# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 3 Feature Extraction and Selection  
Machine learning algorithms require relevant and important input features to predict the output. However, not all features are equally applicable in a prediction task, and some may even generate noise in the model. Feature selection and feature extraction are two approaches to handle this. Feature selection is picking a subset of relevant features from the initial set of features. The goal is to minimize the dimensionality of the feature space, simplify the model, and increase its generalization performance. In addition, feature selection enables the proposed algorithm to train faster, reduce computational complexity, and minimize the overfitting of a model [51]. On the other hand, feature extraction is changing the original features into a new collection of more informative and compact features. The idea is to extract the most essential information from the original features and represent it in a lower-dimensional feature space. Combining two or more feature extraction techniques (shape, texture, color, venation, etc.) gives better classification results than a single-feature extraction technique [51; 52]. Figure 2 illustrates feature extraction and selection in machine learning with six phases. In phase 1, after raw data are collected from different sources, they will be preprocessed to select the target data we need to study. Afterward, feature extraction and selection processes are utilized in Phases 2 and 3 to extract and detect the most essential features. The preprocessing step is given in phase 4. This step is considered the second step in building a classification model. The collected data need to be preprocessed to ensure its quality. This involves handling missing values, dealing with outliers, and transforming the data into a format suitable for analysis. After we extract the best features of the leaf, we conduct a classification in phase 5 based on a trained classification algorithm (i.e., Convolutional Neural Network (CNN), Nearest Neighbor (KNN), Support Vector Machine (SVM), or Principal Components Analysis (PCA), etc. Using data mining techniques, the data are transformed into a structure that is suitable for interpretation and evaluation of the results in phase 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Literature Work**} & \multicolumn{3}{c}{**Proposed Work**} \\ \hline \multirow{2}{*}{**Reference**} & **\# of** & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Accuracy**} & **\# of** & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Accuracy**} \\  & **Classes** & & & & **Classes** & \\ \hline
[47] & 2 & CNN & 95\% & 2 &
\begin{tabular}{c} Multiple \\ algorithms \\ \end{tabular} & 100\% \\
[10] & 2 & SVM & 91\% & 2 &
\begin{tabular}{c} Multiple \\ algorithms \\ \end{tabular} & 100\% \\
[13; 48] & 4 & PiTILiD & 99.45\% & 5 & NBC & 100\% \\
[49] & 10 & DNN-PDC & 94.6\% & 10 & NBC & 100\% \\
[5; 12] & 10 & CNN & 96\% & 10 & NBC & 100\% \\
[16] & 16 & RF & 97.98\% & 25 & NBC & 96.21 \\
[7; 50] & 31 & NFC & 97.6\% & 25 & NBC & 96.21 \\
[16] & 38 & YR2S & 99.69\% & 25 & NBC & 96.21 \\
[9; 14] & 43 & ANN & 94.88\% & 25 & NBC & 96.21 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of algorithms. Plants have medical properties and provide food and oxygen. Therefore, the research community is very interested in this area, especially in plant classification and identification through the leaves. Various research studies identify plant properties from flowers or other parts; however, the leaf is considered the most reliable source of information since it is available all year round [53]. Identification of plants using their leaves is becoming increasingly fascinating and trendy. Every leaf contains specific details that aid in the recognition of different types of plants. Many authors have studied this issue from different perspectives [54; 55]. For example, the authors in [54] seek to examine and assess the execution and effectiveness of various approaches for categorizing plants.