ItemArticle

Predicting Nitrogen Flavanol Index (NFI) in Mentha arvensis Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture

Title
Predicting Nitrogen Flavanol Index (NFI) in Mentha arvensis Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture
Creator
Gulati, Bhavneet; Zubair, Zainab; Sinha, Ankita; Sinha, Nikita; Prasad, Nupoor; Semwal, Manoj
Description
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.
Source
Drones;Volume;9;Issue;7;Article No.;483;
Date
01-01-2025
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Subject
machine learning; Mentha arvensis; multispectral remote sensing; nitrogen; precision agriculture; UAV
Coverage
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
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 2504446X;
Format
online
Language
English
Type
Article

Citation

Gulati, Bhavneet; Zubair, Zainab; Sinha, Ankita; Sinha, Nikita; Prasad, Nupoor; Semwal, Manoj, “Predicting Nitrogen Flavanol Index (NFI) in Mentha arvensis Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23523.

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