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.