# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 2 Materials and Methods
### Modeling Methods  
Polynomial Regression (PR) is a special case of linear regression in which the dimensionality of a variable is converted from nonlinear to linear. This is a way of increasing the number of times an independent variable, and when we set the number (greater than 1) on an independent variable, we attain the result of projecting these data into a higher power space.  
Random Forest (RF) is a bagging ensemble method based on a set of decision trees [28]. The basic principle of random forest is to utilize the self-service method resampling technique to extract a plurality of self-service sample sets from the original sample and carry out decision tree modeling for each self-service sample set. Each node variable of the tree is generated. Node segmentation in a plurality of predictors is randomly selected, and the average value of all decision tree predictions is taken as the final prediction result. In  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vegetation Index** & **Computing Formula** & **Explanation** \\ \hline Normalized Difference Vegetation Index (NDVI) & \(\left(R_{i}-R_{j}\right)/\left(R_{i}+R_{j}\right)\) & \\ Difference Vegetation Index (DVI) & \(\left(R_{i}-R_{i}\right)\) & \(i,\ j\) are any band in the spectrum; \\ Ratio Vegetation Index (RVI) & \(\left(R_{i}/R_{j}\right)\) & \(R_{i},\ R_{j}\) are reflectivity \\ Renormalized Difference Vegetation Index (RDVI) & \(\left(R_{i}-R_{j}\right)/sqrt(R_{i}+R_{j})\) & \\ Green Chlorophyllll Index (GCI) & \(\left(R_{i}-R_{j}\right)-1\) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vegetation index formula.  
Figure 3: Correlation between spectral reflectance and chlorophyll.  
general, a random forest will randomly generate hundreds or thousands of classification trees and then select the tree with the highest repetition as the final result, which improves the prediction accuracy of the model by summarizing a large number of classification trees [29]. When using the random forest regression algorithm to construct the model, the overall error of different data sets can be balanced, and the operation performance can be improved by adjusting a few parameters, and the performance is higher.  
Decision Tree (DT) is a predictive model used to represent the mapping relationship between the attributes of entities and their corresponding value. Each node in the tree represents an object, and the path of each branch represents a possible property, with each leaf node corresponding to the path that this leaf node travels through. When constructing a decision tree, the characteristics to be divided are typically chosen based on the principle of maximizing purity after partitioning. Additionally, if it is a tree, pruning is often necessary to eliminate nodes that could result in increased validation errors, thus preventing overfitting [30].  
An Artificial Neural Network (ANN) is a computational model inspired by the human brain's function, designed to solve practical problems by mimicking the brain's information processing capabilities. It is an algorithmic model that can judge the problems of the human brain from the perspective of information processing. The ability to approximate and the information is used to complete the simplest abstract calculations [31]. The neural network is composed of an input layer, an output layer, and one or more hidden layers, which are given corresponding weights through the connection between neurons, and the weights are continuously adjusted through training and learning algorithms to ultimately reach an optimum.