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 Faculty Publications Article Faculty Publications -Articles 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/. Creator An entity primarily responsible for making the resource Gaba, Ira; Ramamurthy, B. Title A name given to the resource A COMPUTATIONAL MODEL FOR TEA LEAF PRICE PREDICTION BASED ON QUALITY FACTORS USING HYBRID MACHINE LEARNING TECHNIQUES Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Theoretical and Applied Information Technology;Volume;103;Issue;10;pp.4285-4286 Identifier An unambiguous reference to the resource within a given context <a href="https://www.scopus.com/pages/publications/105006939611?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006939611?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gaba I., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Ramamurthy B., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource This document reflects the effort made to calculate and identify the grade of the tea leaves based on the assessment of the leaves' size and color. The leaves were classified based on their severity with the help of HSV. The leaves were further classified using the k prototypes clustering once their length and width were established. The leaves were then further categorized in line with that. Light, medium, and dark are the three-color categories into which it belongs. The leaves were further sorted according to their quality so that the farmer could sell the produce at a better price. With the machine learning method for the categorization part, we were able to show its values. All of the healthy leaves were considered in a different dataset, and the images were obtained using the feature selection method. The length and width of each individual leaf, along with its color and shape, were then measured using those leaves. We were able to differentiate between the various leaf grades based on the findings. The healthy leaves were separated from the diseased leaves using the textual features. Additionally, we were able to use the other criteria to obtain higher-grade leaves. Little Lion Scientific. Subject The topic of the resource Classification; Color Parameters; Feature Selection; HSV; Image Pre-Processing; K-Prototypes Clustering Publisher An entity responsible for making the resource available Little Lion Scientific Relation A related resource ISSN: 19928645; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online