## 4 Discussion  
Hyperspectral and multispectral remote sensing bands provide abundant and uninterrupted information, enabling precise feature recognition and mapping. However, their  
Figure 5: Variation in wheat digital image indices with fertility period under different fertilization treatments. Figure 6: Variation in wheat digital image indices with fertility period under different fertilization treatments. use in field production has been limited due to the high cost of equipment, their complex operation, and high technological requirements. Since the early 21 st century, coinciding with the widespread use of digital cameras, research into crop growth monitoring using digital images has significantly increased. However, the use of handheld or stationary digital cameras has been limited by low image resolution and restricted indices in digital images, preventing this research from evolving into a self-contained system. On the other hand, UAV digital images have become increasingly popular for small-to-medium-scale crop monitoring in farmlands due to their straightforward operation, relatively low cost, and minimal technical requirements. The DJI MAVIC 2 Professional UAV was chosen for this study because of its widespread use in contemporary applications and its technical parameters and application effects that are ideally suited to meet the requirements of crop production monitoring in research areas. This study used UAV remote sensing to capture digital images of spring wheat during four crucial fertility stages. The commonly used digital image indices of the crop were analyzed to establish quantitative relationships between these indices and the physiological indicators of wheat during each fertility period. The study aimed to investigate the potential of UAV digital images for monitoring the growth of spring wheat under various fertilization treatments. Based on the correlation analysis results presented in Figure 7, the digital image indices GLA, R, G, INT, g, GRVI, MGRVI, RGBVI, EXG, and GRRI were found to have a higher frequency of significant correlation with physiological indices during the four key reproductive periods of wheat. Therefore, they are considered more suitable for characterizing the physiological indices of wheat. The indices of r and CIVE have a low correlation with physiological indicators across wheat reproductive periods and are not recommended for use in monitoring wheat physiological indicators. The same results have been found in other studies as well [16; 17]. EXG, MGRVI, and RGBVI consistently showed strong correlations with biomass and yield across all fertility stages of winter wheat [7]. The present study showed that these indices also exhibited significant correlations with biomass during the flowering period of spring wheat. However, their performance was not exceptional in other fertility periods. [PERSON] [18] found a significant correlation between the aboveground biomass of winter wheat during the pulling stage and several digital image indices, including R, G, B, GRRI, GBRI, RBRI, r, g, and b. However, in the present study, no significant correlation was found between the aboveground biomass at the jointing stage of spring wheat and any of the digital image indices. Both studies found a significant correlation between the digital image indices R, G, and GBRI and changes in SPAD. The results of this study indicate that digital image indices R, G, and B exhibit significant negative correlations with biomass at all fertility periods in spring wheat. Similar findings were observed in studies on rice, maize, and spring wheat [18; 19; 20]. [PERSON] [21] concluded that the digital image indices RGBVI and GLA were significantly correlated with the leaf area index (LAI) in spring wheat. Similarly, the present study found a significant positive correlation between the digital image index GLA and LAI in spring wheat at filling and mature stages. However, RGBVI did not exhibit a significant correlation with LAI.