# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 2 Materials and Methods
### Vegetation Indices Calculation and Selection  
#### 2.2.1 Vegetation Indices of Apple Diseases and Pests  
Vegetation indices, as indices for analyzing crop growth in agricultural remote sensing, can effectively analyze crop growth and provide accurate data support for agricultural production. The vegetation information on remote sensing images is mainly reflected by the spectral characteristics of and spectral differences in vegetation canopy leaves, and there is a certain correlation between the spectra of different bands and different elements or characteristic states of vegetation. Using multispectral remote sensing data, through linear or nonlinearity combination operations, some values that have certain indicative significance for vegetation growth and biomass are generated, which is the vegetation index. The visible light spectrum is controlled by the chlorophyll content of the leaves, the short-wave near-infrared spectrum is controlled by the intracellular structure of the leaves, and the medium-wave near-infrared spectrum is controlled by the water content in the leaves. Green light is effective for distinguishing plant categories, while red light is effective for assessing vegetation coverage and plant growth conditions. Healthy green vegetation has a large difference in reflectance between the near-infrared spectrum \(R_{nir}\), and the red spectrum \(R_{red}\), because \(R_{red}\) is strongly absorbed by green plants, while \(R_{nir}\) exhibits high reflection and high transmission. When diseases and pests occur, bacteria and pests have varying degrees of influence on the changes in the chlorophyll, biomass, cellulose, and protein content in the invaded parts of the vegetation, which further affects the spectral information observable in the canopy of apple trees. Therefore, a total of 22 types of relevant vegetation indices, such as NDVI, GNDVI, RDVI, TNDVI, SR, and MTVI2, which are sensitive to vegetation and background differentiation, and MSAVI, OSAVI, and DVI, which are related to various biomass calculations, were selected and calculated. The calculation formulas for each vegetation index are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Disease and Pest** & **Train Dataset** & **Val Dataset** \\ \hline Aphids & 6012 & 1503 \\ Alternaria & 600 & 150 \\ Mosaic & 432 & 108 \\ Brown spot & 6156 & 1539 \\ Gray spot & 1116 & 279 \\ \hline \end{tabular} Notes: This dataset is a multi-label dataset; thus, the number of images for each disease category may not be consistent with the total number of images. The distribution of RGB and multispectral images is consistent.  
\end{table}
Table 2: Apple diseases and pests canopy multispectral image dataset.  
\begin{table}
\begin{tabular}{c c c} \hline
**Index Name** & **Equation** & **Introduction** \\ \hline NDVI [37] & \(NDVI=\frac{R_{out}-R_{rad}}{R_{out}+R_{rad}}\) & Normalized Differential Vegetation Index: a commonly used index to measure vegetation growth and coverage \\ TNDVI [38] & \(TNDVI=SQRT(\frac{R_{out}-R_{rad}}{R_{out}+R_{rad}}+0.5)\) & Index: a modified NDVI more sensitive to soil brightness for arid and semi-arid regions. \\ GNDVI [39] & \(GNDVI=\frac{R_{out}-R_{gram}}{R_{out}+R_{gram}}\) & Green Normalized Difference Vegetation Index: an index emphasizing the green vegetation component, which is suitable for high-biomass areas \\ RENDVI [40] & \(RENDVI=\frac{R_{bridge}-R_{rad}}{R_{night}+R_{rad}}\) & Index: using red edge bands to be more sensitive to vegetation cover and chlorophyll content \\ \hline \end{tabular}
\end{table}
Table 3: Equations used for vegetation indices calculation.