## 1 Introduction  
Remote sensing technology has advanced agricultural quantitative remote sensing, making it the predominant and precise method for assessing global plant growth. A mathematical model established relationships between image information derived from remote sensing sensors and specific plant parameters. Analyzing image characteristics facilitated the monitoring, diagnosis, and comprehension of plant growth status, enabling the accurate regulation and management of plant growth [1]. Several studies showed that crop quantitative remote sensing was effective in reflecting actual nitrogen levels and monitoring nitrogen across various cereal crops [2; 3; 4]. [PERSON] [5] used a digital image analysis tool for high-throughput screening of four large wheat populations to validate the relationship between digital image analysis and measures of the normalized difference vegetation index (NDVI), leaf area index, and light penetration through the crop canopy. Image processing techniques have immense potential to use characteristics like size, shape, color, and texture attributes from digital images to characterize agricultural produce [6]. Due to the ability of crop stems, leaves, and other aboveground organs to absorb, reflect, and refract visible light, with advantages of mobility, timeliness, ease of operation, and high resolution, light parameters such as color, hue, brightness, and saturation could enable a comprehensive characterization of plants, and digital image index made it possible to standardize spectral information from visible light bands, specifically red (R), green (G), and blue (B) [7; 8]. Unmanned aerial vehicles (UAVs) for remote sensing, with their advantages of mobility, timeliness, ease of operation, and high resolution, have recently shown significant advantages in extracting crop information and monitoring nutrient levels across small to medium spatial scales [9; 10; 11]. The traditional determination of crop physiological indicators and nutrient diagnosis required a large number of field samples and laboratory analysis, which was cumbersome and time-consuming. With the development of digital image technology, some researchers began to apply digital image processing technology for nutrient diagnosis [12]. [PERSON] et al. [13] used a cell phone camera to obtain the canopy images of maize in different periods, extracted digital image indexes from the images, and analyzed the correlation between the digital image indexes and maize nitrogen nutrient indexes. Their experimental results showed that, in the six-leaf period of maize, the color parameter of the canopy image was significantly correlated with the maize SPAD value, indicating it could be used as a nitrogen diagnostic period. Moreover, B/(R + G + B) and G/(R + G + B) were significantly correlated with the leaf SPAD value, a traditional indicator of nitrogen diagnosis, and B/(R + G + B) was more sensitive, so it could be used as a color parameter indicator for the diagnosis of nitrogen nutrition in maize. This result suggested that it was feasible to use digital image technology for nitrogen nutrition diagnosis. In addition, [PERSON] et al. [14] used a digital camera to obtain the image data of rice canopy, extracted the image index and the actual nutrient index of rice for correlation analysis, and found that the two had a good correlation relationship, in which the correlation coefficient between NRI and leaf nitrogen content reached \(-\)0.65, indicating that the application of image technology could be fast and simple to realize the rapid monitoring of crop nutrient indexes. The Hetao region of Inner Mongolia is a significant center for spring wheat production in China. Research into the rapid, non-destructive, and cost-effective monitoring of spring wheat production using unmanned aerial vehicles (UAVs) is indispensable for this region's agricultural sector. This study conducted field trials on spring wheat with varying nitrogen levels and captured digital images using UAVs at different reproductive stages.