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 Digital Soil Texture Classification Using Machine Learning Approaches Subject The topic of the resource Feature extraction; Image processing; Neural network; Soil texture classification; Support vector machines Description An account of the resource The texture of the soil is an important factor to consider during cultivation. The water transmission property is being regulated by the texture of the soil. To determine sand, silt and clays percentage present in a soil sample, a conventional laboratory method is used, which consumes more time. Digitization in agriculture has given a new direction of innovative research in agriculture domain. In this paper, based on image processing an efficient model has been developed for soil texture classification. Eight different image preprocessing techniques were used for the image enhancement. Out of that, the linear contrast adjustment found to be best in image enhancement. A feature vector was calculated by extracting six different features from the enhanced image. The feature vector of an image is input to the machine learning classifier. The various classifiers used in this research work are SVM, KNN, ANN and PNN. The accuracy of the classifiers was SVM (0.98), KNN (0.89), ANN (0.89) and PNN (0.86). From the result, it is found SVM model has higher rate in classification of soil. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Sharmila G.; Rajamohan K. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-1, pp. 133-144. 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/10.1007/978-981-99-5015-7_12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-5015-7_12</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200410154&amp;doi=10.1007%2F978-981-99-5015-7_12&amp;partnerID=40&amp;md5=95bee10db245da83e37fe543ad68bcde" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200410154&amp;doi=10.1007%2f978-981-99-5015-7_12&amp;partnerID=40&amp;md5=95bee10db245da83e37fe543ad68bcde</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981995014-0 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 Sharmila G., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Rajamohan K., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India