Institutional-Repository, University of Moratuwa.  

Explainable ai techniques for deep convolutional neural network based plant disease identification

Show simple item record

dc.contributor.author Kiriella, S
dc.contributor.author Fernando, S
dc.contributor.author Sumathipala, S
dc.contributor.author Udayakumara, EPN
dc.contributor.editor Piyatilake, ITS
dc.contributor.editor Thalagala, PD
dc.contributor.editor Ganegoda, GU
dc.contributor.editor Thanuja, ALARR
dc.contributor.editor Dharmarathna, P
dc.date.accessioned 2024-02-06T08:05:20Z
dc.date.available 2024-02-06T08:05:20Z
dc.date.issued 2023-12-07
dc.identifier.uri http://dl.lib.uom.lk/handle/123/22191
dc.description.abstract Deep learning-based computer vision has shown improved performance in image classification tasks. Due to the complexities of these models, they have been referred as opaque models. As a result, users need justifications for predictions to enhance trust. Thus, Explainable Artificial Intelligence (XAI) provides various techniques to explain predictions. Explanations play a vital role in practical application, to apply the exact treatment for a plant disease. However, application of XAI techniques in plant disease identification is not popular. This paper discusses the key concerns and taxonomies available in XAI and summarizes the recent developments. Also, it develops a tomato disease classification model and uses different XAI techniques to validate model predictions. It includes a comparative analysis of XAI techniques and discusses the limitations and usefulness of the techniques in plant disease symptom localization. en_US
dc.language.iso en en_US
dc.publisher Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.subject Explainable artificial intelligence en_US
dc.subject Plant disease en_US
dc.subject Deep learning en_US
dc.title Explainable ai techniques for deep convolutional neural network based plant disease identification en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty IT en_US
dc.identifier.department Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.identifier.year 2023 en_US
dc.identifier.conference 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.place Moratuwa, Sri Lanka en_US
dc.identifier.pgnos pp. 1-6 en_US
dc.identifier.proceeding Proceedings of the 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.email shkiriella@agri.sab.ac.lk en_US
dc.identifier.email subhaf@uom.lk en_US
dc.identifier.email sagaras@uom.lk en_US
dc.identifier.email udayaepn@appsc.sab.ac.lk en_US


Files in this item

This item appears in the following Collection(s)

  • ICITR - 2023 [47]
    International Conference on Information Technology Research (ICITR)

Show simple item record