Grass grub (Costelytra giveni) in improved grasslands detected by remote sensing data and machine learning approaches

• Larvae of the scarab pest Costelytra giveni cause widespread damage to improved grasslands. • Automated technology to map emerging outbreak populations provides an early warning. • A machine learning model was developed to detect damaging larval populations. • The model was able to detect damage by larvae with 76% accuracy for unseen test data. • This research improves protection of improved grasslands to reduce economic losses. Smarter ways to monitor and predict invertebrate pest outbreaks on an area wide scale to provide early implementation of appropriate controls, have the potential to provide economic, environmental and social benefits to growers. Grass grub ( Costelytra giveni ) is a major scarab pest in New Zealand pastoral farming systems, where the root-feeding larvae cause significant damage to improved grasslands leading to declines in persistence and productivity losses. Early detection of a possible pest infestation at both a paddock and farm and paddock scale are a crucial first step to reducing economic losses. Costelytra giveni population outbreaks can often be unexpected and widespread. Furthermore, current early detection methods are too labour intensive for most farmers, so monitoring of populations each year and across paddocks is not undertaken. Automated methods for early detection of larval populations above the damage threshold (150 larvae m -2 ) at the paddock scale would assist farmers and farm advisers to make decisions of when and where control measures are needed. In this context, new advances in remote sensing technologies and machine learning algorithms offer great potential for managing this challenging pest. Using field data on larval densities collected each year over five years and corresponding high resolution satellite images of the pasture (5 m spatial resolution), we tested a machine learning model to detect the risk that larval densities were above the damage threshold. The best performing model achieved 77% accuracy for unseen test data and was able to reliably distinguish damage caused by larvae below and above threshold. This demonstrates proof of concept for a new approach to identify the presence of C. giveni populations that are above threshold before significant pasture damage occurs, allowing for implementation of effective control measures.