## 4 Discussion  
Peanut is an important cash crop, with an important role in ensuring food supply and security. How to improve its yield per unit area has been a constant focus of peanut research. Traditional cultivation methods aiming to ensure peanut production and emergence rates have set excessive seeding densities, resulting in decreases in peanut yield. Seeding density is not only related to the physiological form of the plant, but also affects crop yield. Some studies have suggested that low-density peanut planting facilitates good growth and chlorophyll content, while high-density planting results in poor growth and a decrease in the overall photosynthetic performance of leaves [25]. In contrast, a low planting density creates a large light-receiving area per individual plant, which is conducive to photosynthesis, but the lower number of groups leads to greater light leakage, which is not conducive to use of light energy. Leaf chlorophyll content is an important indicator of plant nitrogen status. Changes in leaf chlorophyll lead to broad band differences in leaf reflectance and transmission spectra. However, the transition from the leaf to canopy spectrum is complex; canopy spectral reflectance is strongly affected by changes in chlorophyll concentration and by other factors (e.g., canopy architecture, soil background and LAI), making chlorophyll retrieval at the canopy level complicated and challenging. Hyperspectral data provide a large number of adjacent narrow-band leaf reflectivity. However, the lack of an effective means for analyzing hyperspectral information makes it difficult to conduct systematic quantitative analysis of hyperspectral indices on all possible two-band combinations (from 350 nm to 2500 nm), precise feature bands indicating biochemical components in plants might therefore not be fully explored and utilized [26]. Therefore, the development and optimization of techniques such as a spectral vegetation index that requires only limited data is of great significance and practical value in monitoring the physiological and ecological parameters of plants [27]. Studies have shown that the paired spectral vegetation index, a method that minimizes interference from LAI and background reflectance, can be used to estimate leaf chlorophyll concentrations [28]. Chlorophyll absorbs intense radiation in the VIS spectrum (400 nm to 700 nm) and is most obvious in chlorophyll \(a\) at wavelengths of 430 (B) and 660 (R) nm and chlorophyll \(b\) at wavelengths of 450 (B) and 650 (R) nm. In contrast, plants have higher reflectivity in the NIR region (700 nm to 1300 nm) due to the effects of leaf density and canopy structure. This sharp contrast between red and NIR reflectance behaviors in the spectrum is the motivation for developing spectral indices based on the ratio of reflectance values in the VIS and NIR regions [29]. The spectral vegetation index uses the characteristic shape of the green vegetation spectrum by combining the low reflectance in the VIS spectrum with the high reflectivity in the NIR spectrum. These combinations can be ratios of two or more bands, slopes or ratios of other formulae that minimize the effects of changes due to external factors and maximize sensitivity to variables of interest [30]. Therefore, the main purpose of the spectral vegetation index is to enhance information  
Figure 7: Pictures from (**A**) to (**D**) show the linear regression of vegetation indices NDSI, RSI, DSI, and SASI with chlorophyll content in peanut leaves respectively. contained in the spectral reflectance data by extracting changes caused by vegetation characteristics (such as LAI, vegetation coverage) and to minimize the effects of soil, atmosphere and solar sensors [1]. Our objectives were to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive estimation of chlorophyll in leaves with a wide range of pigment content and composition, using reflectance in a few broad spectral bands [19].