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 George, Jossy; Yadav, Jai; Nair, Akhil M. Title A name given to the resource Integrating LightGBM and XGBoost for Robust Plant Disease Classification: A Homogenous Stacking Approach 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;1377 LNNS;pp.409-422 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-5370-6_30" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5370-6_30</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018796519?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018796519?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 George J., CHRIST (Deemed to Be University), Delhi NCR Campus, Ghaziabad, India; Yadav J., CHRIST (Deemed to Be University), Delhi NCR Campus, Ghaziabad, India; Nair A.M., CHRIST (Deemed to Be University), Delhi NCR Campus, Ghaziabad, India Description An account of the resource In addressing the critical challenge of early and accurate plant disease diagnosis, this study explores the application of a novel homogeneous multi-layered stacking model utilising Light Gradient Boosting Model (LGBM) and Extreme Gradient Boost (XGB) for the detection of plant diseases. Traditional approaches often rely on basic stacking methods; however, this research seeks to explore the intricacies of altering model architecture, combining the strengths of LGBM and XGB classifiers to build a highly accurate and efficient disease detection system. Comprehensive evaluations were conducted using metrics such as AUCROC curves, Confusion matrix and F1 scores. The ROC curve for the stacked model demonstrated superior performance with a score of 85.12%, compared to 83.09% for the single LightGBM model used for comparative analysis. The future scope of ML in agriculture includes integrating such models with real-time monitoring systems and expanding its applications to diverse crops and environments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource AUCROC curve; Confusion matrix; F1 score; Homogenous stacking; Plant disease prediction Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981965369-0; 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