Using deep learning to assess the toxicological effects of sublethal exposure of a novel green pesticide in a stored‐product beetle
Metrics poorly distinguish individuals in the LC<sub>10</sub> and LC<sub>30</sub> classes, supporting the employment of lower sublethal concentration for the control of P. truncatus. However, our findings indicate possible neuroethological effects of green pesticides on sensory systems, highlighting the need for an accurate risk assessment to minimize ecosystem impacts and supporting integrated pest management within One-Health and Eco-Health frameworks. © 2026 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Authors
- Gianluca Manduca (ORCID: https://orcid.org/0000-0003-0338-441X)
- Eleonora Spinozzi (ORCID: https://orcid.org/0000-0002-1216-1368)
- Marta Ferrati (ORCID: https://orcid.org/0000-0001-9748-3042)
- Maria C. Boukouvala (ORCID: https://orcid.org/0009-0007-7213-3442)
- Filippo Maggi (ORCID: https://orcid.org/0000-0001-9530-5621)
- Anita Casadei
- Antonio DeSimone
- Cesare Stefanini
- Nickolas G. Kavallieratos
Institutions
- Agricultural University of Athens (GR)
- Università di Camerino (IT)
- Scuola Superiore Sant'Anna (IT)
- Mohamed bin Zayed University of Artificial Intelligence (AE)
Publication Details
- Journal
- Pest Management Science
- Published
- 2026-02-03
- DOI
- https://doi.org/10.1002/ps.70545
- Primary Topic
- Insect Pest Control Strategies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- Scuola Superiore Sant'Anna