# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 3 Feature Extraction and Selection
Every method has its pros and cons when it comes to identifying leaf patterns. Leaf image quality is crucial; thus, a dependable leaf database is necessary to set up the machine learning algorithm for leaf recognition and validation. The study in [56] suggests utilizing two features, namely, shape, and texture, as part of the proposed method. The method based on the shape will capture the outline characteristics of each leaf and subsequently evaluate the differences between them utilizing the Jeffrey-divergence metric. The patterns of edge gradients will be used to examine the overall texture of the leaf. Then, an incremental classification algorithm will combine the outcomes of these approaches. On the other hand, in [56], the authors stressed that leaf images must be pre-processed for plant identification using leaves to extract essential features. The authors in [56]  
Figure 2: Features selection, extraction, and classification using machine learning. introduce plant identification through leaf characteristics with Multiclass SVM (MSVM) as the classifier. Through the provided analysis, the authors claim that a high recognition rate of 90% is received by using different datasets. In this work, feature extraction aims to reduce data complexity while retaining as much relevant information as possible. This helps to improve the performance and efficiency of machine learning algorithms and simplify the analysis process. Feature extraction may involve the creation of new features and data manipulation to separate and simplify the use of meaningful features from irrelevant ones. The separation of the leaf object (foreground) from its background is referred to as segmentation. This procedure uses the adaptive threshold K-Means method [28]. Following segmentation, geometric features are taken from the segmented image. For example, the aspect ratio and roundness (\(R\)) of the leaf can be determined using the following very know formula:  
\[R=\frac{4\times\pi\times A}{p^{2}} \tag{1}\]  
The color of the leaves is considered a morphological characteristic. Several statistical characteristics, such as mean, skewness, and kurtosis, can be calculated in the color space to characterize leaf color attributes. This approach has low computer complexity and is suitable for real-time processing. Because the processing image contains three color planes (red, green, and blue), the mean of the three-color planes after the leaf contour can be used to estimate \(Ch\) and \(N\), as shown below:  
\[ChN=G-\left(\frac{R+B}{2}\right) \tag{2}\]  
The values of red and blue are provided for normalization and to estimate the \(ChN\) of healthy and dead leaves. Nitrogen can be calculated using equation:  
\[N=\frac{1}{3}\bigg{(}\frac{HUI-60}{60}+(1-SA)+(1-BG)\bigg{)} \tag{3}\]  
where \(HUI\), \(SA\), and \(BG\) represent the hue value, saturation intensity, and brightness intensity of the colored image, respectively. The maximum distance between two spots on the leaf object's boundary in the processing image is referred to as effective diameter (\(ED\)). The effective diameter can be estimated using another morphological property of a leaf known as area, as follows:  
\[ED=\sqrt{\frac{Area}{\pi}} \tag{4}\]