A novel approach to navigate the taxonomic hierarchy to address the Open-World Scenarios in Medicinal Plant Classification
This work addresses the challenge of accurately classifying unknown species in medicinal plant taxonomy, which is crucial for comprehensive plant identification in real-world applications, though it appears incremental in combining existing techniques for a specific domain.
The paper tackles the problem of hierarchical classification of medicinal plants in open-world scenarios, where unknown species must be identified, by proposing a method that integrates DenseNet121, Multi-Scale Self-Attention, and cascaded classifiers, achieving accuracies up to 83.36% for phylum prediction and reducing model size by four times compared to state-of-the-art methods.
In this article, we propose a novel approach for plant hierarchical taxonomy classification by posing the problem as an open class problem. It is observed that existing methods for medicinal plant classification often fail to perform hierarchical classification and accurately identifying unknown species, limiting their effectiveness in comprehensive plant taxonomy classification. Thus we address the problem of unknown species classification by assigning it best hierarchical labels. We propose a novel method, which integrates DenseNet121, Multi-Scale Self-Attention (MSSA) and cascaded classifiers for hierarchical classification. The approach systematically categorizes medicinal plants at multiple taxonomic levels, from phylum to species, ensuring detailed and precise classification. Using multi scale space attention, the model captures both local and global contextual information from the images, improving the distinction between similar species and the identification of new ones. It uses attention scores to focus on important features across multiple scales. The proposed method provides a solution for hierarchical classification, showcasing superior performance in identifying both known and unknown species. The model was tested on two state-of-art datasets with and without background artifacts and so that it can be deployed to tackle real word application. We used unknown species for testing our model. For unknown species the model achieved an average accuracy of 83.36%, 78.30%, 60.34% and 43.32% for predicting correct phylum, class, order and family respectively. Our proposed model size is almost four times less than the existing state of the art methods making it easily deploy able in real world application.