A real-time remote surveillance system for fruit flies of economic importance: sensitivity and image analysis

Abstract

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.

Effect of natural attractant essential oil from Ocimum gratisimum from Timor Island against fruit flies

“…The resulting yield losses have a substantial impact on economic growth, amounting trillions of rupiah [5]. Fruit flies fall into the quarantine category of pest [6,7], which means that fruit infested by these flies cannot be exported.…”

Machine Vision for Smart Trap Bandwidth Optimization and New Threat Identification

“…This also helps to reduce the possibility of crowds of insects, thus avoiding overlaps between them, which will have a negative impact on the identification process through image analysis. When the smart trap is applied in an environment with a large population of attracted insects, the sticky board becomes saturated in a short time, requiring manual intervention in a higher frequency [5]. In order to reduce this problem, Huang et al [6] propose a motor-driven e-trap based on a yellow sticky trap to automatically replace attractants and avoid insect overlapping, allowing long-acting work without manual operation.…”

Mango Fruit Fly Trap Detection Using Different Wireless Communications

“…Recently, artificial intelligence has been effectively introduced in precise farming [27,28]. Deep convolutional networks (R-CNN) are being used for object detection in traps and are considered an accurate predictor of their locations [29,30].…”