A novel approach to navigate the taxonomic hierarchy to address the Open-World Scenarios in Medicinal Plant Classification

2025-02-24

AI Summary

To address the dual challenges of unknown-species identification and systematic multi-level (phylum–class–order–family–species) taxonomic annotation in open-world classification of medicinal herbal plants, this paper proposes the first open-set hierarchical classification framework integrating DenseNet121, multi-scale self-attention (MSSA), and a cascaded hierarchical classifier. The method enables interpretable hierarchical predictions for unknown species across the full taxonomic hierarchy—marking the first such capability—and overcomes the longstanding limitation of conventional models in jointly modeling hierarchical structure and open-set recognition. Evaluated on a realistic medicinal plant dataset with background clutter, it achieves unknown-class identification accuracies of 83.36%, 78.30%, 60.34%, and 43.32% at the phylum, class, order, and family levels, respectively. With only one-quarter the parameters of state-of-the-art methods, the model is lightweight and suitable for edge-device deployment.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Medical and Biological Imaging
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

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.

Problem

• Open-World Medicinal Plant Classification
• Hierarchical Taxonomy for Unknown Species
• Multi-Scale Attention for Species Identification

Innovation

• DenseNet121 integration
• Multi-Scale Self-Attention
• Cascaded classifiers usage