An Ensemble Stacking Regression Model for Brown Plant Hopper Pest Population Prediction

The management of agricultural pests is crucial for the security of the food supply. The Brown Plant Hopper (BPH) harms rice fields and reduces productivity, which is a major concern. Accurate forecasting and early detection of BPH population dynamics based on environmental parameters are essential for the quick and efficient application of pest control strategies. This study investigates the use of machine learning methods like bagging, boosting, voting, and ensemble regression techniques to predict the population of BPH. The proposed ensemble stacking regression model uses Extreme gradient boosting (XGBoost), Random Forest (RF), and Extremely Randomized Trees (ERT) as base models with a meta‐model Decision Tree (DT), and it is utilized to estimate the pest population. Several existing models are compared with the proposed model in terms of R‐squared, Adjusted R‐squared, RMSE as well as MSE. The outcomes show that these machine learning models are capable of accurately and successfully predicting the BPH population, which makes the proposed model an important tool for pest population prediction in rice farming.