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 Faculty Publications 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/. Creator An entity primarily responsible for making the resource Gulati, Bhavneet; Zubair, Zainab; Sinha, Ankita; Sinha, Nikita; Prasad, Nupoor; Semwal, Manoj Title A name given to the resource Predicting Nitrogen Flavanol Index (NFI) in Mentha arvensis Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Drones;Volume;9;Issue;7;Article No.;483; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/drones9070483" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/drones9070483</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011670146?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011670146?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gulati B., Computational Biology Department, CSIRCentral Institute of Medicinal and Aromatic Plants, Lucknow, 226015, India; Zubair Z., Computational Biology Department, CSIRCentral Institute of Medicinal and Aromatic Plants, Lucknow, 226015, India; Sinha A., Department of Electronics and Communication Engineering, Christ University, Bangalore, 560074, India; Sinha N., Department of Electronics and Communication Engineering, Christ University, Bangalore, 560074, India; Prasad N., Plant Protection and Production Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, 226015, India; Semwal M., Computational Biology Department, CSIRCentral Institute of Medicinal and Aromatic Plants, Lucknow, 226015, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India Description An account of the resource Crop growth monitoring at various growth stages is essential for optimizing agricultural inputs and enhancing crop yield. Nitrogen plays a critical role in plant development; however, its improper application can reduce productivity and, in the long term, degrade soil health. The aim of this study was to develop a non-invasive approach for nitrogen estimation through proxies (Nitrogen Flavanol Index) in Mentha arvensis using UAV-derived multispectral vegetation indices and machine learning models. Support Vector Regression, Random Forest, and Gradient Boosting were used to predict the Nitrogen Flavanol Index (NFI) across different growth stages. Among the tested models, Random Forest achieved the highest predictive accuracy (R2 = 0.86, RMSE = 0.32) at 75 days after planting (DAP), followed by Gradient Boosting (R2 = 0.75, RMSE = 0.43). Model performance was lowest during early growth stages (1530 DAP) but improved markedly from mid to late growth stages (4590 DAP). The findings highlight the significance of UAV-acquired data coupled with machine learning approaches for non-destructive nitrogen flavanol estimation, which can immensely contribute to improving real-time crop growth monitoring. 2025 by the authors. Subject The topic of the resource machine learning; Mentha arvensis; multispectral remote sensing; nitrogen; precision agriculture; UAV Publisher An entity responsible for making the resource available Multidisciplinary Digital Publishing Institute (MDPI) Relation A related resource ISSN: 2504446X; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online