Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Choudhury, Rishab; Poonia, Ramesh Chandra; Mehndiratta, Vandana Title A name given to the resource Disease Identification for Tea Leaves Using Explainable Artificial Intelligence Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies;Volume;121 SIST;pp.199-209 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-6254-8_15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-6254-8_15</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018920420?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018920420?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Choudhury R., CHRIST University Bangalore, Bengaluru, India; Poonia R.C., CHRIST University Bangalore, Bengaluru, India; Mehndiratta V., CHRIST University Bangalore, Bengaluru, India Description An account of the resource Infection can consequently reduce both quality and yield, and causes major threats to tea production round the world. It is therefore sometime difficult to achieve fast, reliable, and precise identification of disease in tea plants and hence the need to embrace new methods of disease identification. To enable realisation of accurately understandable models for classification of the diseases in tea leaves, Explainable Artificial Intelligence (XAI) approaches are applied in this work. In order to train and test machine learning models, we collected a set of repos of high-resolution images of tea leaves affected by various diseases along with meta information. CNN models were trained with the help of our approach and adopting XAI tools as tools for explanation of predictions. From this study, the field of agricultural AI is benefitted from the illustration of how XAI might enhance disease management strategies in tea agriculture. The results demonstrate an accuracy of 87.85%, with precision, recall and F1-scores ranging between 0.78 and 0.95 across different classes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Agricultural AI; Conventional methods; Explainable artificial intelligence (XAI); Tea industry Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981966253-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online