Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant

• DOI: 10.1016/j.isci.2023.108349
• Publication Date: 2023-10-27T11:44:05Z
• Authors: Gianluca Manduca; Valeria Zeni; Sara Moccia; Beatrice A. Milano; Angelo Canale; Giovanni Benelli; Cesare Stefanini; Donato Romano
• Keywords: Ceratitis capitata; Toxicant

Pesticide exposure, even at low doses, can have detrimental effects on ecosystems. This study aimed validating the use of machine learning for recognizing motor anomalies, produced by minimal insecticide exposure a model insect species. The Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), was exposed to food contaminated with concentrations Carlina acaulis essential oil (EO). A deep approach enabled fly pose estimation video recordings in custom-built arena. Five algorithms were trained handcrafted features, extracted from predicted pose, distinguish treated individuals. Random Forest and K-Nearest Neighbor best performed, an area under receiver operating characteristic (ROC) curve 0.75 0.73, respectively. Both achieved accuracy 0.71. Results show potential detecting sublethal arising behavior, which could also affect other organisms environmental health.