Monitoring of Summer Maize Growth Status and Nitrogen Based on Field Characteristic Data and UAV Multispectral Technology

Accurate estimation of aboveground dry matter accumulation and plant nitrogen content in summer maize is essential for optimizing both yield and nitrogen-use efficiency. Exclusive reliance on two-dimensional multispectral imagery results in data saturation and elevated estimation errors. This study proposes an integrated approach utilizing UAV-based multispectral data, SPAD index, and plant height index, employing deep learning algorithms to develop a precise model for inferring aboveground dry matter accumulation and plant nitrogen content. A field experiment incorporating five nitrogen application levels (N0: 0 kg·ha−1; N1: 120 kg·ha−1; N2: 240 kg·ha−1; N3: 300 kg·ha−1; N4: 360 kg·ha−1) and four summer maize varieties was conducted in the Huanghuaihai region. The results demonstrated that the aboveground dry matter accumulation and plant nitrogen content of the four maize varieties consistently followed a critical nitrogen dilution curve (CNDC) pattern (R2 ≥ 0.88), yielding a unified CNDC model (Nc = 34.92 ± 0.64DM−0.35±0.01, R2 = 0.94). The random forest (RF) model demonstrated exceptional precision in predicting aboveground dry matter accumulation (R2 = 0.94, RMSE = 1.56 t ha−1) and plant nitrogen content (R2 = 0.92, RMSE = 1.98 g/kg). This method exhibits higher accuracy compared to using vegetation index alone for predicting aboveground dry matter (R2 = 0.92, RMSE = 1.56) and plant nitrogen concentration (R2 = 0.91, RMSE = 2.49). Its performance significantly surpassed that of the support vector machine (SVM) and partial least squares regression (PLSR) models. This study indicates that the incorporation of SPADi and plant height index enhances the accuracy of drone multispectral-based random forest inversion models for nitrogen concentration and aboveground dry matter accumulation in summer maize throughout its growth period. Furthermore, when combined with key nitrogen dilution curves, this approach enables non-destructive and precise detection of nitrogen status in summer maize, thereby providing a scientific basis for nitrogen management and yield prediction.