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 Book Chapter Book Chapter Faculty Publications- Book Chapter 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 Agricultural Crop-Yield Prediction: Comparative Analysis Using Machine Learning Models Description An account of the resource Machine learning (ML) is a crucial decision-support tool for predicting agricultural crop yields, enabling choices about which crops to grow and what to do while they are in the growing season. The research on agricultural production prediction has been supported by the application of several ML techniques. We employed a comparative analysis in this study to synthesize using three ML models, including linear regression, polynomial regression, and K-nearest neighbors (KNN), and extracted the results for the prediction of yield. Crop yield depends on a variety of aspects such as temperature, pesticide usage, rainfall, and even year due to changing climatic conditions. It is in our best interest to find out the crop yield based on these factors, as it will help in advancing the farming sector. These collected data have gone through preprocessing - i.e., cleaning, to ensure that no redundant or error data is used to train the ML models. Before we train the models, the dataset is divided into training and testing to provide the performance metrics of each model we use. The experimental results on predictions indicate KNN performs slightly better in comparison with linear regression and polynomial regression models. 2024 Taylor &amp; Francis Group, LLC. Creator An entity primarily responsible for making the resource Kumar K.P.; Kumar S.B.; Gupta A.D.; Johnson K.; Michael M.M. Source A related resource from which the described resource is derived Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture, pp. 158-177. Publisher An entity responsible for making the resource available CRC Press 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/10.1201/9781003485179-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003485179-9</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195964217&amp;doi=10.1201%2F9781003485179-9&amp;partnerID=40&amp;md5=81d539a5bc56644229f8c0660c805814" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195964217&amp;doi=10.1201%2f9781003485179-9&amp;partnerID=40&amp;md5=81d539a5bc56644229f8c0660c805814</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-104003723-2; 978-103261892-0 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 Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar K.P., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Kumar S.B., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Gupta A.D., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Johnson K., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Michael M.M., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India