## 3 Materials and Methods
### Data Preparation
#### 3.3.2 Feature Selection  
Feature selection is a process used to identify an optimal subset of characteristics, which increases the classification performance while reducing the complexity and the amount of computing needed. In the current study, there were two models, among which DL selected the features automatically, but, in the case of ML, a methodology is required to choose the features, which increases the performance of the classification model [66]. In general, having a large number of input features might potentially result in poor performance; this is because the feature space grows. Moreover, it was noticed that our dataset included linked characteristics. As a result, two distinct methods of feature selection were tested in order to choose the superior features, which ultimately led to an increase in the accuracy of the ML model and a reduction in the amount of loss. **(A) Using domain expertise:** In a traditional classification system, domain experts use selected features for identification because, in the case of bamboo, many features are not able to be recorded or used for classification. For example, the flowering year ranges from 3 to 120 years, so, for a human being, it is not easy to record such features. Likewise, many other morphological features are not valid for identification; therefore, we used the recorded attributes that were suggested by the experts in the bamboo domain [67,68]. **(B) Using correlation:** The statistical method known as correlation analysis determines the degree to which a linear connection exists between two or more characteristics [69]. The correlation method was employed in this particular research for two main reasons: first, it is simple to execute; second, it was a good fit for our datasets, DS1 and DS2. Given two variables with '\(n\)' values each, \(X\) and \(Y\), we can calculate the correlation (\(r\)) using (1). \[r=\frac{Cov(X,Y)}{\sigma_{X}\cdot\sigma_{Y}} \tag{1}\]  
The correlation coefficient is \(r\), and the covariance is \(Cov(X,Y)\); \(\sigma_{X}\) and \(\sigma_{Y}\) are the standard deviations of variables \(X\) and \(Y\), respectively. Assume that \(X\) and \(Y\) are two sets of values with \([x_{1},x_{2},\cdots,x_{n}]\) and \([y_{1},y_{2},\cdots,y_{n}]\), respectively. We can determine the \(r\)-value by using (2):  
\[r=\frac{\sum_{i=1}^{n}(X_{i}-\bar{X})(Y_{i}-\bar{Y})}{\sqrt{\sum_{i=1}^{n}(X_{ i}-\bar{X})^{2}}\sqrt{\sum_{i=1}^{n}(Y_{i}-\bar{Y})^{2}}} \tag{2}\]  
where \(n\) denotes the sample, \(X_{i}\) and \(Y_{i}\) are the \(i\)th data values, and \(\bar{X}\), \(\bar{Y}\) are the mean values of \(X\) and \(Y\), respectively. The \(r\) value ranges from \(-1.0\) to \(+1.0\), and its parameter specifies two values. They are (1) the strength of the relationship, which determines how closely these sets are linked (the higher the value, the stronger the relationship), and (2) the direction of the relationship, which specifies that if one value rises in one set, another value rises in another set, or if one value falls in one set, another value falls in another set, respectively. In other words,this association is known as a positive correlation when they are both heading in the same direction. A negative correlation is referred to as \"converse\". A value of 0 indicates that there is no relationship. The relationship becomes stronger as the value approaches \(+1\), and this means a strong connection. Values around \(-1\) suggest a significant negative correlation [69; 70]. The three different forms of correlation are represented in Figure 5. The \(r\) values that indicate the degree of correlation between the characteristics and the labels were evaluated in this study. During our experimentation, we found that the features of our dataset had a significant degree of association with one another. **Normalization and Standardization:** In the ML model, each feature in our study belonged to various scales.