Using deep learning to assess the toxicological effects of sublethal exposure of a novel green pesticide in a stored-product beetle

BACKGROUND: Managing stored-grain pests requires new strategies to limit economic and health risks. This study analyses the sublethal effects of the natural compound carlina oxide on Prostephanus truncatus, providing new behavioural insights through a multidisciplinary approach. A fully automatic computer vision approach was developed to label two specific insect body parts, enabling the generation of an annotated dataset without manual intervention. This dataset was used to train a convolutional neural network (CNN) for pose estimation. A second dedicated CNN focused on the antennae to investigate neuroethological and sensory variations. RESULTS: CNN for body parts detection achieved an average precision of 0.78, recall of 0.90, and F1 score of 0.84 on the test dataset. An additional CNN tracked key points for antennal pose estimation. Motor analysis showed that the LC30 of carlina oxide reduced average speed and distance, induced altered exploratory behaviour, and affected thigmotaxis. Statistically sig- nificant features were evaluated using machine learning classifiers: random forest, support vector machine, and K-nearest neighbours. The analysis comparing control and treated groups distinguishes LC30 and LC10 from the control group, while SHapley Additive exPlanation (SHAP) analysis explained the features contribution to predictions. CONCLUSIONS: Metrics poorly distinguish individuals in the LC10 and LC30 classes, supporting the employment of lower suble- thal concentration for the control of P. truncatus. However, our findings indicate possible neuroethological effects of green pes- ticides 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.