Automated detection of the yellow‐legged hornet (Vespa velutina) using an optical sensor with machine learning

Abstract

BACKGROUND: The yellow-legged hornet (Vespa velutina) is native to Southeast Asia and is an invasive alien species of concern in many countries. More effective management of populations of V. velutina could be achieved through more widespread and intensive monitoring in the field, however current methods are labor intensive and costly. To address this issue, we have assessed the performance of an optical sensor combined with a machine learning model to classify V. velutina and native wasps/hornets and bees. Our aim is to use the results of the present work as a step towards the development of a monitoring solution for V. velutina in the field.RESULTS: We recorded a total 935 flights from three bee species: Apis mellifera, Bombus terrestris and Osmia bicornis; and four wasp/hornet species: Polistes dominula, Vespula germanica, Vespa crabro and V. velutina. The machine learning model achieved an average accuracy for species classification of 80.1 ± 13.9% and 74.5 ± 7.0% for V. velutina. V. crabro had the highest level of misclassification, confused mainly with V. velutina and P. dominula. These results were obtained using a 14-value peak and valley feature derived from the wingbeat power spectral density.CONCLUSION: This study demonstrates that the wingbeat recordings from a flying insect sensor can be used with machine learning methods to differentiate V. velutina from six other Hymenoptera species in the laboratory and this knowledge could be used to help develop a tool for use in integrated invasive alien species management programs.

Deep Learning-Based Portable Image Analysis System for Real-Time Detection of Vespa velutina

“…Recently, research has been conducted on the automated detection of V. velutina using optical sensors with machine learning by analyzing the wingbeat frequencies of seven species of bees, wasps, and hornets. This research was carried out in an entomological tent in the laboratory, and V. velutina individuals were detected by distinguishing species of bees, wasps, and hornets based on the wingbeat frequency [32]. Previous studies have also used machine learning to predict the prioritization of tasks in sighting reports of V. velutina in situations in which human resources are limited [33].…”

Multi-Features and Multi-Deep Learning Networks to identify, prevent and control pests in tremendous farm fields combining IoT and pests sound analysis

“…Our technique is effective in performance and report measurements. Drawing upon an extensive review of relevant academic literature, a majority of the research conducted on images [28][29][30][31][32][33][34][35][36][37][38][39]50] indicates a lack of sufficient attention towards the establishment of reliable analytical techniques for prevention and control [40][41][42][43][44][45] and accurately identifying pests through the analysis of acoustic signals produced by these pests [46][47][48][49]. The summarized findings of these reviews can be found in Table 1.…”

VespAI: a deep learning-based system for the detection of invasive hornets

“…When considering the performance of current operational detection systems, these achieve mean classification accuracies within the range of ~74.5–83.3% for V. velutina 39 , 41 , but suffer from false detections of other hornet species 41 , and in some cases honey bees 39 , 42 . False positives therefore have the potential to accumulate rapidly over time, degrading the efficacy of such systems in cases where true positives are rare.…”

“…The applicability of deep learning to this challenge is evident through parallel machine vision applications in behavioural tracking [28][29][30][31] , pest management [32][33][34] , and conservation biology [35][36][37] . Consequently, there have been several attempts to develop proof-of-concept detection systems for various Vespa species-primarily utilising optical [38][39][40][41] , infrared 42 , and acoustic sensors 43 . These efforts have yielded prototype monitors for deployment at apiaries, with initial tests successfully detecting the presence of hornets in real-time 39,42 .…”