A Study on the Efficacy of Homogeneous and Heterogeneous Stacking in Machine Learning
AUC-ROC curves; Confusion matrix; F1 score; Homogenous stacking; LGBM; Plant disease prediction; XGB
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.
Yadav, Jai; George, Jossy; Nair, Akhil M.
Lecture Notes in Networks and Systems;Volume;1355 LNNS;pp.253-269
Springer Science and Business Media Deutschland GmbH
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-96-4883-2_21 https://www.scopus.com/pages/publications/105010582189?origin=resultslist
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