[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124806-en":3,"doc-seo-124806-105":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},124806,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","APPLYING MACHINE LEARNING FOR ANALYSIS AND FORECASTING OF AGRICULTURAL CROP YIELDS","Accurate crop-yield forecasting underpins food security, efficient resource use, and resilient planning for adverse climatic conditions in Kazakhstan. This study investigates how weather conditions influence agricultural outputs by combining historical yield data (1990–2023) with detailed daily weather records for North Kazakhstan. It builds predictive machine-learning models by analyzing patterns within each dataset and their interactions, using time-series analysis and correlation-matrix evaluation to relate weather factors to crops. Regression performance is assessed with metrics such as RMSE and R2.","28  \nScientific Journal of Astana IT University ISSN (P): 2707-9031 ISSN (E): 2707-904X VolUmE 17, mArch 2024  \nDOI: 10.37943/17LKYF9288  \nAigul Mimenbayeva  \nMaster of Sciences, Senior Lecturer, Department of Computational and Data Science [aigulka79_79@mail.ru](aigulka79_79@mail.ru), [orcid.org/0000-0003-4652-470X](orcid.org/0000-0003-4652-470X)  \nAstana IT University, Kazakhstan  \nGulnur Issakova  \nPhD, Senior Lecturer of the Department of Information Systems  \n[is_gul_oral@mail.ru](is_gul_oral@mail.ru), [orcid.org/0000-0001-7272-4786](orcid.org/0000-0001-7272-4786)  \nS.Seifullin Kazakh Agro Technical Research University, Kazakhstan  \nBalausa Tanykpayeva  \nMaster of Natural Sciences, Senior Lecturer of the Department of Information Systems [balausa1.80@mail.ru](balausa1.80@mail.ru), [orcid.org/0009-0001-1259-0832](orcid.org/0009-0001-1259-0832)  \nS.Seifullin Kazakh Agro Technical Research University, Kazakhstan  \nAinur Tursumbayeva  \nMaster of Technical Sciences, Teacher of the Department of Information Systems [Turcumbaewa_ainur84@mail.ru](Turcumbaewa_ainur84@mail.ru), [orcid.org/0009-0000-3710-3925](orcid.org/0009-0000-3710-3925)  \nS.Seifullin Kazakh Agro Technical Research University, Kazakhstan  \nRaya Suleimenova  \nCandidate of Technical Sciences, Acting Professor of School of Engineering and Information Technology  \n[Suleimenova_raya@mail.ru](Suleimenova_raya@mail.ru), [orcid.org/0009-0004-2780-5391](orcid.org/0009-0004-2780-5391)  \nEurasian Technological University, Kazakhstan  \nAlmat Tulkibaev  \nMaster of Sciences, Teacher of the Department of Information Systems  \n[Almat_tulkibaev@mail.ru](Almat_tulkibaev@mail.ru), [orcid.org/0000-0002-3783-5429](orcid.org/0000-0002-3783-5429)  \nS.Seifullin Kazakh Agro Technical Research University, Kazakhstan  \nAPPLYING MACHINE LEARNING FOR ANALYSIS AND FORECASTING OF AGRICULTURAL CROP YIELDS  \nAbstract: Analysis and improvement of crop productivity is one of the most important areas in precision agriculture in the world, including Kazakhstan. In the context of Kazakhstan, agriculture plays a pivotal role in the economy and sustenance of its population. Accurate forecasting of agricultural yields, therefore, becomes paramount in ensuring food security, optimizing resource utilization, and planning for adverse climatic conditions. In-depth analysis and high-quality forecasts can be achieved using machine learning tools.  \nThis paper embarks on a critical journey to unravel the intricate relationship between weather conditions and agricultural outputs. Utilizing extensive datasets covering a period from 1990 to 2023, the project aims to deploy advanced data analytics and machine learning techniques to enhance the accuracy and predictability of agricultural yield forecasts. At the heart of this endeavor lies the challenge of integrating and analyzing two distinct types of datasets: historical agricultural yield data and detailed daily weather records of North Kazakhstan for 1990-2023. The intricate task involves not only understanding the patterns within each dataset but also deciphering the complex interactions between them. Our primary objective is to develop models that can accurately predict crop yields based on various weather parameters, a crucial aspect for effective agricultural planning and resource allocation. Using  \nCopyright © 2024, Authors. This is an open access article under the Creative Commons CC BY-NC-ND license  \nDOI: 10. 37943/17LKYF9288  \n© Aigul Mimenbayeva, Gulnur Issakova, Balausa Tanykpayeva, Ainur Tursumbayeva, Raya Suleimenova. Almat Tulkibaev  \n29  \nthe capabilities of statistical and mathematical analysis in machine learning, a Time series analysis of the main weather factors supposedly affecting crop yields was carried out and a correlation matrix between the factors and crops was demonstrated and analyzed.  \nThe study evaluated regression metrics such as Root Mean Squared Error (RMSE) and R2 for Random Forest, Decision Tree, Support Vector Machine (SVM)","cbCaioDfHvCFT2t2","https://ap.wps.com/l/cbCaioDfHvCFT2t2","pdf",1691368,1,15,"English","en",105,"# Introduction\n# Data and Methodology\n## Time Series and Correlation Analysis\n## Machine Learning Models and Evaluation Metrics\n# Results\n## Potato Yield Forecasting in North Kazakhstan\n# Implications and Use Cases","[{\"question\":\"What is the main purpose of the study on agricultural crop yields?\",\"answer\":\"The study aims to analyze the relationship between weather conditions and crop outcomes and to build models that forecast agricultural yields more accurately.\"},{\"question\":\"Which datasets are used for forecasting crop yields?\",\"answer\":\"The approach uses historical agricultural yield data from 1990 to 2023 and daily weather records for North Kazakhstan over the same period.\"},{\"question\":\"Which machine learning method performed best for potato yield forecasting?\",\"answer\":\"Random Forest Regressor showed the best performance, achieving a strong R2 value of 0.97865 and relatively low RMSE values.\"}]","APPLYING MACHINE LEARNING FOR ANALYSIS AND FORECASTING OF AGRICULTURAL CROP YIELDS | 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is the main purpose of the study on agricultural crop yields?","Question",{"text":75,"@type":76},"The study aims to analyze the relationship between weather conditions and crop outcomes and to build models that forecast agricultural yields more accurately.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which datasets are used for forecasting crop yields?",{"text":80,"@type":76},"The approach uses historical agricultural yield data from 1990 to 2023 and daily weather records for North Kazakhstan over the same period.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning method performed best for potato yield forecasting?",{"text":84,"@type":76},"Random Forest Regressor showed the best performance, achieving a strong R2 value of 0.97865 and relatively low RMSE 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