The use of Unmanned Aerial Vehicle (UAV) remotely sensed data and biophysical variables to predict maize Above-Ground Biomass (AGB) in small-scale farming systems

Considering the current and projected increase in human population, approaches to optimize crop productivity to meet the rising demand are paramount. Timely and accurate maize Above Ground Biomass (AGB) measurements allow for development of models that can precisely predict yield prior to harvesting, useful for managing cropping systems and food production. The development of Unmanned Aerial Vehicles (UAVs) as a new generation of robust remote sensing platforms, mounted with high-resolution sensors has allowed timely and accurate prediction of maize AGB in pursuit of sustaining food security. Hence, this study aimed to predict maize crop AGB in small-scale farming systems using UAV-remotely sensed data and landscape biophysical variables. The DJI Matrice 300 UAV mounted with a MicaSense multispectral camera was used to acquire high-resolution images at four phenological stages that covered the vegetative (V8 &V12) and reproductive stages (R2 & R5). Furthermore, in-situ plant biophysical measurements and landscape variables were acquired and combined with UAV-remotely sensed derived vegetation indices to model maize AGB using a Deep Neural Network (DNN) model. The optimal model for predicting maize AGB was achieved by combining optimal vegetation indices, with Leaf Area Index (LAI), leaf chlorophyll content, slope, aspect, and soil moisture across all phenological stages. Results showed that the V12 phenological stage yielded a better overall prediction accuracy (R² = 0.75, RMSE = 0.07 kg/m², rRMSE = 6.12 %) than the V8 (R² = 0.71 RMSE = 0.10 kg/m², rRMSE = 8.02 %), R2 (R² = 0.73 RMSE = 0.09 kg/m², rRMSE = 7.86 %), and R5 (R² = 0.70 RMSE = 0.10 kg/m², rRMSE = 8.51 %) growth phases. The study concludes that the V12 and R2 phenological stages are optimum for estimating maize AGB. This study contributes to a better understanding of maize crop monitoring efforts for improved production and food security.