A Fuzzy-Optimized Hybrid Ensemble Model for Yield Prediction in Maize-soybean Intercropping System

Amna Ikram^1*Sunnia Ikram^2*El-Sayed M. El-kenawy^3,4Adil Hussain⁵Amal H. Alharbi⁶Marwa M. Eid⁷

• ¹Government Sadiq College Women University, BAHAWALPUR, Pakistan
• ²Islamia University of Bahawalpur, Bahawalpur, Pakistan
• ³Applied Science research Center. applied science Private University, Amman, Jordan
• ⁴School of ICT, Faculty of engineering, Design and Information & Communication Technology, Bahrain Polytechnic,, Isa Town, Bahrain
• ⁵School of Materials Science and Engineering, Chang’an University, Xi’an, China
• ⁶Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
• ⁷Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt

Maize-soybean intercropping is a sustainable farming practice that optimizes resource use efficiency and improves yield potential. Accurate yield prediction is essential for effective agricultural management in such systems. This study proposes a Fuzzy-Optimized Hybrid Ensemble Model (FOHEM), integrating stacked ensemble machine learning algorithms with a fuzzy inference system (FIS) to improve yield prediction. The dataset includes four intercropping treatments: SM (sole maize), SS (sole soybean), 2M2S (two rows of maize with alternating two rows of soybean), and 2M3S (two rows of maize with alternating three rows of soybean). Key input features include environmental factors, soil nutrients, and management practices across different treatments. The FOHEM framework integrates the outputs of the FIS with a stacked ensemble model comprising Random Forest (RF), Categorical Boosting (CatBoost), and Extreme Learning Machine (ELM)). A genetic algorithm (GA) dynamically adjusts the weights between FIS and the ensemble model, optimizing final prediction while enhancing accuracy and robustness. Additionally, LIME and SHAP are used for model interpretability, and identifying yield influencing factors. The model is validated using performance metrics such as MSE, MAE, and R 2 . The results demonstrated that proposed model significantly enhances yield prediction accuracy, offering valuable insights for optimizing intercropping systems. This study highlights the potential of integrating machine learning, fuzzy inference and optimization techniques to advance precision agriculture and decision-making in sustainable farming.