Maize On-Farm Stressed Area Identification Using Airborne RGB Images Derived Leaf Area Index and Canopy Height

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