A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system
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), integr...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1567679/full |
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| author | Amna Ikram Sunnia Ikram El-Sayed M. El-kenawy El-Sayed M. El-kenawy Adil Hussain Amal H. Alharbi Marwa M. Eid Marwa M. Eid |
| author_facet | Amna Ikram Sunnia Ikram El-Sayed M. El-kenawy El-Sayed M. El-kenawy Adil Hussain Amal H. Alharbi Marwa M. Eid Marwa M. Eid |
| author_sort | Amna Ikram |
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| description | 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 R2. 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. |
| format | Article |
| id | doaj-art-c5058e5b761f40ed8e47b20f1b1ecfca |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-c5058e5b761f40ed8e47b20f1b1ecfca2025-08-20T01:55:31ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-05-011610.3389/fpls.2025.15676791567679A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping systemAmna Ikram0Sunnia Ikram1El-Sayed M. El-kenawy2El-Sayed M. El-kenawy3Adil Hussain4Amal H. Alharbi5Marwa M. Eid6Marwa M. Eid7Department of Computer Science and IT, Government Sadiq College Women University, Bahawalpur, PakistanDepartment of Software Engineering, The Islamia University, Bahawalpur, PakistanSchool of ICT, Faculty of Engineering, Design and Information and Communication Technology (EDICT), Bahrain Polytechnic, Isa Town, BahrainApplied Science Research Center. Applied Science Private University, Amman, JordanSchool of Electronics and Control Engineering, Chang’an University, Xi’an, ChinaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaFaculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, EgyptJadara University Research Center, Jadara University, Irbid, JordanMaize-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 R2. 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.https://www.frontiersin.org/articles/10.3389/fpls.2025.1567679/fullmaize-soybean intercroppingyield predictionfuzzy inference systemensemble learninggenetic algorithmrandom forest |
| spellingShingle | Amna Ikram Sunnia Ikram El-Sayed M. El-kenawy El-Sayed M. El-kenawy Adil Hussain Amal H. Alharbi Marwa M. Eid Marwa M. Eid A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system Frontiers in Plant Science maize-soybean intercropping yield prediction fuzzy inference system ensemble learning genetic algorithm random forest |
| title | A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system |
| title_full | A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system |
| title_fullStr | A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system |
| title_full_unstemmed | A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system |
| title_short | A fuzzy-optimized hybrid ensemble model for yield prediction in maize-soybean intercropping system |
| title_sort | fuzzy optimized hybrid ensemble model for yield prediction in maize soybean intercropping system |
| topic | maize-soybean intercropping yield prediction fuzzy inference system ensemble learning genetic algorithm random forest |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1567679/full |
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