Scalable Hybrid Ensemble Models for Crop Recommendation in Precision Farming Contexts

Precision agriculture requires proper recommendation of crops, which makes it possible to make decisions that maximize yields and resource utilization. The conventional approaches to crop selection are not always effective in such circumstances as soil erosion and changing climate. This paper provides a machine learning model that forecasts the most appropriate crop, which is determined by the data-driven classification of soil nutrients (N, P, K), climate (temperature, humidity, rainfall) and soil pH of 22 types of crops. At the beginning, standard models (Decision Tree, Random Forest, LightGBM) are tested. The paper then suggests a composite stacking ensemble whereby LightGBM, CatBoost, Random Forest, Multi-Layer Perceptron, and XGBoost are used as base models, and Logistic Regression is used as a metalearner. The validation and training were based on a publicly available crop recommendation dataset. The findings indicate that the hybrid ensemble system achieves greater prediction accuracy—99.55% F1-score, recall, and precision—which outperforms the baseline classifiers. The above results point to promising hybrid ensemble learning as a highly scalable and robust solution to aid agricultural decisions in various real-world scenarios.