Predicting Nitrogen Flavanol Index (NFI) in <i>Mentha arvensis</i> Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture
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 w...
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MDPI AG
2025-07-01
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| author | Bhavneet Gulati Zainab Zubair Ankita Sinha Nikita Sinha Nupoor Prasad Manoj Semwal |
| author_facet | Bhavneet Gulati Zainab Zubair Ankita Sinha Nikita Sinha Nupoor Prasad Manoj Semwal |
| author_sort | Bhavneet Gulati |
| collection | DOAJ |
| 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 <i>Mentha arvensis</i> 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 (R<sup>2</sup> = 0.86, RMSE = 0.32) at 75 days after planting (DAP), followed by Gradient Boosting (R<sup>2</sup> = 0.75, RMSE = 0.43). Model performance was lowest during early growth stages (15–30 DAP) but improved markedly from mid to late growth stages (45–90 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. |
| format | Article |
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| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Drones |
| spelling | doaj-art-e07353950ec54cb69dde8010047ea9802025-08-20T03:58:27ZengMDPI AGDrones2504-446X2025-07-019748310.3390/drones9070483Predicting Nitrogen Flavanol Index (NFI) in <i>Mentha arvensis</i> Using UAV Imaging and Machine Learning Techniques for Sustainable AgricultureBhavneet Gulati0Zainab Zubair1Ankita Sinha2Nikita Sinha3Nupoor Prasad4Manoj Semwal5Computational Biology Department, CSIR–Central Institute of Medicinal and Aromatic Plants, Lucknow 226015, IndiaComputational Biology Department, CSIR–Central Institute of Medicinal and Aromatic Plants, Lucknow 226015, IndiaDepartment of Electronics and Communication Engineering, Christ University, Bangalore 560074, IndiaDepartment of Electronics and Communication Engineering, Christ University, Bangalore 560074, IndiaPlant Protection and Production Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow 226015, IndiaComputational Biology Department, CSIR–Central Institute of Medicinal and Aromatic Plants, Lucknow 226015, IndiaCrop 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 <i>Mentha arvensis</i> 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 (R<sup>2</sup> = 0.86, RMSE = 0.32) at 75 days after planting (DAP), followed by Gradient Boosting (R<sup>2</sup> = 0.75, RMSE = 0.43). Model performance was lowest during early growth stages (15–30 DAP) but improved markedly from mid to late growth stages (45–90 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.https://www.mdpi.com/2504-446X/9/7/483UAVmultispectral remote sensingnitrogen<i>Mentha arvensis</i>machine learningprecision agriculture |
| spellingShingle | Bhavneet Gulati Zainab Zubair Ankita Sinha Nikita Sinha Nupoor Prasad Manoj Semwal Predicting Nitrogen Flavanol Index (NFI) in <i>Mentha arvensis</i> Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture Drones UAV multispectral remote sensing nitrogen <i>Mentha arvensis</i> machine learning precision agriculture |
| title | Predicting Nitrogen Flavanol Index (NFI) in <i>Mentha arvensis</i> Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture |
| title_full | Predicting Nitrogen Flavanol Index (NFI) in <i>Mentha arvensis</i> Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture |
| title_fullStr | Predicting Nitrogen Flavanol Index (NFI) in <i>Mentha arvensis</i> Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture |
| title_full_unstemmed | Predicting Nitrogen Flavanol Index (NFI) in <i>Mentha arvensis</i> Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture |
| title_short | Predicting Nitrogen Flavanol Index (NFI) in <i>Mentha arvensis</i> Using UAV Imaging and Machine Learning Techniques for Sustainable Agriculture |
| title_sort | predicting nitrogen flavanol index nfi in i mentha arvensis i using uav imaging and machine learning techniques for sustainable agriculture |
| topic | UAV multispectral remote sensing nitrogen <i>Mentha arvensis</i> machine learning precision agriculture |
| url | https://www.mdpi.com/2504-446X/9/7/483 |
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