Development of an Artificial Intelligence-Based Video Monitoring System to Investigate Drug Effects on Daphnia Magna

In the field of environmental research, Daphnia magna acts as a cost-effective and convenient model organism that offers scientists valuable insights into the impact of various drugs on the health and behavior of organisms. This paper proposes a novel AI-based video monitoring system that includes two cameras, recording both the frontal and sagittal movement of D. magna against grid paper. Next, the path of the D. magna's movement is tracked using ResNet50. Then, by using trajectory dynamics analysis, certain parameters, such as distance traveled, velocity, acceleration, and jerk are determined. By using a system that incorporates deep learning, the tedious process that scientists undergo in order to determine by hand how the Daphnia magna is affected by certain stressors. This highly adaptable low-cost system allows researchers to alter features such as the tank size, aquatic species, camera location, and movement parameters, creating a more customizable and inclusive approach to studying D. magna. Compared to existing methods, this system is much more flexible and sustainable, as the D. magna can be raised continuously in one tank while the research ensues, allowing for longer-term and larger-scale projects. In this particular experiment, a system is developed and its functionality and accuracy is tested with isopropanol, a common drug that effectively determines the capability of the setup. The primary goal of the research project is to design a system for the study of drug effects on D. magna, so the drugs chosen are intended to confirm the accuracy and effectiveness of the system. In the broader field of biology research, this novel system fills a gap in current methods by offering a versatile tool for researchers to study the impacts of stressors on organisms under various experimental conditions.