[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120148-en":3,"doc-seo-120148-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120148,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Performance Evaluation of Machine Learning Models for Crop Yield Prediction - Research Report","Agriculture relies on accurate yield forecasting to support effective resource allocation and sound decision-making, making predictive accuracy a practical priority for food security. This study performs a comparative performance evaluation of widely used machine-learning regressors, including Linear Regression, Random Forest, XGBoost, K-Nearest Neighbors, Decision Tree, and Bagging Regressor. Results show distinct performance gaps, where Linear Regression limits predictive reliability, while ensemble methods—Random Forest and XGBoost—deliver near 97% performance and strong R² behavior by capturing non-linear agricultural patterns. Recommendations emphasize optimizing these ensembles for real-world production prediction, with future work leveraging advanced optimization and domain knowledge to further improve accuracy.","INTERNATIONAL JOURNAL OF SCIENCE FOR GLOBAL SUSTAINABILITY  \n(A publication of Faculty of Science, Federal University Gusau, Nigeria)  \n\n| Performance Evaluation of Machine Learning Models for Crop Yield\u003Cbr>Prediction\u003Cbr>Muhammad Umar Abdullahi1*, Gilbert I.O. Aimufua2, Morufu Olalere2, Kene Tochukwu Anyachebelu2,\u003Cbr>Tahir Abdulhakim2\u003Cbr>1Department of Computer Science, Federal University of Technology, Owerri, Nigeria\u003Cbr>2Department of Computer Science, Nasarawa State University, Keffi, Nigeria\u003Cbr>Corresponding Author’[s Mail:](s Mail: umarmuhammadmuhammad@nsuk.edu.ng)[ ](s Mail: umarmuhammadmuhammad@nsuk.edu.ng)[umarmuhammadmuhammad@nsuk.edu.ng](s Mail: umarmuhammadmuhammad@nsuk.edu.ng)[ ](s Mail: umarmuhammadmuhammad@nsuk.edu.ng)Received on: January, 2024 Revised and Accepted on: March 2024 Published on: March 2024 |\n| --- |\n| ABSTRACT\u003Cbr>Agriculture, a fundamental pillar of worldwide sustenance, greatly benefits from precise yield projections, which provide effective allocation of resources and well-informed decision-making. This work focuses on the crucial task of predicting agricultural yields by conducting a thorough comparative examination of several machine-learning models. The examined models include Linear Regression, Random Forest, Extreme Gradient Boost (XGBoost), K-Nearest Neighbors (KNN), Decision Tree, and Bagging Regressor. The results demonstrate subtle variations in performance, with Linear Regression highlighting constraints in its ability to make accurate predictions. Ensemble approaches, namely: Random Forest and XGBoost, demonstrate remarkable accuracy, achieving almost 97% and R2 ratings. This highlights their ability to effectively capture complex agricultural patterns. The findings of this research provide valuable suggestions for professionals in agriculture and machine learning, making it easier to choose reliable models for predicting crop yields. It is also recommended to optimize Random Forest and XGBoost for accurate production predictions in practical agricultural scenarios. Future studies may focus on using sophisticated optimization approaches and incorporating specialized domain knowledge to enhance the precision of agricultural production prediction.\u003Cbr>Keywords: Performance Evaluation, Machine Learning Models, Crop Yield Prediction, Evaluation Metrics and Model Assessment |\n\n1.0 INTRODUCTION  \nAgriculture serves as the backbone of economies worldwide, providing sustenance and livelihoods for a significant portion of the global population. Predicting crop yields accurately is essential for optimizing agricultural practices, resource allocation, and ensuring food security (Al-Adhaileh & Aldhyani, 2022; Nti, Zaman, Nyarko-Boateng, Adekoya & Keyeremeh, 2023) .  \nTraditional methods of crop yield prediction, relying on historical data and simplistic statistical models, often struggle to capture the complexities inherent in agricultural systems (Engen et al., 2021; Sun et al., 2023) . In recent years, the integration of machine learning (ML) models has emerged as a promising avenue for improving the accuracy and efficiency of crop yield predictions (Araújo, Peres, Ramalho, Lidon & Barata, 2023; Droutsas, Challinor, Deva & Wang, 2022) .  \nMachine learning, a subset of artificial intelligence, offers a data-driven approach that can analyze vast and diverse datasets to discern intricate patterns and relationships (Aldoseri, Al-Khalifa, & Hamouda, 2023) . The application of ML in crop yield prediction involves leveraging a variety of factors, including but not limited to remote sensing data, weather patterns, soil  \ncharacteristics, and historical yield records (Han, Yoon, Kim, Lee & Lee, 2023) . The capability of ML models to adapt to changing conditions and learn from dynamic datasets makes them well-suited for addressing the challenges posed by the variability in agricultural environments (Elahi, Afolaranmi, Martinez-Lastra & Perez Garcia, 2023) .  \nThis study focuses on the performance evaluatio","cbCainr0QTCGMcVz","https://ap.wps.com/l/cbCainr0QTCGMcVz","pdf",645220,1,9,"English","en",105,"# Abstract\n## Introduction\n## Methods and Models\n## Comparative Performance Evaluation\n## Findings and Recommendations\n## Future Work","[{\"question\":\"Which machine-learning models are evaluated for crop yield prediction?\",\"answer\":\"The study compares Linear Regression, Random Forest, Extreme Gradient Boost (XGBoost), K-Nearest Neighbors (KNN), Decision Tree, and Bagging Regressor.\"},{\"question\":\"How do the models perform according to the study results?\",\"answer\":\"Linear Regression shows constraints in prediction accuracy, while ensemble approaches such as Random Forest and XGBoost achieve almost 97% performance and strong R² values.\"},{\"question\":\"What guidance does the study provide for real agricultural use?\",\"answer\":\"It recommends optimizing Random Forest and XGBoost to improve the accuracy of production predictions in practical agricultural scenarios.\"}]","Performance Evaluation of Machine Learning Models for Crop Yield Prediction - 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