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 Yadav, Ajay; Poonia, Ramesh Chandra; Mehndiratta, Vandana Title A name given to the resource Wheat Grain Identification 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 Lecture Notes in Networks and Systems;Volume;1122;pp.281-288 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7426-5_21" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7426-5_21</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218216277?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218216277?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 Yadav A., CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, 201003, India; Poonia R.C., CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, 201003, India; Mehndiratta V., CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, 201003, India Description An account of the resource This research delves into the innovative application of Explainable Artificial Intelligence (XAI) within wheat grain identification. Employing sophisticated machine learning models augmented by XAI techniques, the study aims to enhance the transparency and comprehensibility of decision-making processes associated with classifying wheat grains. Key objectives include refining model accuracy, imparting insights into critical identification-influencing characteristics, and developing an intuitive user interface tailored for end users, particularly farmers. Through a methodical analysis, the research underscores the significance of XAI in detecting flaws and fine-tuning the model, ultimately bolstering its reliability. The findings of this investigation carry implications for advancing agricultural practices, fostering stakeholder trust, and adapting to the ever-evolving dynamics of the environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Artificial intelligence; CNN; LIME; SHAP; XAI Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981977425-8; 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