<![CDATA[This paper describes a leaf classification framework based on machine learning methodologies. The dataset included 36 leaf species with 340 instances and 16 features. Decision Tree, Logistic Regression, Support Vector Machine, XGBoost, AdaBoost, and Random Forest were applied initially on the raw dataset. It was found that Decision Tree, XGBoost and Random Forest performed well, with accuracy scores of 53%, 60% and 85% respectively. After feature reduction based on Information Gain and correlation matrix analysis, four highly correlated features were removed. This led to a marginal improvement in accuracy to 60% for Decision Tree and to 65% percentage for XG Boost, while the accuracy of Random Forest dropped to 74%. We also applied SMOTE (Synthetic Minority Oversampling Technique) to balance the raw dataset. This enhanced the accuracy of Random Forest and XGBoost to 91% and 81% respectively. With Computer Vision, we achieved an accuracy of 66% for SVM, 61% for Random Forest, 43% for K-NN and 37% for the Decision Tree classifier on the image dataset. The pre-trained VGG16 model achieved an impressive 90.47% accuracy, outperforming the custom CNN model and conventional models like Linear Discriminant Analysis (LDA) and KNN with various K-values from the literature]]>
Copyrights © 2026
