[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124144-en":3,"doc-seo-124144-105":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},124144,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Optimizing potato crop productivity - a meteorological analysis and machine learning approach","A study investigates how weather conditions drive potato yield in Bangladesh amid climate change and resource constraints. Statistical analyses and machine-learning methods are combined to identify key meteorological variables affecting monthly production. ANOVA F regression and random forest models with feature-importance analysis isolate crucial factors, while Pearson and Spearman correlation with p-values quantify relationships between weather and yield. Seaborn visualization supports ideal harvesting conditions. Validated KNN, random forest, and SVR forecast future yield, with random forest achieving the highest reliability (R²=0.9990) and low error (MAPE=0.70, MAE=0.0803, RMSE=0.1114), supporting climate-smart agricultural decisions.","Optimizing potato crop productivity: a meteorological analysis  \nand machine learning approach  \nMd. Jiabul Hoque1,2, Md. Saiful Islam2, Abdullah Al Noman1, Md. Abrarul Hoque1, Irfan A. Chowdhury1, Mohammed Saifuddin1  \n1Department of Computer and Communication Engineering, International Islamic University Chittagong, Chattogram, Bangladesh 2Department of Electronics and Telecommunication Engineering, Chittagong University of Engineering and Technology,  \nChattogram, Bangladesh  \nArticle history:  \nReceived Dec 11, 2023 Revised Nov 9, 2024 Accepted Nov 14, 2024  \nKeywords:  \nCorrelation analysis  \nEarly yield forecasting Enhance potato production Feature importance analysis  \nFeature selection Machine learning Random forest  \nCorresponding Author:  \nMotivated by the critical need to enhance potato production in Bangladesh, particularly in the face of a changing climate, this study investigates the significant impact of weather on potato yield. This research employs various statistical and machine-learning approaches to identify key weather factors influencing potato crops. We utilize ANOVA F regression and random forest (RF) with feature importance analysis to pinpoint crucial monthly weather variables. Additionally, a correlation study employing Pearson's and Spearman's coefficients alongside p-values is conducted to determine the relationships between weather conditions and crop yield. Seaborn's bivariate kernel density estimation is then used to visualize ideal weather conditions for optimal harvests. Furthermore, to predict future yields, the study implements thoroughly trained and validated machine learning models including k-nearest neighbors (KNN), RF, and support vector regressor (SVR) . Our analysis reveals that the RF model emerges as the most reliable predictor, achieving a high correlation coefficient (R²=0.9990), and minimal error values (mean absolute percentage error (MAPE)=0.70, mean absolute error (MAE)=0.0803, and root mean square error (RMSE)=0.1114) . These findings provide valuable insights to guide informed agricultural decisions and climate-related strategies, particularly for resource-limited countries like Bangladesh.  \nThis is an open access article under the CC BY-SA license.  \nMd. Saiful Islam  \nDepartment of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chattogram, Bangladesh  \nEmail: [saiful05eee@cuet.ac.bd](saiful05eee@cuet.ac.bd)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nUnpredictable weather patterns and resource limitations in Bangladesh pose a significant barrier to the continuous and sustainable production of potatoes, a vital commodity for millions of people worldwide [1] . Securing food supply for the growing nation's population depends on overcoming these issues. Researchers must focus on the sustainable growth of potato farming and the creation of new technologies to increase yields due to a growing population and limited arable land [2] . Strategic planning and technical innovation are crucial to ensure sustainable food security and to improve agricultural production in the long term. Despite recent developments, accurately predicting potato production early in the growth phase is still essential. This knowledge enables farmers to make informed decisions about resource distribution, use proactive management techniques, reduce possible production losses, and improve food security [3] .  \nOngoing studies have investigated different techniques to forecast potato crop output. However, these endeavors often face constraints [4], [5] .  \nImprovements in potato yield forecasting are hindered by issues related to the precision and scope of the data. Contemporary studies frequently use past data to train machine learning algorithms, but encounter challenges due to the need for more data, limited coverage, and variations in data sources [6]–[8] . This study suggests improving the accuracy of the prediction in Bangladesh by collecting comprehen","cbCaidb2hyWxicDO","https://ap.wps.com/l/cbCaidb2hyWxicDO","pdf",814905,1,14,"English","en",105,"# Introduction\n## Weather impact on potato production\n## Challenges in early yield forecasting\n## Data sources and dataset construction\n## Study motivation and proposed approach","[{\"question\":\"Why is early potato yield prediction important in Bangladesh?\",\"answer\":\"Unpredictable weather and limited resources make late prediction too slow for effective planning. Early projections help farmers adjust irrigation and pest control, improve resource allocation, and reduce production losses.\"},{\"question\":\"Which weather variables are identified as most influential in the study?\",\"answer\":\"The research uses ANOVA F regression and random forest feature-importance analysis to pinpoint crucial monthly meteorological factors tied to potato yield.\"},{\"question\":\"How is the relationship between weather conditions and yield measured?\",\"answer\":\"Pearson and Spearman correlation coefficients are computed along with p-values to assess how specific weather conditions relate to crop yield.\"}]","Optimizing potato crop productivity - a meteorological analysis and machine learning approach | PDF",1785820687,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"optimizing-potato-crop-productivity-a-meteorological-analysis-and-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/optimizing-potato-crop-productivity-a-meteorological-analysis-and-machine-learning-approach/124144/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early potato yield prediction important in Bangladesh?","Question",{"text":75,"@type":76},"Unpredictable weather and limited resources make late prediction too slow for effective planning. Early projections help farmers adjust irrigation and pest control, improve resource allocation, and reduce production losses.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which weather variables are identified as most influential in the study?",{"text":80,"@type":76},"The research uses ANOVA F regression and random forest feature-importance analysis to pinpoint crucial monthly meteorological factors tied to potato yield.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the relationship between weather conditions and yield measured?",{"text":84,"@type":76},"Pearson and Spearman correlation coefficients are computed along with p-values to assess how specific weather conditions relate to crop yield.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]