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A fast and reliable larval sampling method for improving the monitoring of fruit flies in soft and stone fruits.

Ghais ZrikiRémy BeloisChristine FournierLéa Tergoat-BertrandPierre-Yves PoupartAmélie BardelBenjamin GardNicolas Olivier Rode
Published in: Journal of economic entomology (2024)
The spotted-wing drosophila, Drosophila suzukii (Matsumura) (Diptera: Drosophilidae), threatens both the soft-skinned and stone fruit industry in Asia, Europe, and America. Integrated pest management requires monitoring for infestation rates in real time. Although baited traps for adult D. suzukii are widely used for field monitoring, trap captures are weakly correlated to larval infestation rates. Thus, monitoring for larvae instead of adult flies represents the most reliable monitoring technique. Current methods for larval monitoring (e.g., sugar or salt floatation) are time-consuming and labor-intensive. In this study, we develop a new "sleeve method" for detecting larvae in strawberries through the inspection of individual fruits crushed within transparent plastic sleeves. Samples can be optionally frozen until further processing. Based on count data from non-expert observers, the estimation of larval infestation with the sleeve method is fast, precise, and highly repeatable within and among observers. Mean processing time is half the time compared to previous methods (33-80 s per sample depending on infestation levels). As the accuracy of the sleeve method decreases with infestation levels, we suggest ways to improve its accuracy by incubating fruits for 48 h and calibrating data using fruits with a known number of larvae. The method could also be used in other fruits, as it is easier to use, faster, and requires less equipment than previous monitoring methods. Finally, the method represents a promising tool for growers or researchers to effectively monitor and manage D. suzukii and other insect pests of soft and stone fruits.
Keyphrases
  • drosophila melanogaster
  • aedes aegypti
  • zika virus
  • clinical practice
  • machine learning
  • deep learning