Automated Medicinal Plant Classification Using PSO-Optimized Machine Learning Techniques

Abstract

Enhancing medicinal plant categorization by parameter adjustment of the Support Vector Machine (SVM) in a PSO-optimized cascaded network is the main goal of this work's expansion. To choose the most pertinent qualities, Particle Swarm Optimization (PSO) is used to enhance deep features that were retrieved using ResNet50. When compared to other techniques, the customized SVM model dramatically increases classification accuracy and decreases misclassification. According to experimental data, the improved SVM performs better than the others, achieving an accuracy of 99.75%. This method guarantees an automated medicinal plant identification system that is more dependable, effective, and scalable.