Bombs and cocaine: detecting nefarious nitrogen sources using remote sensing and machine learning

Abstract

Plants are treated with synthetic or organic nitrogen sources to increase growth and yield, the most common being calcium ammonium nitrate. However, some nitrogen sources are used in illicit activities. Ammonium nitrate is used in explosive manufacture and ammonium sulphate in the cultivation and extraction of the narcotic cocaine from Erythroxylum spp. Here we show that hyperspectral sensing, multispectral imaging and machine learning image analysis can be used to visualise and differentiate plants exposed to different nefarious nitrogen sources. Metabolomic analysis of leaves from plants exposed to different nitrogen sources reveals shifts in colourful metabolites that may contribute to altered reflectance signatures. Overall this suggests that different nitrogen feeding regimes alter plant secondary metabolism leading to changes in the reflectance spectrum detectable via machine learning of multispectral data but not the naked eye. Our results could facilitate the development of technologies to monitor illegal activities involving various nitrogen sources and further inform nitrogen application requirements in agriculture.