Automated identification and counting of predated Ephestia kuehniella (Zeller) eggs using deep learning image analysis

Abstract Predation or kill rate of biological control agents is often used as a proxy to evaluate the efficacy of different species of natural enemies, and under different conditions. For generalist predators and many egg parasitoids, eggs of the Mediterranean flour moth Ephestia kuehniella (Zeller) (Lepidoptera: Pyralidae) are used as factitious prey or hosts in laboratory and field experiments, as they are widely available from mass rearings of the biological control industry. Evaluating the predation or parasitism activity of natural enemies on E. kuehniella is a valuable tool for biological control practitioners around the world. However, manual assessments are laborious and prone to error, as observations may be subjective and depend on the individual performing the task. Here, we developed an automated protocol based on the deep learning object detection algorithm YOLOv5, for the accurate estimation of predated E. kuehniella eggs by piercing-sucking generalist predators. The application of the trained deep learning model achieved high precision (0.90) and recall (0.93) in the identification of predated eggs among intact eggs in our test experiment and was more accurate and faster than two independent observers that performed manual counting under a stereo microscope based on the mean absolute percentage error metric. Furthermore, a case study is presented where the predation activity of the generalist predators Orius laevigatus, Orius majusculus, Orius minutus, Nesidiocoris tenuis, Macrolophus pygmaeus and Dicyphus errans is compared. Further applications and expansions of the protocol are discussed.