[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117743-en":3,"doc-seo-117743-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117743,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Classification of arecanut using machine learning techniques - Research report","This paper presents a machine-vision and machine-learning approach to classify good and defective arecanuts using measurable visual cues. Quality grading is addressed as an alternative to manual sorting by leveraging color, texture, and density-related information extracted from images. The study evaluates multiple classifiers and shows that artificial neural networks outperform logistic regression, k-nearest neighbor, naive Bayes, and support vector machine. A density feature is included and results are compared with models that omit it, reaching 98.8% accuracy.","Classification of arecanut using machine learning techniques  \nShabari Shedthi Billadi1, Madappa Siddappa2, Surendra Shetty3, Vidyasagar Shetty4  \n1Department of Computer Science and Engineering, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University),  \nNitte, Karnataka, India  \n2Department Computer Science and Engineering, Sri Siddhartha Institute of Technology, Tumkur, Karnataka, India 3Department of Master of Computer Applications, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University),  \nNitte, Karnataka, India  \n4Department of Mechanical Engineering, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University), Nitte,  \nKarnataka, India  \nArticle history:  \nReceived Feb 3, 2022 Revised Oct 6, 2022 Accepted Nov 1, 2022  \nKeywords:  \nAgriculture Arecanut Classifying Image processing Machine learning  \nCorresponding Author:  \nIn agricultural domain research, image processing and machine learning techniques play an important role. This paper provides a unique solution for classifying the good and defective arecanuts based on their color, texture, and density value. In the market different varieties of arecanut are available. Usually, qualitative sorting is done manually, and this can be replaced by applying machine vision techniques to grade the arecanut. Classification of arecanut based on quality is done using various machine learning techniques and it is observed that artificial neural networks give good results compared to other classifiers like logistic regression, k-nearest neighbor, naive Bayes classifiers, and support vector machine. A unique density feature is considered here for better classification. The result of classifiers without considering the density feature is compared with respect to the density feature and it is observed that artificial neural networks work better than the others. The proposed method works effectively for classifying arecanut with an accuracy of 98.8% .  \nThis is an open access article under the CC BY-SA license.  \nShabari Shedthi Billadi  \nDepartment of Computer Science and Engineering, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University)  \nNitte 574110, Karnataka, India  \nEmail: [shabarishetty87@gmail.com](shabarishetty87@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe use of technology in agriculture was initially used for simple and precise calculations, which were found to be relatively difficult in manual calculations. In the next generation, research decision support systems will be developed to take tactical decisions on agricultural production and protection. Arecanut is a major cash crop in the undivided Dakshina Kannada district and Malnad region. Areca Catechu Linn is the scientific name of the arecanut and it is also called betelnut in India. In India, the cultivation and use of arecanut have their own unique practice [1] . In the food processing industry nowadays, there is a requirement for the production of quality products at a very fast rate, so developing an expert system helps to make decisions in less time. In manual grading, individual person perception makes differences in identifying whether a product is defective or healthy, but this machine vision framework will decrease such human errors and help to perform at a faster rate.  \nArecanut is used for making supari, areca tea, and paint. It has its own value in several religious ceremonies. In the Indian’s ancient medicine system book of i.e., Dhanwantari Nighantu, it mentioned the use of arecanut as one of the five natural aromatics (panchasugandhikam) along with pepper, clove, nutmeg,  \nand camphor. In the Indian subcontinent, the chewing of betel leaf and arecanuts back to the pre-Vedic period of the Harappan Empire [2] . The beginning of the arecanut cannot be traced exactly, but in the Philippines or Malaysia, it probably originated. Usage of nuts for chewing initially started in Vietnam and Malaysia. Then it moved to other pa","cbCaihyqHYeuXZ5s","https://ap.wps.com/l/cbCaihyqHYeuXZ5s","pdf",581637,1,"English","en",105,"# Abstract\n# Introduction\n## Background and agricultural importance\n## Manual grading vs machine vision\n# Image processing and segmentation\n## Color spaces and preprocessing\n# Methods and classification (implied)","[{\"question\":\"How does the paper classify good versus defective arecanuts?\",\"answer\":\"It uses image processing to extract visual information and applies machine learning classifiers to distinguish good and defective arecanuts based on color, texture, and density-related features.\"},{\"question\":\"Which classifier performs best in the study?\",\"answer\":\"Artificial neural networks provide the best results compared with logistic regression, k-nearest neighbor, naive Bayes, and support vector machine.\"},{\"question\":\"Why is the density feature important?\",\"answer\":\"The paper incorporates a unique density feature and compares results with models that exclude it; including density improves performance, with neural networks achieving the strongest outcomes.\"}]","Classification of arecanut using machine learning techniques - Research report | PDF",1785679305,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"classification-of-arecanut-using-machine-learning-techniques-research-report","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/classification-of-arecanut-using-machine-learning-techniques-research-report/117743/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"How does the paper classify good versus defective arecanuts?","Question",{"text":74,"@type":75},"It uses image processing to extract visual information and applies machine learning classifiers to distinguish good and defective arecanuts based on color, texture, and density-related features.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which classifier performs best in the study?",{"text":79,"@type":75},"Artificial neural networks provide the best results compared with logistic regression, k-nearest neighbor, naive Bayes, and support vector machine.",{"name":81,"@type":72,"acceptedAnswer":82},"Why is the density feature important?",{"text":83,"@type":75},"The paper incorporates a unique density feature and compares results with models that exclude it; 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