Unveiling host-seeking behaviour in entomopathogenic nematodes via lab-on-a-chip technology

Entomopathogenic nematodes (EPNs) can be employed as biological control agents (BCAs) for many insect pests’ sustainable management. Despite their widespread use, our understanding of EPNs biology, particularly interactions with their hosts, remains limited. Advancing knowledge of EPNs ecology and host interactions is crucial for optimising their efficacy in pest management. This study pioneers an interdisciplinary approach, at the interface of engineering and applied entomology, to investigate the behaviour of the EPN Steinernema carpocapsae . A novel method combining microfluidics, machine learning, and optical flow is presented. A lab-on-a-chip platform was designed to enable accurate investigation of EPN response to stimuli. A convolutional neural network (CNN) identified nematodes and distinguished their responses to host-derived cues achieving 0.94 accuracy and 1.00 precision in detecting stimulus presence at video-level, classifying EPN behaviour within a controlled environment that simulates host conditions. Optical flow analysis revealed differences in motor activity of EPN upon exposure to stimuli, providing new insights into their dynamic responses. Steinernema carpocapsae exhibited more intense activity in presence of host-borne cues ( p = 0.0055). Support vector machine (SVM) and multilayer perceptron (MLP) classifiers distinguished stimulus contexts from optical flow features, with an area under the receiver operating characteristic (ROC) curve of 0.71. These results highlight that, although S. carpocapsae is typically considered an ambusher, it may actively engage in host-seeking behaviour, suggesting a shift in our understanding of its search strategies. This methodology significantly enhances the detection and understanding of EPN responses to cues, advancing their potential in precision biocontrol programs for sustainable pest management actions. This study develops a novel lab-on-a-chip platform integrating artificial intelligence (AI) for the precise investigation of host-seeking behaviours in the entomopathogenic nematode Steinernema carpocapsae , a biological control agent (BCA) with potential for sustainable pest management. By combining microfluidic design with deep learning, the platform accurately assesses nematode responses to host-derived cues, providing new insights into its foraging adaptability beyond conventional techniques. This research can help researchers and agricultural stakeholders by enhancing understanding of BCA behaviour, optimising pest control applications, and informing evidence-based decisions on sustainable crop protection. The findings also support quality assurance in biological control validation by offering a rigorous framework for evaluating nematode effectiveness under realistic conditions, promoting its broader adoption in integrated pest management strategies. • Entomopathogenic nematodes (EPNs) reactions to cues is still limited. • A multidisciplinary approach linking microfluidics, deep learning and optical flow. • A novel lab-on-a-chip platform promotes precise EPN reaction investigation. • Motor analysis highlights behavioural traits of a cruiser species. • EPNs can be used as biosensors and biocontrol agents in integrated pest management.