LAUSR

Conventional methods for crop lodging assessments need accurate ground observations and tend to be laborious. Lodging assessment methods and accuracy can thus be improved using remote sensing data from small unmanned aerial systems (UASs) and low orbiting satellites (LOSs). With such aim, imagery to assess spearmint crop lodging was acquired using a small UAS at two ground sample distances (GSDs) of 0.01 and 0.03 m. Crop surface model (CSM) and six image color features were extracted from small UAS-based data. These features were then classified into not lodged (NL), partially lodged (PL), and lodged (L) groups. Mean and majority feature classes were obtained for 50 regions of interest (ROI) of size 1 m² each. Features were compared with visual crop lodging ratings using Pearson correlation ( ${r}$ ) and Cohen’s kappa (CK) coefficients. CSM showed higher assessment accuracy with ${r}~\approx $ 0.85 and CK > 0.60. Mean percentage red (%R) was observed to have the strongest correlation with visual ratings ( $r= 0.75$ and CK = 0.40–0.59) followed by mean percentage blue (%B), both at 0.01-m GSD. The percentage of lodging calculated from %R and %B maps was also contrasted with similar estimates from LOS-based imagery at 3.00-m GSD with no statistical differences found at 5% level.