Grass grub (Costelytra giveni) in improved grasslands detected by remote sensing data and machine learning approaches
Year: 2025
Type: article
Abstract: 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 farm and paddock scale are a crucial first step to reducing economic losses. Current early detection methods are too labour intensive for most farmers. Automated methods for both early detection and population monitoring of larvae 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. 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 of 150 larvae m-2. The best performing model achieved 76% 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 subterranean larval populations before significant pasture damage occurs, allowing for implementation of effective control measures by growers both at a paddock and farm scale.
Source: Smart Agricultural Technology
Institution: Bioeconomy Science Institute
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDGs: Zero hunger, Life on land
Open Access status: gold
Estimated APC paid: $1,770
Award: G8599735973
OpenAlex ID: W4416777751