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, Jai; George, Jossy; Nair, Akhil M. Title A name given to the resource A Study on the Efficacy of Homogeneous and Heterogeneous Stacking in Machine Learning 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;1355 LNNS;pp.253-269 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4883-2_21" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4883-2_21</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010582189?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010582189?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 J., CHRIST (Deemed to Be University), Delhi NCR Campus, Delhi, India; George J., CHRIST (Deemed to Be University), Delhi NCR Campus, Delhi, India; Nair A.M., CHRIST (Deemed to Be University), Delhi NCR Campus, Delhi, India Description An account of the resource This study addresses the crucial issue of early and accurate plant disease diagnosis by comparing the performance of homogeneous and heterogeneous stacking models. The study seeks to introduce a novel homogeneous multi-layered stacking model that combines the Light Gradient Boosting Method (LightGBM) and Extreme Gradient Boosting (XGBoost) for plant disease detection and compare it with a heterogeneous stacking model that employs diverse classifiers. While traditional methods typically use basic stacking techniques, this research explores the complexities of various model architectures. By leveraging the strengths of both LGBM and XGB classifiers, the approach aims to deliver a highly accurate and efficient disease detection system. A comprehensive evaluation reveals that the homogeneous stacking model achieves superior performance, with a ROC AUC of 85.12%, compared to 83.09% for the single LGBM model. The study utilizes metrics such as AUC-ROC curves, accuracy, and precision-recall curves to assess performance. Future work will focus on integrating these models with real-time monitoring systems and extending their applications to a wider range of crops and environmental conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource AUC-ROC curves; Confusion matrix; F1 score; Homogenous stacking; LGBM; Plant disease prediction; XGB Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964882-5; 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