[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-608438-105":59,"doc-detail-608438-en":134},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":127,"head_meta":129,"extra_data":131,"updated_unix":133},105,"en","artificial-intelligence-in-rice-quality-and-milling-technologies-applications-and-future-prospects","Artificial Intelligence in Rice Quality and Milling - Technologies, Applications, and Future Prospects","","The global demand for high-quality rice requires milling technologies and quality assessment methods that are rapid, accurate, and scalable. Conventional rice evaluation is often time-consuming and subjective, so many studies replace it with artificial-intelligence systems that enable non-destructive, real-time monitoring. This review synthesizes current AI approaches—machine vision, deep learning, spectroscopy, thermal imaging, and hyperspectral imaging—across processing stages. It emphasizes CNNs, YOLO, and Mask R-CNN integration into industrial milling, and outlines next steps including lean edge architectures, hybrid imaging, and open datasets. Reported results show high classification accuracy (often ≥90%) and strong physicochemical prediction fits (R² ≈ 0.9–0.99).",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/artificial-intelligence-in-rice-quality-and-milling-technologies-applications-and-future-prospects/608438/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/artificial-intelligence-in-rice-quality-and-milling-technologies-applications-and-future-prospects/608438.png","ImageObject",300,407,{"name":92,"@type":93},"Olivia Brown","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-10-17",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"Why is artificial intelligence used for rice quality assessment?","Question",{"text":112,"@type":113},"Conventional rice evaluation is time-consuming and subjective, while AI enables non-destructive, real-time monitoring that can be faster, more accurate, and scalable for milling operations.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which AI methods are highlighted for assessing rice quality?",{"text":117,"@type":113},"The review highlights machine vision and deep learning, including CNNs, YOLO architectures, and Mask R-CNN, along with spectroscopy, thermal imaging, and hyperspectral imaging.",{"name":119,"@type":110,"acceptedAnswer":120},"What performance levels are reported for AI-based rice classification?",{"text":121,"@type":113},"Across recent rice-focused studies, classification accuracies for grading and varietal identification are typically at least 90% using machine vision and CNNs, and NIR-ANN models often report strong fits for physicochemical properties with R² roughly 0.9–0.99.",{"name":123,"@type":110,"acceptedAnswer":124},"What future directions are proposed for deploying AI in rice milling?",{"text":125,"@type":113},"Next steps include designing lean AI architectures for edge computing, developing hybrid imaging systems, and creating open-access datasets to support broader industrial and research deployment.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},608438,1793050908,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149},16904993612988,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Artificial Intelligence in Rice Quality and Milling:  \nTechnologies, Applications, and Future Prospects .  \nILO, Benjamin, BADJONA, Abraham \u003C [http://orcid.org/0000-0003-4759-7572](http://orcid.org/0000-0003-4759-7572)>, SINGH, Yogang \u003C [http://orcid.org/0000-0002-7352-8936](http://orcid.org/0000-0002-7352-8936)>, SHENFIELD, Alex \u003C [http://orcid.org/0000-0002-2931-8077](http://orcid.org/0000-0002-2931-8077)> and ZHANG, Hongwei  \n\u003C [http://orcid.org/0000-0002-7718-021X](http://orcid.org/0000-0002-7718-021X)>  \nAvailable from Sheffield Hallam University Research Archive (SHURA) at: [https://shura.shu.ac.uk/36432/](https://shura.shu.ac.uk/36432/)  \nThis document is the Published Version [VoR]  \nCitation:  \nILO, Benjamin, BADJONA, Abraham, SINGH, Yogang, SHENFIELD, Alex and ZHANG, Hongwei (2025) . Artificial Intelligence in Rice Quality and Milling:  \nTechnologies, Applications, and Future Prospects. Processes, 13 (11): 3731. [Article]  \nCopyright and re-use policy  \nSee [http://shura.shu.ac.uk/information.html](http://shura.shu.ac.uk/information.html)  \nSheffield Hallam University Research Archive  \n[http://shura.shu.ac.uk](http://shura.shu.ac.uk)  \nReview  \nArtificial Intelligence in Rice Quality and Milling: Technologies, Applications, and Future Prospects  \nBenjamin Ilo 1,*, Abraham Badjona 1, Yogang Singh 2, Alex Shenfield 1,2 and Hongwei Zhang 1, *  \nAcademic Editors: Dariusz Dziki and Xiaosong Du  \nReceived: 16 September 2025  \nRevised: 27 October 2025  \nAccepted: 13 November 2025  \nPublished: 19 November 2025  \nCitation: Ilo, B.; Badjona, A.; Singh, Y.; Shenfield, A.; Zhang, H. Artificial Intelligence in Rice Quality and Milling: Technologies, Applications, and Future Prospects. Processes 2025, 13, 3731. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)pr13113731  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Advanced Food Innovation Centre (AFIC), Sheffield Hallam University, Sheffield S9 2AA, UK; [a.badjona@shu.ac.uk](a.badjona@shu.ac.uk) (A.B.); [a.shenfield@shu.ac.uk](a.shenfield@shu.ac.uk) (A.S.)  \n2 School of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK; [y.singh@shu.ac.uk](y.singh@shu.ac.uk)  \n* [Correspondence: b.ilo@shu.ac.uk](Correspondence: b.ilo@shu.ac.uk) (B.I.); [h.zhang@shu.ac.uk](h.zhang@shu.ac.uk) (H.Z.)  \nAbstract  \nThe global demand for high-quality rice necessitates advancements in milling technologies and quality assessment techniques that are rapid, accurate, and scalable. Traditional methods of rice evaluation are time-consuming and subjective, and are increasingly being replaced by artificial intelligence driven solutions that offer non-destructive, real-time monitoring capabilities. This review presents a comprehensive synthesis of current AI applications including machine vision, deep learning, spectroscopy, thermal imaging, and hyperspectral imaging for the assessment and classification of rice quality across various stages of processing. Major emphasis is put on the recent advances in convolutional neural networks (CNNs), YOLO architectures, and Mask R-CNN models, and their integration into industrial rice milling systems is discussed. Additionally, the review highlights next steps, notably designing lean AI architectures suitable for edge computing, hybrid imaging systems, and the creation of open-access datasets. Across recent rice-focused studies, classification accuracies for grading and varietal identification are typically ≥90% using machine vision and CNNs, while NIR–ANN models for physicochemical properties (e.g., moisture/protein proxies) commonly report strong fits (R2 ≈ 0.9–0.99) . End-to-end detectors/segmenters (e.g., YOLO/YO-L","cbCaidSAVtIOdkcV","https://ap.wps.com/l/cbCaidSAVtIOdkcV","pdf",1841383,35,"English","# Abstract\n# Keywords\n# 1. Introduction\n## 1.1. Aim and Scope of the Study","[{\"question\":\"Why is artificial intelligence used for rice quality assessment?\",\"answer\":\"Conventional rice evaluation is time-consuming and subjective, while AI enables non-destructive, real-time monitoring that can be faster, more accurate, and scalable for milling operations.\"},{\"question\":\"Which AI methods are highlighted for assessing rice quality?\",\"answer\":\"The review highlights machine vision and deep learning, including CNNs, YOLO architectures, and Mask R-CNN, along with spectroscopy, thermal imaging, and hyperspectral imaging.\"},{\"question\":\"What performance levels are reported for AI-based rice classification?\",\"answer\":\"Across recent rice-focused studies, classification accuracies for grading and varietal identification are typically at least 90% using machine vision and CNNs, and NIR-ANN models often report strong fits for physicochemical properties with R² roughly 0.9–0.99.\"},{\"question\":\"What future directions are proposed for deploying AI in rice milling?\",\"answer\":\"Next steps include designing lean AI architectures for edge computing, developing hybrid imaging systems, and creating open-access datasets to support broader industrial and research deployment.\"}]","Artificial Intelligence in Rice Quality and Milling - Technologies, Applications, and Future Prospects | PDF",1792256013,88]