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Explainable AI: Leaf-based medicinal plant classification using knowledge distillation

dc.contributor.authorMengisti Berihu Girmay, Samuel Obeng
dc.date.accessioned2024-04-08T11:56:32Z
dc.date.available2024-04-08T11:56:32Z
dc.date.issued2024
dc.description.abstractMedicinal plants are used in a variety of ways in the pharmaceutical industry in many parts of the world to obtain medicines. They are traditionally used especially in developing countries, where they provide cost-effective treatments. However, accurate identification of medicinal plants can be challenging. This study uses a deep neural network and knowledge distillation approach based on a dataset of 4,026 images of 8 species of leaf-based Ethiopian medicinal plants. Knowledge from a ResNet50 teacher model was applied to a lightweight 2-layer student model. The student model, optimized for efficiency, achieved 96.91% accuracy and came close to the 98.98% accuracy of the teacher model on unseen test data. The training was built on optimization strategies, including oversampling, data augmentation, and learning rate adjustment. To understand the model's decisions, LIME (Local Interpretable Model-agnostic Explanations) and degree Grad-CAM (Gradient-weighted Class Activation Mapping) post-hoc explanation techniques were used to highlight influential image regions that contributed to classification.en
dc.identifier.isbn978-3-88579-738-8
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43864
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics(LNI) - Proceedings, Volume P - 344
dc.subjectknowledge distillation
dc.subjectconvolutional neural network
dc.subjectexplainable AI
dc.subjectresource efficiency
dc.titleExplainable AI: Leaf-based medicinal plant classification using knowledge distillationen
dc.typeText/Conference Paper
gi.citation.endPage34
gi.citation.publisherPlaceBonn
gi.citation.startPage23
gi.conference.date27.-28. Februar 2024
gi.conference.locationStuttgart
gi.conference.reviewfull

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