Alkaloid concentration, which represents the quality of industrial poppy, needs to be estimated in a spatially explicit manner to predict the value of crop prior to harvesting. Current practice is to estimate alkaloid concentration using destructive sampling and laboratory analysis. However, in order to estimate the value of the whole crop, a method that could predict alkaloid concentration in field conditions prior to harvesting is needed. In this study, an unmanned aerial system (UAS) with multispectral imaging was tested for estimation of alkaloid concentration of a poppy crop before harvest, which was sown for pharmaceutical purposes in Tasmania, Australia. This study presents the result of a random forest (RF) regression analysis to evaluate the contribution and predictive ability of spectral and structural variables derived from the images. It was found that UAS imagery with an RF model has the potential to estimate thebaine (paramorphine) concentration well before harvesting and without laboratory analysis. It was found that an RF model with the combination of MSAVI, mSR, OSAVI, NDVI and EVI spectral indices can provide optimal results to estimate thebaine with a relative error of 13.56% to 22.36% with training and validation datasets, respectively. The thebaine concentration predicted using the proposed RF model was strongly correlated to the laboratory-measured thebaine concentration, with an R² value ranging from 0.63 to 0.82 for the training and validation datasets, respectively. These results indicate that poppy thebaine concentration can be estimated with reasonable accuracy 3 weeks prior to harvesting.