# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 4 Machine Learning for Plant Leaf Classification  
Plant leaf classification through machine learning is a rapidly developing study area with significant challenges and promising opportunities. The application of machine learning has allowed researchers to address complex problems previously considered impossible by conventional computational methods. Using advanced computational models, plant species classification is now being redefined through the lens of agricultural practices, environmental conservation, and medicinal research [57]. Researchers have been motivated to develop machine learning techniques to classify leaves to prevent plant species' extinction. This ensures that plant biodiversity is better understood and utilized. Furthermore, it serves multiple objectives, from conserving ecosystems to identifying plant species with potential undiscovered medicinal properties [57]. However, classifying plant leaves is complex due to the diverse morphologies that appear. Various factors, such as their geographical location and seasonal changes, can affect the size, shape, and color of these plants. These variations introduce difficulties in developing a universal classification system [58]. In addition, the sheer number of plant species adds an additional layer of complexity that requires sophisticated, adaptable, and highly nuanced machine learning algorithms [57]. To meet these challenges, several machine learning models have been widely implemented, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and K-Nearest Neighbors (K-NN). The application of each model to the problem of plant leaf classification presents different advantages [58; 59]. CNNs have become dominant because they have been found to learn features in an automatic, 'end-to-end' way, directly from images without manual feature extraction. CNNs use hierarchical layers to capture fine details such as leaf shape, venation, and texture. One reason CNNs are so effective at processing leaf images is that this complex visual input requires the ability to learn representations that capture small morphological details. CNNs also excel in tasks where intra-class variability (differences within the same species) is high, as their deep learning structure allows for greater flexibility in identifying variations in patterns like leaf vein arrangement or surface texture [60; 61]. On the other hand, SVMs tend to be used when the data are represented with a manually extracted set of features. In high-dimensional spaces, where the decision boundary between classes has to be optimized, these models are quite effective [60; 61]. Features such as leaf perimeter, aspect ratio, and texture features extracted from the Gray Level Co-occurrence Matrix (GLCM) [59] are used by SVMs. Manually extracted features of the leaf give a structured view of the leaf, on which SVMs learn the optimal hyperplane to separate the feature space and thus classify species. In this case, SVMs have been shown to perform well until the features become inseparable due to complexity. As with CNNs or SVMs, the K-NN algorithm has also been used for plant leaf classification. K-NN finds the new leaf sample, compares it with all stored examples, and then classifies it according to the majority class of its nearest neighbors. This model is strong for class separation and clear geometric features learned from the feature space. Unfortunately, performance can be jeopardized by high-dimensional data or overlapping classes. Because K-NN is often inefficient and inaccurate in high-dimensional feature spaces, it is often enhanced with dimensionality reduction techniques such as Principal Component Analysis (PCA) [57; 62]. The success of these models largely depends on feature extraction. The literature has explored the shape, texture, and venation feature extraction of plant leaves. Differentiation among species based on overall structure, as well as shape features such as perimeter, convexity, and aspect ratio, are important in identifying species.