# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 4 Machine Learning for Plant Leaf Classification
GLCM is often employed to extract texture features of surface characteristics that are not readily apparent from the leaf's shape alone. Additionally, edge detection techniques can be used to extract venation features, which give a detailed map of the internal structure of the leaf, adding discriminative power when similarly shaped species differ with respect to their internal vein structure [60; 63]. Machine learning algorithms have proven valuable in overcoming these challenges. Multilayer Perceptron (MLP), Support Vector Machine (SVM), and other models have been employed and evaluated based on specific performance metrics [31; 64]. Machine learning has been significantly accelerated by advancements in deep learning, especially with the enhancement of neural network frameworks such as Convolutional Neural Networks (CNNs). CNNs have gained popularity due to their inherent capability of processing and deciphering complex imagery, particularly plants. Due to their sophisticated image recognition capabilities, CNNs have demonstrated robust performance in a wide range of plant classification tasks, thereby establishing new standards for accuracy and reliability. As illustrated in Figure 3, the workflow of leaf classification commences with image preprocessing to standardize and improve the quality of the input images. This step ensures that lighting, scale, and orientation variations do not affect the subsequent feature extraction process. After preprocessing, feature extraction is performed to capture the intrinsic characteristics of the leaves, such as texture, shape, and color features. Once these features are extracted, feature selection is employed to identify and retain the most significant features that contribute to differentiating between leaf species, thereby reducing the complexity of the model. The selected features are then fed into a machine learning classifier trained to recognize and classify the various leaf types. The outcome of this process is a set of results that reflect the classifier's accuracy in leaf classification. It is critically important to realize that the effectiveness of machine learning models in plant leaf classification is heavily dependent on image processing and feature extraction techniques. Techniques like the Gray Level Co-occurrence Matrix (GLCM) play a significant role in mining out critical features such as leaf texture, which are vital for classifying leaves. These characteristics form the basis for machine learning models to distinguish between species [51]. In addition to these models, commonly used performance metrics to evaluate plant leaf classification are accuracy, precision, recall, and F1-score. These metrics allow us to assess how well a model separates species accounts for misclassifications, and finds the right balance between precision and recall. The accuracy of CNNs in complex classification tasks has consistently been shown to be the best of the three compared, as they can identify fine details in images. SVMs generally perform well with structured, high-dimensional features, and K-NN provides a simple, interpretable baseline for comparison. We applied these machine learning techniques to classify plant leaves in our study. Based on the unique challenges posed by our dataset, we addressed them by automatically extracting features using CNNs, and manually extracting features using SVMs and K-NN, achieving high classification accuracy. In our approach, we blended shape, texture, and venation features to form a single comprehensive model, which is robust against variation in plant morphology. Feature extraction techniques are crucial for the success of the machine learning process. They stand as a cornerstone technique, drawing upon the shape, texture, and venation of leaves as key discriminative attributes. The shape, encompassing the leaf's outline, tip, base, margins, and vein arrangement, is discerned to classify plant species distinctly [65].