Monitoring plant health in an accurate way through multivariate sensor readings is a crucial aspect of accuracy in agriculture. The paper examines the outcomes of ensemble learning techniques to identify the level of their effectiveness and proposes an additional stacking model AgroStackNet consisting of XGBoost, LightGBM, Random Forest, and CatBoost. These comparative analysis results were given on the other combinations of ensembles as CNN + RF, KNN + SVM + RF and XGBoost + LightGBM. Data preprocessing was done by using some iterations of imputation, normalizing features and encoding labels and cross-validation was done at the five-fold level in order to provide reliability. Although the results of the boosting and the hybrid models were competitive, the AgroStackNet showed an even better result with an accuracy of 87.55%, precision of 87.61%, and recall and F1-score of 87.55 patches and 87.54. Confusion matrix analysis showed good performance, i.e., that there were balanced ratios of mistakes in all classes. The findings show that the use of nonhomogeneous learners in a single stacking scheme enhances generalization and predictivity and, as such, AgroStackNet is robust and scalable intelligent medical approach to sensor-enabled plant health diagnosis, and intelligent agricultural systems.