Estimating Leaf Area Index of Wheat Using UAV-Hyperspectral Remote Sensing and Machine Learning
Hyperspectral remote sensing using Unmanned Aerial Vehicles (UAVs) provides accurate, near real-time, and large-scale spatial estimation of the leaf area index (LAI), a significant crop variable for monitoring crop growth. In the present study, the LAI of wheat crops was estimated using high-resolut...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2025
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/179783 |
| _version_ | 1855524315914567680 |
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| author | Rejith, Rajan G. Sahoo, Rabi N. Ranjan, Rajeev Kondraju, Tarun Bhandari, Amrita Gakhar, Shalini |
| author_browse | Bhandari, Amrita Gakhar, Shalini Kondraju, Tarun Ranjan, Rajeev Rejith, Rajan G. Sahoo, Rabi N. |
| author_facet | Rejith, Rajan G. Sahoo, Rabi N. Ranjan, Rajeev Kondraju, Tarun Bhandari, Amrita Gakhar, Shalini |
| author_sort | Rejith, Rajan G. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Hyperspectral remote sensing using Unmanned Aerial Vehicles (UAVs) provides accurate, near real-time, and large-scale spatial estimation of the leaf area index (LAI), a significant crop variable for monitoring crop growth. In the present study, the LAI of wheat crops was estimated using high-resolution UAV-borne hyperspectral data. The PLS (Partial Least Squares) regression combined with the VIP (Variable Importance in the Projection) was used for selecting the optimum indices as feature vectors to the Extreme Gradient Boosting (Xgboost) model for predicting LAI. Twelve of twenty-seven vegetation indices were selected to develop the prediction model. On validation against the in situ measured LAI values, the prediction model shows good accuracy with an R2 of 0.71. The model was used to generate a spatial map showing the variability of the LAI. Accurate mapping of LAI from high-resolution hyperspectral UAV data using machine learning models facilitates near-real-time monitoring of crop health. |
| format | Journal Article |
| id | CGSpace179783 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1797832026-01-14T02:07:18Z Estimating Leaf Area Index of Wheat Using UAV-Hyperspectral Remote Sensing and Machine Learning Rejith, Rajan G. Sahoo, Rabi N. Ranjan, Rajeev Kondraju, Tarun Bhandari, Amrita Gakhar, Shalini leaf area index machine learning remote sensing unmanned aerial vehicle hyperspectral imagery wheat Hyperspectral remote sensing using Unmanned Aerial Vehicles (UAVs) provides accurate, near real-time, and large-scale spatial estimation of the leaf area index (LAI), a significant crop variable for monitoring crop growth. In the present study, the LAI of wheat crops was estimated using high-resolution UAV-borne hyperspectral data. The PLS (Partial Least Squares) regression combined with the VIP (Variable Importance in the Projection) was used for selecting the optimum indices as feature vectors to the Extreme Gradient Boosting (Xgboost) model for predicting LAI. Twelve of twenty-seven vegetation indices were selected to develop the prediction model. On validation against the in situ measured LAI values, the prediction model shows good accuracy with an R2 of 0.71. The model was used to generate a spatial map showing the variability of the LAI. Accurate mapping of LAI from high-resolution hyperspectral UAV data using machine learning models facilitates near-real-time monitoring of crop health. 2025-06-18 2026-01-14T01:37:04Z 2026-01-14T01:37:04Z Journal Article https://hdl.handle.net/10568/179783 en Open Access application/pdf MDPI Sahoo, Rabi N., R. G. Rejith, Tarun Kondraju, Rajeev Ranjan, Amrita Bhandari, Shalini Gakhar, Mohd Asim et al. "Scaling-up plant chlorophyll retrieval from proximal to UAV-borne hyperspectral data using a Gaussian process hybrid model." International Journal of Remote Sensing 46, no. 23 (2025): 8990-9014. |
| spellingShingle | leaf area index machine learning remote sensing unmanned aerial vehicle hyperspectral imagery wheat Rejith, Rajan G. Sahoo, Rabi N. Ranjan, Rajeev Kondraju, Tarun Bhandari, Amrita Gakhar, Shalini Estimating Leaf Area Index of Wheat Using UAV-Hyperspectral Remote Sensing and Machine Learning |
| title | Estimating Leaf Area Index of Wheat Using UAV-Hyperspectral Remote Sensing and Machine Learning |
| title_full | Estimating Leaf Area Index of Wheat Using UAV-Hyperspectral Remote Sensing and Machine Learning |
| title_fullStr | Estimating Leaf Area Index of Wheat Using UAV-Hyperspectral Remote Sensing and Machine Learning |
| title_full_unstemmed | Estimating Leaf Area Index of Wheat Using UAV-Hyperspectral Remote Sensing and Machine Learning |
| title_short | Estimating Leaf Area Index of Wheat Using UAV-Hyperspectral Remote Sensing and Machine Learning |
| title_sort | estimating leaf area index of wheat using uav hyperspectral remote sensing and machine learning |
| topic | leaf area index machine learning remote sensing unmanned aerial vehicle hyperspectral imagery wheat |
| url | https://hdl.handle.net/10568/179783 |
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