A Novel Cascade Classifier Framework for Open-World Medicinal Plant Classification

Tanisha Rana, Soumen Sinha, Rahul Roy

Abstract: In the domain of medicinal plant classification, where the diversity of plant species is vast and increasing, precise categorization is essential. Traditional approaches for the classification of species often struggle to accommodate unidentified or unclassified species. Our research introduces a framework to address the issue. Our intuition is to pose the medicinal plants classification as an open-world problem and predict the class for unknown samples in the hierarchy with confidence to the best-known label. To achieve this we propose a unique custom model that combines the concept of VGG-16 and cascaded classifier facilitating the identification of unknown medicinal plant species. This innovative approach significantly enhances the precision and adaptability of our classification system, addressing the challenges posed by unknown or unrecorded plant species. Given an unknown species, our custom model can predict the taxonomic categories of the species. We employed a dataset comprising ten diverse medicinal plant species, serving as the basis for training. The results demonstrated promising accuracy for unknown medicinal plants species. We used three unknown species named Wood Sorel, Noni and Curry Leaves to test our model. For the unknown species the model obtained average accuracies of 81.66% and 76.11 % for predicting phylum and class respectively.