# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 1 Introduction
For example, leaves from different species may appear very similar, or conversely, leaves from the same species may look different due to variations in environmental conditions, age, and other factors [5]. Additionally, obtaining large datasets of leaf images that are sufficiently diverse and labeled with high accuracy is a non-trivial task. These datasets are crucial for training and testing machine learning models but require significant time and effort to compile. Other practical challenges include dealing with the varying quality of leaf images due to differences in lighting, orientation, occlusion, and background clutter. This variability can introduce noise into the system, reducing the accuracy of feature extraction  
Figure 1: Structure of plant leaf classification. and classification. Moreover, the computational complexity of processing large image datasets with advanced machine learning methods can be prohibitive, requiring substantial computational resources. Furthermore, models that can generalize well to unseen data are needed, as the ultimate test of an automated classification system is its performance in real-world conditions, where ideal image capture is not always possible. Developing models robust to variations in data quality and capable of being deployed in various environmental and technological contexts remains a significant hurdle. Our work uses three types of datasets from the Cope Leaf Dataset [6] for leaf classification. Enhancing classification accuracy, particularly with large datasets and numerous classes, requires a combination of data preprocessing, model selection, regularization techniques, and fine-tuning. To improve the accuracy of data with multiple classes, we devised several feature selection, preprocessing, and training methods. We address a complex classification problem involving 99 classes from the Cope Leaf Dataset, tackling challenges such as class imbalance, overfitting, and feature complexity. Our approach to resolving these issues enhances classification accuracy, an aspect that has been underexplored in prior studies. Contributions to this work are listed as follows:  
* A complex classification problem is addressed involving 99 classes from the Cope Leaf Dataset, tackling challenges such as class imbalance, overfitting, and feature complexity. Our approach to resolving these issues enhances classification accuracy, an aspect that has been underexplored in prior studies. * Although preprocessing techniques like normalization, imputation, and noise reduction are well established, our systematic integration of these methods into a cohesive workflow tailored for a large multi-class dataset significantly improves model performance, as evidenced by the accuracy gains in our experimental results. * By comparing multiple machine learning (ML) algorithms, including the Hoeffding Tree, we provide new insights into their performance on high-dimensional, imbalanced multi-class datasets. The superior performance of the Hoeffding Tree over models like Naive Bayes offers valuable contributions to understanding which models are more effective for complex classification challenges. This paper is structured as follows: First, the introduction and literature review are presented in Section 1. Section 2 provides a comprehensive understanding of the topic. Section 3 presents feature extraction and selection. Section 4 introduces plant leaf classification using machine learning algorithms to delve deeper into the subject. Section 5 outlines the preprocessing and classification methods used in this study, followed by the presentation of the experimental results in Section 6. Lastly, Sections 7 and 8 are dedicated to discussion, conclusion, and future recommendations.