Machine vision solutions for monitoring pest snails in australian no-till cropping fields: An exploration of spectral characteristics and detectability

In the realm of Australian grain agriculture, the invasive presence of two round snail species, Cernuella virgata and Theba pisana, alongside two conical counterparts, Cochlicella acuta and Cochlicella Barbara, has inflicted substantial economic losses. Effective integrated pest management hinges on precise monitoring of snail populations, yet current manual sampling techniques prove labour-intensive and susceptible to errors. This study underscores the imperative for a machine vision system capable of accurately and efficiently surveilling snail populations in the diverse landscapes of Australian broadacre no-till cropping fields. The inherent challenges of varied plant residues, gravels and soil types in the fields demand a sophisticated optical system to detect small, clustered and camouflaged snails. Through a meticulous exploration of spectral features and detectability by employing different combinations of wavelengths and machine learning algorithms, this research yielded promising results that live round snails, dead round snails, live conical snails, dead conical snails and complex field materials can be classified with F1 scores range from 0.7 to 0.9. This paper not only highlights the potential of such a machine vision system but also delineates ongoing challenges that warrant further investigation. The insights derived from this study serve as a crucial guide for the development of a robust machine vision system aimed at mitigating the impact of pest snails in agricultural fields.