Insights into Entomopathogenic Nematode Behavior by Using AI Techniques to Advance Sustainable Pest Control

Entomopathogenic nematodes (EPNs) are organisms that are often mass-produced as biological control agents (BCAs) to mitigate pesticide-related hazards and foster environmental sustainability. Enhancing our understanding of EPNs biology and their interactions with hosts is crucial for refining the use of EPNs in integrated pest management. This study pioneers an interdisciplinary approach, integrating engineering, and entomology, to investigate the behavior of Steinernema carpocapsae EPN. Employing a novel blend of deep learning and optical flow analysis, a Convolutional Neural Network (CNN) effectively recognizes how nematodes react to host-borne stimuli. Achieving remarkable precision (1) and an overall accuracy of 0.938, the model elucidates EPN behaviors in a prototyped microfluidic platform reproducing a host-environment context. The integration of optical flow analysis highlights an increased motor activity in EPNs when exposed to stimuli, adding novel information on their dynamic responses. This versatile methodology represents a significant advancement in detecting and understanding EPNs responses to diverse stimuli, fostering their use as advanced BCAs in sustainable pest control and environmental management.