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This study addresses rice variety classification and identification using distinguishing physical properties such as shape, texture, and colour. Traditional models (SVM, Random Forest, Decision Tree) are compared with a deep learning approach (Convolutional Neural Network, CNN) for classifying multiple rice types. A dataset containing 75,000+ grain images (15,000 per type) is used after preprocessing for feature extraction. Random Forest achieves the highest accuracy at 92.66%, followed by SVM (89.33%), Decision Tree (77.33%), and CNN (50.66%).",{"@graph":69,"@context":123},[70,84,106],{"@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/a-comparative-study-between-traditional-machine-learning-and-deep-learning-models-to-classify-rice-types-msc-research-project-submission-sheet/291108/",{"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/a-comparative-study-between-traditional-machine-learning-and-deep-learning-models-to-classify-rice-types-msc-research-project-submission-sheet/291108.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the project focus on?","Question",{"text":113,"@type":114},"The project focuses on classifying different rice types based on their visual physical characteristics such as shape, texture, and colour.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning models and deep learning model are compared?",{"text":118,"@type":114},"Traditional machine learning models compared include Support Vector Machines (SVM), Random Forest, and Decision Tree, and the deep learning model is a Convolutional Neural Network (CNN).",{"name":120,"@type":111,"acceptedAnswer":121},"What classification accuracies were reported for each model?",{"text":122,"@type":114},"Random Forest achieved 92.66% accuracy, SVM achieved 89.33%, Decision Tree achieved 77.33%, and CNN achieved 50.66%.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291108,1789643474,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","A Comparative study between Traditional Machine Learning and Deep Learning Models to classify Rice Types  \nMSc Research Project MSc in Data Analytics  \nZainab Maqsud Ghulam Hussain Mohamed  \nAli  \nStudent ID: x21135614  \nSchool of Computing National College of Ireland  \nSupervisor: Mohammed Hasanuzzaman  \nNational College of Ireland Project Submission Sheet School of Computing  \n| Student Name: | Zainab Maqsud Ghulam Hussain Mohamed Ali |\n| --- | --- |\n| Student ID: | x21135614 |\n| Programme: | MSc in Data Analytics |\n| Year: | 2022 |\n| Module: | MSc Research Project |\n| Supervisor: | Mohammed Hasanuzzaman |\n| Submission Due Date: | 15/12/2022 |\n| Project Title: | A Comparative study between Traditional Machine Learning and Deep Learning Models to classify Rice Types |\n| Word Count: | 6456 |\n| Page Count: | 19 |\n\nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \n\n| Signature: | Zainab Maqsud Ghulam Hussain Mohamad Ali |\n| --- | --- |\n| Date: | 1st February 2023 |\n\nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST:  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) . | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both foryour own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nA Comparative study between Traditional Machine Learning and Deep Learning Models to classify Rice  \nTypes  \nZainab Maqsud Ghulam Hussain Mohamed Ali  \nx21135614  \nAbstract  \nRice is one of the most commonly cultivated grains on the planet. When purchasing rice packets at the market, the physical appearance is the first thing that comes to mind for buyers. There are also rice varieties with numerous distinguishing traits. Typically, these attributes consist of shape, texture, and colour. On the basis of these distinguishing properties of the various types of rice, classification and identification of quality are possible. Machine learning models help facilitate the recognition of trends and patterns. Traditional machine learning models like Support Vector Machines (SVM), Random Forest and Decision Tree are compared with deep learning models like Convolutional Neural Network when it comes to classifying different types of rice (CNN) . Common types of rice used in this experiment include Arborio, Basmati, Ipsala, Jasmine, and Karacadag. Over 75,000 images of rice grains are included in the dataset, with 15,000 depicting each kind. Images were preprocessed to prepare them for feature extraction. Values for quantifying feature set performance were produced using these models. Based on the results, we can see that the Random Forest model had the highest classification accuracy at 92.66%, followed by the SVM model at 89.33%, the Decision Tree model at 77.33%, and the CNN model at 50 .66% . The classification increases with the inclusion of each new feature. On the basis of the obtained performance measurement results, it is feasible to conclude that the study was successful in identifying rice types.  \nKeywords —Rice Classification, Machine Leaning, Random Forest, S","cbCaicjCAwxt4SHZ","https://ap.wps.com/l/cbCaicjCAwxt4SHZ","pdf",1013578,21,"English","# Abstract\n# Keywords\n# 1 Introduction\n## Background and motivation for rice classification\n## Image processing and machine vision applications\n## Limitations of manual classification\n# References","[{\"question\":\"What problem does the project focus on?\",\"answer\":\"The project focuses on classifying different rice types based on their visual physical characteristics such as shape, texture, and colour.\"},{\"question\":\"Which machine learning models and deep learning model are compared?\",\"answer\":\"Traditional machine learning models compared include Support Vector Machines (SVM), Random Forest, and Decision Tree, and the deep learning model is a Convolutional Neural Network (CNN).\"},{\"question\":\"What classification accuracies were reported for each model?\",\"answer\":\"Random Forest achieved 92.66% accuracy, SVM achieved 89.33%, Decision Tree achieved 77.33%, and CNN achieved 50.66%.\"}]","A Comparative study between Traditional Machine Learning and Deep Learning Models to classify Rice Types - MSc Research Project Submission Sheet | PDF",53]