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 Articles 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/. Title A name given to the resource A Study on Experimental Analysis of Best Fit Machine Learning Approach for Smart Agriculture Subject The topic of the resource Agriculture; AI techniques; Best fit; CNN; Deep learning; KNN; Machine learning; Random forest; SVM Description An account of the resource By 2050, the population is projected to exceed nine billion, necessitating a 70% increase in agricultural output to meet the need. Land, water, and other resources are running out due to the growing world population, making it impossible to maintain the demandsupply cycle. The yield of cultivation is also declining as a result of people's ignorance of the growing crop illnesses. Given that food is the most basic human requirement, future research should focus on revitalizing the agricultural sector. Farming may be made more productive for farmers by applying the right artificial intelligence technologies and datasets. Agronomics can benefit greatly from artificial intelligence. So that we can farm more effectively and be as productive as possible, we need to adopt a better strategy. The objective of this paper is to experimentally analyze the machine learning algorithms and methods already in use and forecast the most effective approach to use in each agricultural sector. In this article, we will present the challenges farmers face when using traditional farming methods and how artificial intelligence is revolutionizing agriculture by replacing the traditional methods. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Lovesum J.; Prince B. Source A related resource from which the described resource is derived SN Computer Science, Vol-4, No. 2 Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s42979-022-01612-0" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-022-01612-0</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147100158&amp;doi=10.1007%2Fs42979-022-01612-0&amp;partnerID=40&amp;md5=c230f455115f744ce701c12294f5f848" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147100158&amp;doi=10.1007%2fs42979-022-01612-0&amp;partnerID=40&amp;md5=c230f455115f744ce701c12294f5f848</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2662995X 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 Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Lovesum J., Christ University, Bangalore, India; Prince B., Presidency University, Bangalore, India