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 Wheat Yield Prediction using Temporal Fusion Transformers Subject The topic of the resource Crop Yield Prediction; Deep Learning; Machine Learning; Temporal Fusion Transformers; Time Series Forecasting Description An account of the resource In precision framing, Machine Learning models are an essential decision-making tool for crop yield prediction. They aid farmers with decisions like which crop to grow and when to grow certain crops during the sowing season. Many Machine Learning algorithms have been used to support agriculture yield prediction research, but it is observed that Deep Learning models outperform the benchmark Machine Learning algorithms with a significant difference in accuracy. However, though these Deep Learning models perform better, they are not preferred or widely used in place of Machine Learning models. This is because Deep Learning methods are black box methods and are not interpretable, i.e., they fail to explain the magnitude of the impact of the features on the output, and this is unsuitable for our use case.In this paper, we propose using Temporal Fusion Transformer (TFT), a novel approach published by Google researchers for wheat yield prediction viewed as a Time Series Forecasting problem statement. TFT is the state-of-the-art attention-based Deep Learning architecture, which combines high-performance forecasting with interpretable insights and feature importance. We have used TFT to perform wheat yield prediction and compare its performance with various Machine Learning and Deep Learning algorithms. 2023 IEEE. Creator An entity primarily responsible for making the resource Junankar T.; Sondhi J.K.; Nair A.M. Source A related resource from which the described resource is derived 2023 2nd International Conference for Innovation in Technology, INOCON 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. 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/10.1109/INOCON57975.2023.10101144" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/INOCON57975.2023.10101144</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85157974107&amp;doi=10.1109%2FINOCON57975.2023.10101144&amp;partnerID=40&amp;md5=f711c3f13b878b46d98e74dcc0dd9c83" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85157974107&amp;doi=10.1109%2fINOCON57975.2023.10101144&amp;partnerID=40&amp;md5=f711c3f13b878b46d98e74dcc0dd9c83</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835032092-3 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 Junankar T., CHRIST(Deemed to Be University), Department of Data Science, Pune, Lavasa, India; Sondhi J.K., CHRIST(Deemed to Be University), Department of Data Science, Pune, Lavasa, India; Nair A.M., CHRIST(Deemed to Be University), Department of Data Science, Pune, Lavasa, India