[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117975-en":3,"doc-seo-117975-105":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},117975,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Predicting Agricultural Commodities Prices with Machine Learning - A Review of Current Research","Agricultural price prediction plays a decisive role for farmers, policymakers, and related stakeholders, yet remains difficult because agricultural markets are complex, dynamic, and influenced by multiple interacting factors. Machine learning can strengthen forecasting accuracy and enable real-time, customizable predictions that integrate with decision-making. This review summarizes recent studies on machine learning approaches for agricultural price prediction, evaluates their strengths and weaknesses, highlights key challenges, and concludes that further work is needed to address remaining limitations.","Predicting Agricultural Commodities Prices with Machine Learning: A Review of Current Research  \nNhat-Quang Tran†  \nSchool of Science, Engineering, and Technology, RMIT University Vietnam [quang.tran26@rmit.edu.vn](quang.tran26@rmit.edu.vn)  \nThanh Nguyen Ngoc  \nSchool of Science, Engineering, and Technology, RMIT University Vietnam [thanh.nguyenngoc@rmit.edu.vn](thanh.nguyenngoc@rmit.edu.vn)  \nQuang Tran  \nSchool of Science, Engineering, and Technology, RMIT University Vietnam [quang.tran@rmit.edu.vn](quang.tran@rmit.edu.vn)  \nAnna Felipe  \nSchool of Science, Engineering, and Technology, RMIT University Vietnam [anna.felipe@rmit.edu.vn](anna.felipe@rmit.edu.vn)  \nTom Huynh  \nSchool of Science, Engineering, and Technology, RMIT University Vietnam [tom.huynh@rmit.edu.vn](tom.huynh@rmit.edu.vn)  \nArthur Tang  \nSchool of Science, Engineering, and Technology, RMIT University Vietnam [arthur.tang@rmit.edu.vn](arthur.tang@rmit.edu.vn)  \nThuy Nguyen  \nSchool of Science, Engineering, and Technology, RMIT University Vietnam [thuy.nguyen43@rmit.edu.vn](thuy.nguyen43@rmit.edu.vn)  \nABSTRACT  \nAgricultural price prediction is crucial for farmers, policymakers, and other stakeholders in the agricultural sector. However, it is a challenging task due to the complex and dynamic nature of agricultural markets. Machine learning algorithms have the potential to revolutionize agricultural price prediction by improving accuracy, real-time prediction, customization, and integration.  \nThis paper reviews recent research on machine learning algorithms for agricultural price prediction. We discuss the importance of agriculture in developing countries and the problems associated with crop price falls. We then identify the challenges of predicting agricultural prices and highlight how machine learning algorithms can support better prediction. Next, we present a comprehensive analysis of recent research, discussing the strengths and weaknesses of various machine learning techniques. We conclude that machine learning has the potential to revolutionize agricultural price prediction, but further research is essential to address the limitations and challenges associated with this approach.  \nKEYWORDS  \nAgricultural price prediction, PRISM, machine learning, deep learning.  \n† Corresponding author  \n1 Introduction  \nAgriculture is a vital sector that plays a crucial role in the economic development of countries. It provides a source of livelihood for a significant portion of the population [1, 2], especially in developing countries, where it is often the backbone of the economy. Agriculture contributes to employment , income generation, and food security for those countries [3-5] . It also provides the raw materials for a variety of industries, including food processing, textiles, and biofuels [6, 7] .  \nAgricultural commodity price instability can harm a country's GDP and cause emotional and financial distress to farmers who have invested years of effort [8] . Price forecasting can help the agriculture supply chain make informed decisions and mitigate the risks of price fluctuations [9] . By predicting future prices, farmers and other stakeholders can adjust their production and marketing strategies, accordingly, leading to better outcomes for all parties involved. Thus, accurate agricultural product price prediction is essential [8, 10, 11] .  \nDespite its great potential, agriculture price prediction is challenging due to the complex and dynamic nature of the agricultural market, which is influenced by a wide range offactors, including weather variability, supply and demand dynamics, market interdependencies, data availability, and the complexities of agrarian systems [12-20] .  \nMachine learning algorithms have the potential to  \nrevolutionize agricultural price prediction [21, 22] by improving accuracy, real-time prediction, customization, and  \nintegration. In this study , we systematically review the stateof-the-art research in agriculture price predic","cbCaijsHRluU2ioZ","https://ap.wps.com/l/cbCaijsHRluU2ioZ","pdf",541078,1,7,"English","en",105,"# Abstract\n# Introduction\n# Research methodology\n## PRISMA-based review process\n## Search, screening, and selection\n# Findings","[{\"question\":\"Why is agricultural commodities price prediction considered challenging?\",\"answer\":\"Agricultural prices are hard to forecast due to complex and dynamic market behavior, including weather variability, supply-demand interactions, market interdependencies, and data constraints.\"},{\"question\":\"How does this review identify relevant research papers?\",\"answer\":\"The review follows the PRISMA framework, searches major scholarly indexes, screens abstracts to remove irrelevant topics, and applies citation- and recency-based inclusion rules.\"},{\"question\":\"What does the review conclude about machine learning for price prediction?\",\"answer\":\"Machine learning has strong potential to improve agricultural price forecasting, but additional research is necessary to address current limitations and challenges.\"}]","Predicting Agricultural Commodities Prices with Machine Learning - A Review of Current Research | PDF",1785680602,18,{"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},"predicting-agricultural-commodities-prices-with-machine-learning-a-review-of-current-research","",{"@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/predicting-agricultural-commodities-prices-with-machine-learning-a-review-of-current-research/117975/",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","2026-08-02",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 agricultural commodities price prediction considered challenging?","Question",{"text":75,"@type":76},"Agricultural prices are hard to forecast due to complex and dynamic market behavior, including weather variability, supply-demand interactions, market interdependencies, and data constraints.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does this review identify relevant research papers?",{"text":80,"@type":76},"The review follows the PRISMA framework, searches major scholarly indexes, screens abstracts to remove irrelevant topics, and applies citation- and recency-based inclusion rules.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the review conclude about machine learning for price prediction?",{"text":84,"@type":76},"Machine learning has strong potential to improve agricultural price forecasting, but additional research is necessary to address current limitations and challenges.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]