The biophysical properties of a crop are a good indicator of potential crop stress conditions. However, these visible properties cannot indicate areas exhibiting non-visible stress, e.g., early water or nutrient stress. In this research, maize crop biophysical properties including canopy height and Leaf Area Index (LAI), estimated using drone-based RGB images, were used to identify stressed areas in the farm. First, the APSIM process-based model was used to simulate temporal variation in LAI and canopy height under optimal management conditions, and thus used as a reference for estimating healthy crop parameters. The simulated LAI and canopy height were then compared with the ground-truth information to generate synthetic data for training a linear and a random forest model to identify stressed and healthy areas in the farm using drone-based data products. A Healthiness Index was developed using linear as well as random forest models for indicating the health of the crop, with a maximum correlation coefficient of 0.67 obtained between Healthiness Index during the dough stage of the crop and crop yield. Although these methods are effective in identifying stressed and non-stressed areas, they currently do not offer direct insights into the underlying causes of stress. However, this presents an opportunity for further research and improvement of the approach.