Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Arabica Coffee Bean Grading into Specialty and Commodity Type Based on Quality Using Visual Inspection Subject The topic of the resource K nearest neighbors; Object localization; Primary and secondary defects; Specialty coffee; Transfer learning Description An account of the resource Expanding potential of coffee consumers to seek out the freshest and best flavors is a cause for the rise of specialty coffee inthe market. Specialty coffee is grown and harvestedmaintaining an emphasis on quality and clarity of flavor, whereas commodity coffee is harvested for caffeine content. Within those inclusive categories, arabica and robusta are the two types of main branches of coffee that weencounter in the coffee market. Specialty coffees differ significantly from conventional coffees in that they are cultivated at higher altitudes, can be traced, and are professionally processed after being harvested. The quality is constantly examined and understood at every stage, from growth to brewing. Green arabica quality is assessed by counting the defective beans present in the sample. These defects can be primary (Category I) or secondary (Category II). If the primary defects are null and less than five secondary defects, coffee is said to be a specialty.Prior research has been done on classifying the coffee species and differentiating good beans from bad beans. Our research involves the combination of machine learning like K-NN and deep learning convolutional neural networks for classifying specialty coffee from commodity type using computer vision. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Pragathi S.P.; Jacob L. Source A related resource from which the described resource is derived Lecture Notes in Electrical Engineering, Vol-1096, pp. 13-20. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-99-7137-4_2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-7137-4_2</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185707056&amp;doi=10.1007%2F978-981-99-7137-4_2&amp;partnerID=40&amp;md5=260a3bc9e17abc842cbe879e3a48a23e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185707056&amp;doi=10.1007%2f978-981-99-7137-4_2&amp;partnerID=40&amp;md5=260a3bc9e17abc842cbe879e3a48a23e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18761100; ISBN: 978-981997136-7 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Pragathi S.P., Department of Data Science, Christ University, Bangalore, India; Jacob L., Department of Data Science, Christ University, Bangalore, India