Advanced Leaf Vein Pattern Extraction and Analysis using Machine Learning Algorithms
Year: 2025
Type: article
Abstract: Leaf vein extraction has a significant utility in botanical research and plant taxonomy. This research proposes an automated approach for extracting and analysing vein patterns from leaf photos using image processing and machine learning approaches. The methodology involves photo capture, pre-processing, morphological procedures, and feature extraction with Local Binary Pattern (LBP). Various machine learning classifiers like as Logistic Regression, SVM, Decision Tree, Random Forest, KNN, Gaussian Naïve Bayes, Gradient Boosting, and XGBoost are applied for vein classification. Among these, Gradient Boosting and XGBoost obtain the maximum accuracy of 92.86%, followed by Random Forest with 90.48% accuracy. The suggested method offers high accuracy and efficiency, making it a great tool for plant species identification, disease detection, and ecological investigations.
Author: Nikunj Babbar
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409166309