Drone remote sensing of wheat N using hyperspectral sensor and machine learning

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.