# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 2 Materials and Methods
### Vegetation Indices Calculation and Selection
#### 2.2.2 Vegetation Indices Selection Algorithm
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Index Name** & **Equation** & **Introduction** \\ \hline MSAVI2 [41] & \(MSAVI2=\frac{1}{2}(2R_{nir}+1-SQRT((2R_{nir}+1)^{2}-8(R_{nir}-R_{nod})))\) & Modified Soil-Adjusted Vegetation Index 2: a vegetation index to reduce the influence of soil background, which is suitable for bare soil and vegetation mixed areas \\ RVI [42] & \(RVI=\frac{R_{nod}}{R_{nun}}\) & Vegetation Index: the ratio of vegetation reflectance to soil reflectance, reflecting the degree of vegetation density \\ DVI [43] & \(DVI=R_{nir}-R_{red}\) & Differential Vegetation Index: simple vegetation and non-vegetation reflectance differences for rapid assessment of vegetation cover \\ GDVI [44] & \(DVI=R_{nir}-R_{green}\) & Global Differential Vegetation Index: considering differences in multiple bands for the assessment of complex vegetation environments \\ GRVI [44] & \(GRVI=\frac{R_{nir}}{R_{pwn}}\) & Green-Red Vegetation Index: using the ratio of green and red bands to reflect chlorophyll content \\ WDRVI [45] & \(WDRVI=\frac{aR_{nir}-R_{red}}{aR_{nir}+R_{red}}\) & Weighted Differential Vegetation Index: adjusting NDVI by weighting to be more sensitive to high vegetation cover areas \\ MSAVI [41] & \(\frac{MSAVI=\frac{R_{nir}-R_{nod}}{R_{nir}+R_{red}}(1+L)L}{L=1-2\alpha\times NDVI \times WDVI\)\(WDVI=R_{nir}-aR_{real}}\) & Modified Soil-Adjusted Vegetation Index: an improved version to reduce the influence of soil background on vegetation index \\ OSAVI [46] & \(OSAVI=\frac{R_{nir}-R_{nod}}{R_{nir}+R_{red}+0.16}\) & Optimizing Soil Adjustment Vegetation Index: further optimizing the correction of soil background by introducing soil factors \\ GOSAVI [47] & \(GOSAVI=\frac{R_{nir}-R_{green}}{R_{nir}+R_{green}+0.16}\) & Global Optimizing Soil Adjustment Vegetation Index: an improved version of OSAVI considering the effects of multiple bands to improve accuracy \\ NDRE [48] & \(NDRE=\frac{R_{nir}-R_{nod}}{R_{nir}+R_{nod}}\) & Normalization Differential Red Edge Index: using red edge band, sensitive to vegetation structure and health status \\ SR [49] & \(SR=\frac{R_{nir}}{R_{nod}}\) & Structure Ratio Index: using to reflect complex \\ NLI [50] & \(NLI=\frac{R_{nir}^{2}-R_{nod}}{R_{nir}^{2}+R_{nod}}\) & Normalization of Leaf Canopy Index: using to assess vegetation canopy health and chlorophyll content \\ RDVI [51] & \(RDVI=\sqrt{NDVI\times(R_{nir}-R_{nrad})}=\sqrt{\frac{(R_{nir}-R_{nrad})^{2}}{(R _{nir}+R_{nrad})}}\) & \(RDVI=\sqrt{NDVI\times(R_{nir}-R_{nrad})}=\sqrt{\frac{(R_{nir}-R_{nrad})^{2}}{(R _{nir}+R_{nrad})}}\) \\ MSR [52] & \(MSR=\frac{\frac{R_{nir}-1}{R_{nir}+1}}{\sqrt{\frac{R_{nir}}{R_{nir}+1}}+1}\) & Multispectral Ratios: comprehensive assessment of vegetation growth using ratios of multiple bands \\ NG [53] & \(NG=\frac{R_{nir}}{R_{nir}+R_{nrad}+R_{green}}\) & Normalized Greenness: a vegetation index emphasizing green band reflectance for assessing chlorophyll content \\ NR [53] & \(NR=\frac{R_{nid}}{R_{nir}+R_{nrad}+R_{green}}\) & Normalized Redness: a vegetation index based on red band reflectance that reflects the health status of vegetation \\ IPVI [54] & \(IPVI=\frac{R_{nir}}{R_{nir}+R_{red}}\) & Weighted Vegetation Index: using the infrared band to reflect the moisture content and health status of vegetation \\ MTVI2 [55] & \(MTVI2=\frac{15\times(12(R_{nir}-R_{pwn})-25(R_{nir}-R_{pwn}))}{\sqrt{(2R_{nir}+1) ^{2}-(6R_{nir}-5(R_{nir}-5(R_{nir})-0.5)}-0.5})\) & Modified Triangular Vegetation Index 2: vegetation index sensitive to chlorophyll content, suitable for high-biomass areas \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._vegetation indices when calculating vegetation index weights. RF-ML is proposed for multi-label tasks based on the Relieff and RReliefF [57] algorithms. Relieff is one of the commonly used feature selection algorithms. By measuring the distance between adjacent samples and the difference between features within a certain range, each feature is assigned a specific weight.