Abstract
Plant nitrogen (N) is one of the key factors for its growth and yield. Timely assessment of plant N at a spatio-temporal scale enables its precision management in the field scale with better N use efficiency. Airborne imaging spectroscopy is a potential technique for non-invasive near real-time rapid assessment of plant N on a field scale. The present study attempted to assess plant N in a wheat field with three different irrigation levels (I1–I3) along with five nitrogen treatments (N0–N4) using a UAV hyperspectral imager with a spectral range of 400 to 1000 nm. A total of 61 vegetative indices were evaluated to find suitable indices for estimating plant N. A hybrid method of R-Square (R2) and Variable Importance Projection (VIP) followed by Variance Inflation Factor was used to limit the best suitable N-sensitive 13 spectral indices. The selected indices were used as feature vectors in the Artificial Neural Network algorithm to model and generate a spatial map of plant N in the experimental wheat field. The model resulted in R2 values of 0.97, 0.84, and 0.86 for training, validation, and testing respectively for plant N assessment.
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This research activity was conducted under financial support of the Indian Council of Agricultural Research (ICAR) and is hereby duly acknowledged under the project, titled ‘Network Program on Precision Agriculture (NePPA)’.
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Sahoo, R.N., Rejith, R.G., Gakhar, S. et al. Drone remote sensing of wheat N using hyperspectral sensor and machine learning. Precision Agric 25, 704–728 (2024). https://doi.org/10.1007/s11119-023-10089-7
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DOI: https://doi.org/10.1007/s11119-023-10089-7