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 Evaluation of Remote Sensing and Meteorological parameters for Yield Prediction of Sugarcane (Saccharum officinarum L.) Crop Subject The topic of the resource Farming; MLR; NDVI; Prediction; Sugarcane; VCI; Yield Description An account of the resource In the Agriculture sector, the farmers need a reliable estimation for pre-harvest crop yield prediction to decide their import-export policies. The present work aims to assess the impact of remote sensing-based derived products with Climate data on the accuracy of a prediction model for the sugarcane yield. The regression method was used to develop an empirical model based on VCI, Historical Sugarcane Yield, and Climatic Parameters of 75 districts of six major sugar-producing states of India. The MOD13Q1 product of MODIS on Board Terra Satellite at 16-day intervals was accessed during the growing season of sugarcane crop with 36 meteorological parameters for experimentation. The accuracy of the model was evaluated using R2, Root Mean square Metric (RMSE), Mean Absolute Error (MAE), and mean square error (MSE). The preliminary results concluded that the proposed methodology achieved the highest accuracy with (R2 =0.95, MAE=5.18, MSE=34.5, RMSE=5.87). The conclusion of the study highlighted that the coefficient of determination can be improved significantly by incorporating maximum and minimum temperature parameters with Remote sensing derived vegetation indices for the sugarcane yield. 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY NC) license (https://creativecommons.org/licenses/by-nc/4.0/). Creator An entity primarily responsible for making the resource Saini P.; Nagpal B.; Garg P.; Kumar S. Source A related resource from which the described resource is derived Brazilian Archives of Biology and Technology, Vol-66 Publisher An entity responsible for making the resource available Instituto de Tecnologia do Parana 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/10.1590/1678-4324-2023220781" target="_blank" rel="noreferrer noopener">https://doi.org/10.1590/1678-4324-2023220781</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168841686&amp;doi=10.1590%2F1678-4324-2023220781&amp;partnerID=40&amp;md5=465968558182a69e1fe4c7744a71e510" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168841686&amp;doi=10.1590%2f1678-4324-2023220781&amp;partnerID=40&amp;md5=465968558182a69e1fe4c7744a71e510</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 15168913 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 Saini P., NSUT East Campus (Formerly AIACTR), Guru Gobind Singh Indraprastha University, USICT, Delhi, India; Nagpal B., NSUT East Campus (Formerly AIACTR), Department of Computer Science and Engineering, Delhi, India; Garg P., Christ University, School of Sciences, Delhi, India; Kumar S., South Ural State University, Big Data &amp; Machine Learning Lab, Chelyabinsk, Russian Federation