Improved pest monitoring and management using multispectral imaging to distinguish the slug species Deroceras reticulatum from field surface materials

BACKGROUND: Deroceras reticulatum (Müller, 1774) (Agriolimacidae), known commonly as the grey field slug, is one of themost economically significant slug pests globally, causing significant damage to a variety of crop types. Traditionally, the pres-ence of slugs in the field has been monitored using traps or visual observations. Such manual approaches are labour intensiveand limit the scope of monitoring. Automated detection could facilitate both population monitoring and the development ofprecision control strategies. In this study, we explore the potential of both multispectral and fluorescence imaging as strategiesfor the automated detection of D. reticulatum.RESULTS: Analysis of multispectral images demonstrated that D. reticulatum has a distinct spectral signature from the four com-mon agricultural field-surface materials evaluated (green leaves, stubble, soil and stones). Furthermore, it was possible to trainstatistical transformations to distinguish slugs from field-surface materials, either alone or in combination. By evaluating func-tions trained using subsets of the multispectral data, it was possible to demonstrate that as few as five wavelengths were suf-ficient for slug detection. The selected wavelengths were from the ultraviolet (365 nm), blue (405 and 450 nm), green (570 nm)and near-infrared (880 nm). Fluorescence imaging failed to detect a slug-specific signal.CONCLUSION: This study has demonstrated the potential of multispectral imaging (MSI) as an image-based approach for slugmonitoring with only a small number of wavelengths being required to distinguish D. reticulatum from complex backgrounds. Ifsuccessfully combined with robotic image capture, MSI could facilitate both automated in-field monitoring and the develop-ment of novel, precision slug control strategies including the use of biocontrols and biorationals.