Manual monitoring of animal behavior is time-consuming and prone to bias. An alternative to such limitations is using computational resources in behavioral assessments, such as tracking systems, to facilitate accurate and long-term evaluations. There is a demand for robust software that addresses analysis in heterogeneous environments (such as in field conditions) and evaluates multiple individuals in groups while maintaining their identities. The Ethoflow software was developed using computer vision and artificial intelligence (AI) tools to monitor various behavioral parameters automatically. An object detection algorithm based on instance segmentation was implemented, allowing behavior monitoring in the field under heterogeneous environments. Moreover, a convolutional neural network was implemented to assess complex behaviors expanding behavior analyses’ possibilities. The heuristics used to generate training data for the AI models automatically are described, and the models trained with these datasets exhibited high accuracy in detecting individuals in heterogeneous environments and assessing complex behavior. Ethoflow was employed for kinematic assessments and to detect trophallaxis in social bees. The software was developed in desktop applications and had a graphical user interface. In the Ethoflow algorithm, the processing with AI is separate from the other modules, facilitating measurements on an ordinary computer and complex behavior assessing on machines with graphics processing units. Ethoflow is a useful support tool for applications in biology and related fields.

Bee pollination is crucial for ecosystem maintenance and crop production. The ubiquity of bee pollinators in agricultural landscapes frequently results in their exposure to agrochemicals, which has been associated with their decline. Stingless bees are wild pollinators restricted to the Pantropical region, and like honey bees, are suffering colony losses. However, stingless bees and honey bees do not show the same behaviors, therefore, methods used for risk assessment of honey bees cannot be utilized on stingless bees. Herein, we describe protocols to standardize methods that allow for the exploration of lethal and sublethal effects of agrochemicals via acute and chronic exposure of stingless bees. The in vitro rearing used for chronic exposure from the egg to the adult stage proved to be effective in obtaining relevant screenings. In addition, we performed a meta-analysis and summarized the results of toxicological studies conducted with the protocols described. The meta-analyses indicated a reduction in survival under acute and chronic exposures to agrochemicals, and revealed that our protocols for toxicological assessments did not have publication bias for either acute or chronic exposure. These findings proved that these standardized protocols are reliable for toxicological research on stingless bee.