## 1 Introduction  
Nitrogen, being a crucial element for crop growth, can be accurately applied as fertilizer based on the nitrogen nutritional status during the optimal fertility period of the crop [1, 2, 3]. This approach aims to decrease agricultural expenses while enhancing crop production and quality. In the field of crop nitrogen nutrition diagnosis, the current methods involve either measuring soil inorganic nitrogen or conducting chemical analysis on plant samples. However, these methods are time-consuming, labor-intensive, and complicated. Both their practicality and their suitability for large-scale operations are questionable [4, 5]. Remote sensing has been extensively utilized in several parts of agricultural production since the 1970s due to its quick, non-destructive, macroscopic, and objective nature. A remote sensing nitrogen nutrition diagnosis technique utilizes remote sensing sensorsto capture crop canopy spectrum information. It enables the real-time diagnosis of crop nitrogen nutrition status via mathematical inversion of agronomic nitrogen indicators [6]. Satellite remote sensing is a useful method for monitoring and assessing agriculture [7]. It possesses the capability to assess crop development and nutritional condition across an extensive area. However, it is limited by its temporal and spatial resolutions. The majority of the satellite remote sensing techniques are not capable of capturing specific information regarding the phenotypic structure and texture of crops, and there is also a lack of support for real-time data access [8]. Unmanned aerial vehicle (UAV) remote sensing technology has experienced significant advancements in the field of agriculture and other applications when compared to satellite remote sensing. This is primarily due to its maneuverability, flexibility, real-time capabilities, and ease of operation [9]. UAV remote sensing not only fulfills the need for high-resolution data acquisition but also allows for the instant, non-destructive, and reliable acquisition of crop phenotypic information on a broad scale [10; 11]. Furthermore, it achieves these outcomes while minimizing the need for human labor and material resources. Scholars have conducted extensive research, both domestically and internationally, on the quantitative inversion of nitrogen, resulting in significant conclusions. The researchers utilized three different prediction methods to evaluate winter wheat plant nitrogen concentration (PNC) at booting, heading, flowering, filling, and the whole growth stage in the Guanzhong area from unmanned aerial vehicle (UAV) hyperspectral imagery [12]. The researchers utilized UAV spectral information and texture feature information to construct a \"map-spectrum\" fusion index and established a model for monitoring the nitrogen content of winter wheat leaves during the nodulation period, the earning period, and the grouting period by using a machine learning method, which significantly improved the prediction accuracy of the nitrogen content of winter wheat leaves [13]. The researchers proposed a novel approach combining feature selection based on improved grey wolf optimizer and feature fusion with projected gradient nonnegative matrix factorization and matrix cross recombination (PNMF-MCR) to realize the integrated monitoring of total nitrogen (TN) content in winter wheat and soil based on crop canopy hyperspectral data. The PNMF-MCR method was utilized to generate a feature matrix VF to further integrate the TN sensitive spectral information of crops and soil by fusing the winter wheat and soil TN sensitive wavelengths. The TN inversion results based on the fused feature matrix VF achieved a high accuracy for both winter wheat and soil simultaneously [14]. The study conducted regression experiments on winter wheat biomass using the random forest algorithm to determine the most effective method for estimating winter wheat biomass [15].