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.摘要 生物防治天敌的捕食率或致死率常被用作评估不同天敌物种在不同条件下防控效能的替代指标。对于广食性捕食者与多数卵寄生蜂而言，地中海粉螟（Ephestia kuehniella Zeller，鳞翅目：螟蛾科）的卵常被作为实验室与田间试验中的替代猎物或寄主，因其可通过生物防治产业的规模化饲养批量获取。评估天敌对地中海粉螟卵的捕食或寄生活性，是全球生物防治从业者的一项实用工具。然而人工评估不仅耗时费力，且易产生误差，观测结果往往带有主观性，且受操作者个体差异影响。 为此，本研究开发了一套基于深度学习目标检测算法YOLOv5的自动化流程，用于精准估算刺吸式广食性捕食者对地中海粉螟卵的捕食情况。经训练的深度学习模型在测试试验中，对完整卵群中被捕食卵的识别精度达0.90、召回率达0.93；基于平均绝对百分比误差指标，该方法相较于两位在体视显微镜下开展人工计数的独立观察者，准确性与效率均更优。 此外，本研究还呈现了一则案例研究，对比了亮小花蝽（Orius laevigatus）、大翅小花蝽（Orius majusculus）、微小花蝽（Orius minutus）、烟盲蝽（Nesidiocoris tenuis）、矮小花盲蝽（Macrolophus pygmaeus）以及弯胫盲蝽（Dicyphus errans）等广食性捕食者的捕食活性。最后讨论了该自动化流程的进一步应用场景与拓展方向。