## 2 Materials and Methods
### Image Processing for Extracting Twelve Vegetation Indices
#### 2.3.1 Vegetation Indices  
In agricultural applications, twelve commonly used vegetation indices (VIs) are instrumental in assessing vegetation health and performance. These indices encompass a range of parameters and include: (1) Chlorophyll Index green (Clgr), (2) Chlorophyll Index red edge (Clre), (3) Enhanced Vegetation Index 2 (EVI2), (4) Green Normalized Difference Vegetation Index (GNDVI), (5) Modified Chlorophyll Absorption Ratio Index 2 (MCRI2), (6) Modified Triangular Vegetation Index 2 (MTVI2), (7) Normalized Difference Red Edge (NDRE), (8) Normalized Difference Vegetation Index (NDVI), (9) Normalized Difference Water Index (NDWI), (10) Optimized Soil-Adjusted Vegetation Index (OSAVI), (11) Renormalized Difference Vegetation Index (RDVI), and (12) Red-Green-Blue Vegetation Index (RGBVI), as shown in Table 3.  
To mitigate the influence of soil surface on each VI image, a soil mask layer is generated using the soil-adjusted vegetation index. The plots are defined by 270 ROIs, each measuring  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vegetation** & **Formulation** & **Reference** \\ \hline Clgr & \(\frac{NIR}{Green}\ -1\) & [35] \\ Clre & \(\frac{NIR}{RedEdge}\ -1\) & [35] \\ EVI2 & \(\frac{2S\times(NIR-Ref)}{1+NIR+(24\times Red)}\) & [36] \\ GNDVI & \(\frac{NIR-Green}{NIR+Green}\) & [37] \\ MCARI2 & \(\frac{1S\times(|2S\times(NIR-Ref)|-(13\times(NIR-Green))|}{\sqrt{(2\times N +IR+1)^{2}}-(6\times NIR-5\times Wed)-0.5}\) & [38] \\ MTVI2 & \(\frac{1S\times(|12\times(NIR-Green))-(2S\times(Red-Green))|}{\sqrt{(2\times N +IR+1)^{2}}-(6\times NIR-5\times Wed)-0.5}\) & [38] \\ NDRE & \(\frac{NIR-RefEdge}{NIR+RedEdge}\) & [39] \\ NDVI & \(\frac{NIR-Red}{NIR+Red}\) & [40] \\ NDWI & \(\frac{Green-NIR}{Green+NIR}\) & [41] \\ OSAVI & \(\frac{NIR-Red}{NIR+Red+0.16}\) & [42] \\ RDVI & \(\frac{NIR-Red}{\sqrt{(N1R+Rad)}}\) & [43] \\ RGBVI & \(\frac{Green^{2}-(Blue\times Red)}{Green^{2}+(Blue\times Red)}\) & [44] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vegetation indices formulations and references.  
Figure 2: Image processing pipelines for digital traits’ extraction adapted from [34]. From the multispectral (MS) images, twelve vegetation indices were generated. On the other hand, from the red, green, blue (RGB) images, twelve canopy volumes were obtained.  
4.5 \(\times\) 4.5 m, and are digitized in a shapefile (*.shp) format using the open-source software _Quantum GIS_ (_QGIS_, version 3.28.3-Firenze).  
Ground reference data obtained from the harvested area are utilized to estimate the canopy volume (CV) based on the subplot polygons. Subsequently, the plot segmentation shapefiles are imported into the developed algorithm. Various image features, including maximum (Max), average (Mean), sum, standard deviation, 95 th percentile, 90 th percentile, and 85 th percentile (representing the highest data values extracted after eliminating 5%, 10%, and 15% of the data following the soil subtraction process from the VI images, respectively, denoted as 95P, 90P, and 85P), are computed for each subplot within each VI image. These features are calculated using the Python libraries _NumPy_ and _Rasterstats_. During the plot segmentation creation process, the feature data are labeled and subsequently exported as a Comma-Separated Values (CSV) file.