## 2 Materials and Methods
### Imagery Processing for Deriving Vegetation Index Features  
Pix4D Mapper (Pix4D, Inc., Lausanne, Switzerland) was used to obtain five spectral orthomosaics (corresponding to blue, green, red, red edge, and NIR sensors on the imaging sensor) through a series of image stitching operations on acquired imagery snapshots. These operations include key point feature extraction and matching, image model opti  
Figure 1: (**a**) Characterization of the experimental area at Tidewater Agricultural Research and Extension Center in Sufffolk, VA. (**b**) Soybean experimental plots imaged using the aerial multispectral platform. (**c**) RGB natural color composition with study area bordered in red.  
mization, georectification, point cloud densification, radiometric calibration, meshing, and orthomosaic generation (Figure 2). Generated orthomosaics were then exported to QGIS for further processing to obtain 24 VIs listed in Table 1 through the \"Raster Calculator\" toolbar (Figure 2). These listed VIs were selected for their importance in highlighting crop health under a variety of agroclimatic conditions. Soil background was then removed from each VI layer using the histogram separation method [36,37]. Next, rectangular regions of interest (ROI) were created over two central rows of each plot and mean REF and VI features were extracted for further analysis (Figure 2).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Vegetation Index** & **Equation** & **Reference** \\ \hline Green Difference Vegetation Index (GDVI) & \(\text{NIR}-\text{G}\) & [27] \\ Enhanced Vegetation Index (EVI) & \(2.5\times(\text{NIR}-\text{R})/(\text{NIR}+6\times\text{R}-7.5\times\text{B}+1)\) & [6] \\ Infrared Percentage Vegetation Index (IPVI) & (NIR)/(NIR + R) & [26] \\ Green Normal Difference Vegetation Index (GNDVI) & \((\text{NIR}-\text{G})/(\text{NIR}+\text{G})\) & [18] \\ Normalized Difference Vegetation Index (NDVI) & \((\text{NIR}-\text{R})/(\text{NIR}+\text{R})\) & [18] \\ Wide Dynamic Range Vegetation Index (WDRVI) & \((\text{a}\times\text{NIR}-\text{R})/(\text{a}\times\text{NIR}+\text{R})\) & [26] \\ Simple Ratio (SR) & \(\text{NIR}/\text{R}\) & [6] \\ Modified Non-Linear Index (MNLI) & \((\text{NIR}^{2}-\text{R})\times(1+\text{L})/(\text{NIR}^{2}+\text{R}+\text{L})\) & [27] \\ Soil Adjusted Vegetation Index (SAVI) & \(1.5\times(\text{NIR}-\text{R})/(\text{NIR}+\text{R}+0.5)\) & [13] \\ Optimized Soil Adjusted Vegetation Index (OSAVI) & \((\text{NIR}-\text{R})/(\text{NIR}+\text{R}+0.16)\) & [15] \\ Green Soil Adjusted Vegetation Index (GSAVI) & \((\text{NIR}-\text{G})/(\text{NIR}+\text{G}+0.5)\) & [27] \\ Green Optimized Soil Adjusted Vegetation & \((\text{NIR}-\text{G})/(\text{NIR}+\text{G}+0.16)\) & [26] \\ Index (GOSAVI) & \((\text{2}\times\text{NIR}+1-\text{sqrt}\{(2\times\text{NIR}+1)\ ^{2}-8\times(\text{NIR}-\text{R}))}/2\) & [26] \\ Normalized Difference Red Edge Index (NDRE) & \((\text{NIR}-\text{RE})/(\text{NIR}+\text{RE})\) & [6] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multispectral imagery-derived vegetation indices for soybean yield evaluations.  
Figure 2: Flowchart showing soybean cyst Nematode and crop vigor evaluation; and estimation of soybean yield using aerial multispectral imagery and statistical and machine learning models.