An investigation on the best-fit models for sugarcane biomass estimation by linear mixed-effect modelling on unmanned aerial vehicle-based multispectral images: A case study of Australia

Due to the worldwide population growth and the increasing needs for sugar-based products, accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth. This research aims to find the imperative predictors correspond to the random and fixed effects to improve the accuracy of wet and dry sugarcane biomass estimations by integrating ground data and multi-temporal images from Unmanned Aerial Vehicles (UAVs). The multispectral images and biomass measurements were obtained at different sugarcane growth stages from 12 plots with three nitrogen fertilizer treatments. Individual spectral bands and different combinations of the plots, growth stages, and nitrogen fertilizer treatments were investigated to address the issue of selecting the correct fixed and random effects for the modelling. A model selection strategy was applied to obtain the optimum fixed effects and their proportional contribution. The results showed that utilizing Green, Blue, and Near Infrared spectral bands on models rather than all bands improved model performance for wet and dry biomass estimates. Additionally, the combination of plots and growth stages outperformed all the candidates of random effects. The proposed model outperformed the Multiple Linear Regression (MLR), Generalized Linear Model (GLM), and Generalized Additive Model (GAM) for wet and dry sugarcane biomass, with coefficients of determination (R²) of 0.93 and 0.97, and Root Mean Square Error (RMSE) of 12.78 and 2.57 t/ha, respectively. This study indicates that the proposed model can accurately estimate sugarcane biomasses without relying on nitrogen fertilizers or the saturation/senescence problem of Vegetation Indices (VIs) in mature growth stages.