Advancing Plant Species Identification: A Comparative Analysis of SVM and PNN Classifiers using the Flavia Dataset

Plants are essential for human life and provide food and oxygen. However, technological advancements have led to habitat loss, overconsumption, and climate change, causing numerous plant species to extinction. Identifying plants is crucial for ecosystem conservation and protection. Advancements in data analysis, imaging, and plant morphology have revolutionized decision-making in fields like climate science, veterinary nutrition, crop management, and yield prediction. This paper analyses plant identification methods, focusing on feature extraction and classification techniques. It evaluates the performance of Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) on the Flavia dataset, revealing that SVM outperforms PNN in accurately identifying plant species.