[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120545-en":3,"doc-seo-120545-105":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":20,"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},120545,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning Based Method for Forecasting Crop Yield","Machine learning for crop yield prediction addresses the growing global food supply crisis by improving data-driven decision-making in agriculture. The approach centers on data acquisition, preprocessing, and model assessment using a crop yield prediction dataset. Key influential factors—rainfall, temperature, and pesticide—are identified to support stronger predictive modeling. Decision Trees, Random Forest, SVM, ANN, Naïve Bayes, and LSTM are evaluated for effectiveness, yielding practical insights for precision farming and sustainable agriculture.","Machine Learning Based Method for Forecasting Crop Yield  \nRenu Kumari1 ,Vikash Sawan2 ,Mrinmoy Kayal3   \n1Research Scholar K. K. University, Nalanda Bihar  \n2,3Assistant Professor, Department of Computer Engineering & Applications GLA University, Mathura Uttar Pradesh.  \n*[Corresponding Email: vikash.sawan@gla.ac.in](Corresponding Email: vikash.sawan@gla.ac.in)  \nThis is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.   \nAbstract  \nApplications of machine learning are revolutionizing data processing and decision-making, which is having a significant effect on the global economy. Given the worldwide food supply crisis, agriculture isone of the industries where the effects are most noticeable. This paper focuses on crop yield prediction based on pattern analysis with the help of the machine learning approach, which focuses on data acquisition, preprocessing, and assessment. Taking the Crop Yield Prediction Dataset as a solution, the most potential factors, including rainfall, temperature, and pesticide, have been identified to have the most influential factors in creating better prediction models. Among these, Decision Trees, Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes, and Long Short-Term Memory (LSTM)are the most common and are checked for their effectiveness. It brings out facts that are instrumental in analysis to improve the yields on farms and come up with possible recommendations on precision farming and sustainable agriculture. This paper aims to provide insights that can help improve farm yields.  \nKeywords: Crop yield prediction, Machine learning, Precision agriculture, Sustainable farming.  \n1. Introduction  \nAgriculture plays a vital role in ensuring food security, economic support, and environmental sustainability. With increasing population across the globe, demand for food production increases and it becomes important to use advanced technologies to enhance agriculture's productivity. Accurate predictions of crop yields have been agriculture's biggest challenge for farmers and agriculture specialists because there are numerous factors such as soil health, weather patterns, and farming practices affecting production. Historical records, weather forecasts, and statistical modeling have been traditionally used to predict crop yields [1] . These do not provide correct predictions because agriculture has a dynamic nature and complexity involved. The use of machine learning has emerged as a breakthrough to support decision-making with a focus on enhancing crop yield predictions. Through processing huge amounts of data, ML algorithms can identify patterns, analyse environmental factors, and provide better predictions than conventional methods [3],[18],[21] . This paper explains how machine learning has been utilized in crop yield prediction using different ML methods, techniques to obtain data, and preprocessing techniques. It also discusses challenges, practical applications,  \nethics, and future directions in precision agriculture. The objective is to provide a holistic overview of how ML has changed farm practices in recent years, increasing productivity, reducing wastage, and making agriculture sustainable [4] .  \nAdvances in machine learning and crop simulation modeling have opened up new avenues for agricultural prediction. These technologies have each brought distinct capabilities and considerable gains in prediction performance; however, they have been primarily evaluated independently, and there may be benefits in integrating them to further increase prediction accuracy [7] .  \n2. Literature Review  \n2.1 Historical Yield Prediction Techniques  \nAccording to Kallenberg et al., 2023[6], Previous crop production forecast techniques were predominantly multiple linear regression model","cbCaiqNJjZ4Dp4jZ","https://ap.wps.com/l/cbCaiqNJjZ4Dp4jZ","pdf",658252,1,11,"English","en",105,"# Introduction\n# Literature Review\n## Historical Yield Prediction Techniques\n## Deficiencies in Conventional Methods\n## Development in Machine Learning in Agriculture\n## Recent developments in Machine Learning for Crop Yield","[{\"question\":\"What problem does the paper address in agriculture forecasting?\",\"answer\":\"The paper targets inaccurate crop yield predictions caused by agriculture’s dynamic and complex nature, where many interacting factors influence production.\"},{\"question\":\"Which factors are identified as most influential for building better prediction models?\",\"answer\":\"Rainfall, temperature, and pesticide are highlighted as the most influential factors for improving prediction performance.\"},{\"question\":\"Which machine learning methods are discussed and evaluated for crop yield prediction?\",\"answer\":\"The paper covers Decision Trees, Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes, and Long Short-Term Memory (LSTM), checking their effectiveness.\"}]","Machine Learning Based Method for Forecasting Crop Yield | 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problem does the paper address in agriculture forecasting?","Question",{"text":75,"@type":76},"The paper targets inaccurate crop yield predictions caused by agriculture’s dynamic and complex nature, where many interacting factors influence production.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which factors are identified as most influential for building better prediction models?",{"text":80,"@type":76},"Rainfall, temperature, and pesticide are highlighted as the most influential factors for improving prediction performance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning methods are discussed and evaluated for crop yield prediction?",{"text":84,"@type":76},"The paper covers Decision Trees, Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes, and Long Short-Term Memory (LSTM), checking their 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