[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-125146-105":3,"detail-sidebar-cat-0-en-105":80,"doc-detail-125146-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","bayesian-machine-learning-approach-for-corn-yield-prediction-using-satellite-imagery-and-topographic-data","Bayesian Machine Learning Approach for Corn Yield Prediction Using Satellite Imagery and Topographic Data","","In an era of climate change and growing global food demand, accurate crop yield prediction is pivotal for enabling data-driven crop management and sustainability. This study compares several Bayesian machine learning methods using high-resolution PlanetScope satellite imagery and topographic data. Bayesian Linear Regression, Bayesian Random Forest, Bayesian Splines, Bayesian Additive Regression Trees, and Bayesian Neural Network models are developed to incorporate uncertainty quantification while targeting improved predictive accuracy. Results indicate that Bayesian Random Forest achieves the strongest yield prediction performance.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/bayesian-machine-learning-approach-for-corn-yield-prediction-using-satellite-imagery-and-topographic-data/125146/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/bayesian-machine-learning-approach-for-corn-yield-prediction-using-satellite-imagery-and-topographic-data/125146.png","ImageObject",300,407,{"name":42,"@type":43},"Levi","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-26","2026-08-05",true,{"@type":52,"interactionType":53,"userInteractionCount":33},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"Which Bayesian machine learning models are used for corn yield prediction?","Question",{"text":62,"@type":63},"The study develops Bayesian Linear Regression, Bayesian Random Forest, Bayesian Splines, Bayesian Additive Regression Trees, and Bayesian Neural Network models.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"What input data sources drive the yield prediction?",{"text":67,"@type":63},"The models use high-resolution PlanetScope imagery together with topographic data.",{"name":69,"@type":60,"acceptedAnswer":70},"Which method performs best for predicting corn yield?",{"text":71,"@type":63},"Bayesian Random Forest outperforms the other compared models in corn yield prediction performance.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},125146,1790076823,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":33,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":22,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":30},5909887256941,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Bayesian Machine Learning Approach for Corn Yield Prediction Using Satellite Imagery and Topographic Data  \nEtornam Kwame Kunu 1 and Hossien Moradi Rekabdarkolaee2  \n1,2 Department of Mathematics and Statistics, SDSU, Brookings, South  \nDakota, USA  \nAbstract  \nIn an era of climate change and growing global food demand, accurate crop yield prediction is pivotal for leveraging advanced technologies to enhance crop management and sustainability. This study compares the prediction performance of several Bayesian Machine Learning method using high-resolution PlanetScope imagery and topographic data. In specific, the Bayesian Linear Regression, Bayesian Random Forest, Bayesian Splines, Bayesian Additive Regression Trees, and Bayesian Neural Network were developed to incorporate uncertainty quantification and achieve enhanced predictive accuracy. Our finding shows that the Bayesian Random Forest outperform the other model in term of crop yield prediction.  \nKeywords: Bayesian, Machine Learning, Yield Prediction, PlanetScope Imagery, Topographic Data","cbCaiocRK0L5qoTR","https://ap.wps.com/l/cbCaiocRK0L5qoTR","pdf",67862,"English","# Abstract\n## Methods\n## Results\n## Keywords","[{\"question\":\"Which Bayesian machine learning models are used for corn yield prediction?\",\"answer\":\"The study develops Bayesian Linear Regression, Bayesian Random Forest, Bayesian Splines, Bayesian Additive Regression Trees, and Bayesian Neural Network models.\"},{\"question\":\"What input data sources drive the yield prediction?\",\"answer\":\"The models use high-resolution PlanetScope imagery together with topographic data.\"},{\"question\":\"Which method performs best for predicting corn yield?\",\"answer\":\"Bayesian Random Forest outperforms the other compared models in corn yield prediction performance.\"}]","Bayesian Machine Learning Approach for Corn Yield Prediction Using Satellite Imagery and Topographic Data | PDF",1785896925]