Timely detection of an invasion event, or a pest outbreak, is an extremely challenging operation of major importance for implementing management action toward eradication and/or containment. Fruit flies—FF—(Diptera: Tephritidae) comprise important invasive and quarantine species that threaten the world fruit and vegetables production. The current manuscript introduces a recently developed McPhail-type electronic trap (e-trap) and provides data on its field performance to surveil three major invasive FF (Ceratitis capitata, Bactrocera dorsalis and B. zonata). Using FF male lures, the e-trap attracts the flies and retains them on a sticky surface placed in the internal part of the trap. The e-trap captures frames of the trapped adults and automatically uploads the images to the remote server for identification conducted on a novel algorithm involving deep learning. Both the e-trap and the developed code were tested in the field in Greece, Austria, Italy, South Africa and Israel. The FF classification code was initially trained using a machine-learning algorithm and FF images derived from laboratory colonies of two of the species (C. capitata and B. zonata). Field tests were then conducted to investigate the electronic, communication and attractive performance of the e-trap, and the model accuracy to classify FFs. Our results demonstrated a relatively good communication, electronic performance and trapping efficacy of the e-trap. The classification model provided average precision results (93–95%) for the three target FFs from images uploaded remotely from e-traps deployed in field conditions. The developed and field tested e-trap system complies with the suggested attributes required for an advanced camera-based smart-trap.

及时发现入侵事件或害虫爆发是一项极具挑战性的操作，对于实施根除和/或遏制的管理行动具有重要意义。果蝇——FF——（双翅目：实蝇科）是威胁世界水果和蔬菜生产的重要入侵和检疫物种。目前的手稿介绍了最近开发的 McPhail 型电子陷阱 (e-trap)，并提供了有关其现场性能的数据，以监测三种主要的侵入性 FF（Ceratitis capitata、Bactrocera dorsalis和B. zonata）。使用 FF 雄性诱饵，电子陷阱可以吸引苍蝇并将它们保留在放置在陷阱内部的粘性表面上。e-trap 捕获被困成年人的帧，并自动将图像上传到远程服务器，以便通过一种涉及深度学习的新算法进行识别。e-trap 和开发的代码都在希腊、奥地利、意大利、南非和以色列进行了现场测试。FF 分类代码最初是使用机器学习算法和来自两个物种（C. capitata和B.zonata ）的实验室菌落的 FF 图像进行训练的）。然后进行现场测试，以研究电子陷阱的电子、通信和吸引力性能，以及对 FF 进行分类的模型准确性。我们的结果证明了电子陷阱的相对良好的通信、电子性能和诱捕效率。分类模型提供了三个目标 FF 的平均精度结果 (93-95%)，这些图像来自从部署在现场条件下的电子陷阱远程上传的图像。开发和现场测试的电子陷阱系统符合先进的基于相机的智能陷阱所需的建议属性。