Estimation of aboveground biomass from spectral and textural characteristics of paddy crop using UAV-multispectral images and machine learning techniques

Multispectral (MS) images offer essential spectral information for monitoring paddy crops' Aboveground-biomass (AGB), but efficiency decreases due to background materials and high canopy biomass. Texture reveals canopy structure and can be employed in vegetation-indices (VIs) to enhance monitoring accuracy. This study focuses to estimate AGB of paddy crop by exploring the combined potential of spectral and textural features of unmanned aerial vehicle (UAV)-MS images using linear regression (LR), multi-linear regression (MLR), and random forest (RF) models. Results demonstrate that near infrared (NIR)-based VIs outperform Colour-Indices. Normalised difference texture indices (NDTIs) composed of NIR, red-edge (RE) and blue (B) bands outperform all-evaluated VIs and grey-level co-occurrence matrix (GLCM)-textures for different growth stages. Combining VIs and NDTIs, RF performs best compared to other models. The outcomes suggest that the combined spectral and texture information can significantly improve estimation of AGB in paddy crops compared to using either of them alone.