitrogen status and lodging severity in maize using unmanned aerial vehicle (UAV) hyperspectral technology. The lodging maize experiments, involving different types, were conducted at the vegetative tasseling (VT) and reproductive milk (R3) stages. Agronomic traits and hyperspectral images were collected at 1, 7, 14, 21 and 28 days after lodging (DAL). Lodging intensity was quantified using canopy nitrogen concentration (CNC) and canopy nitrogen volumetric density (CNVD). Firstly, the variation in CNC and CNVD among different lodging types was analyzed, and the correlation between CNC, CNVD and the canopy hyperspectrum were assessed. Subsequently, the recursive feature elimination with cross-validation (RFECV) algorithm was used to screen wavelengths sensitive to CNC and CNVD. Next, the CNC and CNVD estimation models were developed using random forest regression (RFR) and gradient boosting regression (GBR) algorithms. Finally, the spatial distribution maps of CNC and CNVD in lodging maize were generated based on UAV images. The key findings were as follows: (1) CNVD exhibited more significant variation than CNC in different lodging types, with greater lodging severity corresponding to higher CNVD values; (2) the correlation between CNVD and canopy hyperspectrum was stronger than that of CNC; (3) there were 12 CNC-sensitive and 16 CNVD-sensitive wavelengths selected by RFECV algorithm; (4) the CNVD estimation model performed the best (R2 = 0.77, RMSE = 69.61 g/m3 in testing set) using the GBR algorithm. Therefore, the combination of feature selection with regression models effectively reduces hyperspectral data dimensionality. This enhances the estimation accuracy and computational efficiency for assessing canopy nitrogen nutrient status in lodging maize. •The variation in canopy nitrogen of maize on days after lodging (DAL) was analyzed.•A new canopy nitrogen volumetric density (CNVD) of lodging maize was constructed.•The variation in CNC and CNVD in maize among different lodging types was analyzed.•RFECV algorithm was used to select optimal wavelengths from canopy hyperspectrum.•The RFR and GBR algorithms were used to accurately estimate CNVD of lodging maize.