A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy

Smart technology for mosquito control: Recent developments, challenges, and future prospects

Smart technology coupled with digital sensors and deep learning networks have emerging scopes in various fields, including surveillance of mosquitoes. Several studies have been conducted to examine the efficacy of such technologies in the differential identification of mosquitoes with high accuracy. Some smart trap uses computer vision technology and deep learning networks to identify live Aedes aegypti and Culex quinquefasciatus in real time. Implementing such tools integrated with a reliable capture mechanism can be beneficial in identifying live mosquitoes without destroying their morphological features. Such smart traps can correctly differentiates between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak. Smart devices embedded with YOLO V4 Deep Neural Network algorithm has been designed with a differential drive mechanism and a mosquito trapping module to attract mosquitoes in the environment. The use of acoustic and optical sensors in combination with machine learning techniques have escalated the automatic classification of mosquitoes based on their flight characteristics, including wing-beat frequency. Thus, such Artificial Intelligence-based tools have promising scopes for surveillance of mosquitoes to control vector-borne diseases. However working efficiency of such technologies requires further evaluation for implementation on a global scale.

Automated classification of mixed populations of Aedes aegypti and Culex quinquefasciatus mosquitoes under field conditions

BACKGROUND

The recent rise in the transmission of mosquito-borne diseases such as dengue virus (DENV), Zika (ZIKV), chikungunya (CHIKV), Oropouche (OROV), and West Nile (WNV) is a major concern for public health managers worldwide. Emerging technologies for automated remote mosquito classification can be supplemented to improve surveillance systems and provide valuable information regarding mosquito vector catches in real time.

METHODS

We coupled an optical sensor to the entrance of a standard mosquito suction trap (BG-Mosquitaire) to record 9151 insect flights in two Brazilian cities: Rio de Janeiro and Brasilia. The traps and sensors remained in the field for approximately 1 year. A total of 1383 mosquito flights were recorded from the target species: Aedes aegypti and Culex quinquefasciatus. Mosquito classification was based on previous models developed and trained using European populations of Aedes albopictus and Culex pipiens.

RESULTS

The VECTRACK sensor was able to discriminate the target mosquitoes (Aedes and Culex genera) from non-target insects with an accuracy of 99.8%. Considering only mosquito vectors, the classification between Aedes and Culex achieved an accuracy of 93.7%. The sex classification worked better for Cx. quinquefasciatus (accuracy: 95%; specificity: 95.3%) than for Ae. aegypti (accuracy: 92.1%; specificity: 88.4%).

CONCLUSIONS

The data reported herein show high accuracy, sensitivity, specificity and precision of an automated optical sensor in classifying target mosquito species, genus and sex. Similar results were obtained in two different Brazilian cities, suggesting high reliability of our findings. Surprisingly, the model developed for European populations of Ae. albopictus worked well for Brazilian Ae. aegypti populations, and the model developed and trained for Cx. pipiens was able to classify Brazilian Cx. quinquefasciatus populations. Our findings suggest this optical sensor can be integrated into mosquito surveillance methods and generate accurate automatic real-time monitoring of medically relevant mosquito species.

Monitoring Mosquito Abundance: Comparing an Optical Sensor with a Trapping Method

Optical sensors have shown significant promise in offering additional data to track insect populations. This article presents a comparative study between abundance measurements obtained from a novel near-infrared optical sensor and physical traps. The optical instrument, named an Entomological Bistatic Optical Sensor System, or eBoss, is a non-destructive sensor operating in the near-infrared spectral range and designed to continuously monitor the population of flying insects. The research compares the mosquito aerial density (#/m3) obtained through the eBoss with trap counts from eight physical traps during an eight-month field study. The eBoss recorded over 302,000 insect sightings and assessed the aerial density of all airborne insects as well as male and female mosquitoes specifically with a resolution of one minute. This capability allows for monitoring population trends throughout the season as well as daily activity peaks. The results affirmed the correlation between the two methods. While optical instruments do not match traps in terms of taxonomic accuracy, the eBoss offered greater temporal resolution (one minute versus roughly three days) and statistical significance owing to its much larger sample size. These outcomes further indicate that entomological optical sensors can provide valuable complementary data to more common methods to monitor flying insect populations, such as mosquitoes or pollinators.

Surveillance of mosquitoes harnessing their buzzing sound

Mosquito surveillance for vector-borne disease management relies on traditional morphological and molecular techniques, which are tedious, time-consuming, and costly. The present study describes a simple and efficient recording device that analyzes mosquito sound to estimate species composition, male-female ratio, fed-unfed status, and harmonic convergence interaction using fundamental frequency (F0) bandwidth, harmonics, amplitude, and combinations of these parameters. The study examined a total of 19 mosquito species, including 3 species of Aedes, 7 species of Anopheles, 1 species of Armigeres, 5 species of Culex, 1 species of Hulecoetomyia, and 2 species of Mansonia. Among them, the F0 ranges between 269.09 ± 2.96 Hz (Anopheles culiciformis) to 567.51 ± 3.82 Hz (Aedes vittatus) and the harmonic band (hb) number ranges from 5 (An. culiciformis) to 12 (Ae. albopictus). In terms of species identification, the success rate was 95.32 % with F0, 84.79 % with F0-bandwidth, 84.79 % with harmonic band (hb) diversity, and 49.7 % with amplitude (dB). The species identification rate has gone up to 96.50 % and 97.66 % with the ratio and multiplication of F0 and hb, respectively. This is because of the matrices that combine multiple sound attributes. Comparatively, combinations of the amplitude of the F0 and the higher harmonic frequency band were non-significant for species identification (60.82 %). The fed females have shown a considerable increase in F0 in comparison to the unfed. The males of all the species possessed significantly higher frequencies with respect to the females. Interestingly, the presence of male-female of Ae. vittatus together showed harmonic convergence between the 2nd and 3rd harmonic bands. In conclusion, the sound-based technology is simple, precise, and cost-effective and provides better resolution for species, sex, and fed-unfed status detection in comparison to conventional methods. Real-time surveillance of mosquitoes could potentially utilize this technology.

Large-scale performance assessment of the BG-Counter 2 used with two different mosquito traps

BACKGROUND

Mosquitoes are important vectors of pathogens. They are usually collected with CO2-baited traps and subsequently identified by morphology. This procedure is very time-consuming. Automatic counting traps could facilitate timely evaluation of the local risk for mosquito-borne pathogen transmission or decision-making on vector control measures, but the counting accuracy of such devices has rarely been validated in the field.

METHODS

The Biogents (BG)-Counter 2 automatically counts mosquitoes by discriminating the size of captured objects directly in the field and transmits the data to a cloud server. To assess the accuracy of this counting device, 27 traps were placed at 19 sampling sites across Germany and used in daily, weekly or bimonthly intervals from April until October 2021. The BG-Counter 2 was attached to a CO2-trap (BG-Pro trap = CO2-Pro) and the same trap was converted to also attract gravid mosquitoes (upside-down BG-Pro trap with a water container beneath = CO2-Pro-gravid). All captured mosquitoes were identified by morphology. The number of females (unfed and gravid), mosquito diversity and the number of identified specimens in relation to the counting data of the BG-Counter were compared between the two trapping devices to evaluate sampling success and counting accuracy.

RESULTS

In total 26,714 mosquitoes were collected during 854 trap days. The CO2-Pro-gravid trap captured significantly more mosquitoes per trap day for all specimens, gravid females and non-gravid females, while there was no difference in the mosquito diversity. The linear model with the captured mosquitoes as a response and the counted specimens as a predictor explained only a small degree of the variation within the data (R2 = 0.16), but per individual trap the value could reach up to 0.62 (mean R2 = 0.23). The counting accuracy for the daily samples had a significant positive correlation with sample size, resulting in higher accuracy for the CO2-Pro-gravid trap and higher accuracy for sites and sampling months with high mosquito abundance.

CONCLUSIONS

While the accuracy of the BG-Counter 2 is quite low, the device is able to depict mosquito phenology and provide information about local population dynamics.

Temperature Dependency of Insect's Wingbeat Frequencies: An Empirical Approach to Temperature Correction

This study examines the relationship between the wingbeat frequency of flying insects and ambient temperature, leveraging data from over 302,000 insect observations obtained using a near-infrared optical sensor during an eight-month field experiment. By measuring the wingbeat frequency as well as wing and body optical cross-sections of each insect in conjunction with the ambient temperature, we identified five clusters of insects and analyzed how their average wingbeat frequencies evolved over temperatures ranging from 10 °C to 38 °C. Our findings reveal a positive correlation between temperature and wingbeat frequency, with a more pronounced increase observed at higher wingbeat frequencies. Frequencies increased on average by 2.02 Hz/°C at 50 Hz, and up to 9.63 Hz/°C at 525 Hz, and a general model is proposed. This model offers a valuable tool for correcting wingbeat frequencies with temperature, enhancing the accuracy of insect clustering by optical and acoustic sensors. While this approach does not account for species-specific responses to temperature changes, our research provides a general insight, based on all species present during the field experiment, into the intricate dynamics of insect flight behavior in relation to environmental factors.

An optical system to detect, surveil, and kill flying insect vectors of human and crop pathogens

Sustainable and effective means to control flying insect vectors are critically needed, especially with widespread insecticide resistance and global climate change. Understanding and controlling vectors requires accurate information about their movement and activity, which is often lacking. The Photonic Fence (PF) is an optical system that uses machine vision, infrared light, and lasers to identify, track, and interdict vectors in flight. The PF examines an insect's outline, flight speed, and other flight parameters and if these match those of a targeted vector species, then a low-power, retina-safe laser kills it. We report on proof-of-concept tests of a large, field-sized PF (30 mL × 3 mH) conducted with Aedes aegypti, a mosquito that transmits dangerous arboviruses, and Diaphorina citri, a psyllid which transmits the fatal huanglongbing disease of citrus. In tests with the laser engaged, < 1% and 3% of A. aegypti and D. citri, respectfully, were recovered versus a 38% and 19% recovery when the lacer was silenced. The PF tracked, but did not intercept the orchid bee, Euglossa dilemma. The system effectively intercepted flying vectors, but not bees, at a distance of 30 m, heralding the use of photonic energy, rather than chemicals, to control flying vectors.

Low coverage of species constrains the use of DNA barcoding to assess mosquito biodiversity

Mosquitoes (Culicidae) represent the main vector insects globally, and they also inhabit many of the terrestrial and aquatic habitats of the world. DNA barcoding and metabarcoding are now widely used in both research and routine practices involving mosquitoes. However, these methodologies rely on information available in databases consisting of barcode sequences representing taxonomically identified voucher specimens. In this study, we assess the availability of public data for mosquitoes in the main online databases, focusing specifically on the two most widely used DNA barcoding markers in Culicidae: COI and ITS2. In addition, we test hypotheses on possible factors affecting species coverage (i.e., the percentage of species covered in the online databases) for COI in different countries and the occurrence of the DNA barcode gap for COI. Our findings showed differences in the data publicly available in the repositories, with a taxonomic or species coverage of 28.4-30.11% for COI in BOLD + GenBank, and 12.32% for ITS2 in GenBank. Afrotropical, Australian and Oriental biogeographic regions had the lowest coverages, while Nearctic, Palearctic and Oceanian had the highest. The Neotropical region had an intermediate coverage. In general, countries with a higher diversity of mosquitoes and higher numbers of medically important species had lower coverage. Moreover, countries with a higher number of endemic species tended to have a higher coverage. Although our DNA barcode gap analyses suggested that the species boundaries need to be revised in half of the mosquito species available in the databases, additional data must be gathered to confirm these results and to allow explaining the occurrence of the DNA barcode gap. We hope this study can help guide regional species inventories of mosquitoes and the completion of a publicly available reference library of DNA barcodes for all mosquito species.

Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex

BACKGROUND

Mosquito-borne diseases are a major concern for public and veterinary health authorities, highlighting the importance of effective vector surveillance and control programs. Traditional surveillance methods are labor-intensive and do not provide high temporal resolution, which may hinder a full assessment of the risk of mosquito-borne pathogen transmission. Emerging technologies for automated remote mosquito monitoring have the potential to address these limitations; however, few studies have tested the performance of such systems in the field.

METHODS

In the present work, an optical sensor coupled to the entrance of a standard mosquito suction trap was used to record 14,067 mosquito flights of Aedes and Culex genera at four temperature regimes in the laboratory, and the resulting dataset was used to train a machine learning (ML) model. The trap, sensor, and ML model, which form the core of an automated mosquito surveillance system, were tested in the field for two classification purposes: to discriminate Aedes and Culex mosquitoes from other insects that enter the trap and to classify the target mosquitoes by genus and sex. The field performance of the system was assessed using balanced accuracy and regression metrics by comparing the classifications made by the system with those made by the manual inspection of the trap.

RESULTS

The field system discriminated the target mosquitoes (Aedes and Culex genera) with a balanced accuracy of 95.5% and classified the genus and sex of those mosquitoes with a balanced accuracy of 88.8%. An analysis of the daily and seasonal temporal dynamics of Aedes and Culex mosquito populations was also performed using the time-stamped classifications from the system.

CONCLUSIONS

This study reports results for automated mosquito genus and sex classification using an optical sensor coupled to a mosquito trap in the field with highly balanced accuracy. The compatibility of the sensor with commercial mosquito traps enables the sensor to be integrated into conventional mosquito surveillance methods to provide accurate automatic monitoring with high temporal resolution of Aedes and Culex mosquitoes, two of the most concerning genera in terms of arbovirus transmission.

Automated differentiation of mixed populations of free-flying female mosquitoes under semi-field conditions

Great advances in automated identification systems, or 'smart traps', that differentiate insect species have been made in recent years, yet demonstrations of field-ready devices under free-flight conditions remain rare. Here, we describe the results of mixed-species identification of female mosquitoes using an advanced optoacoustic smart trap design under free-flying conditions. Point-of-capture classification was assessed using mixed populations of congeneric (Aedes albopictus and Aedes aegypti) and non-congeneric (Ae. aegypti and Anopheles stephensi) container-inhabiting species of medical importance. Culex quinquefasciatus, also common in container habitats, was included as a third species in all assessments. At the aggregate level, mixed collections of non-congeneric species (Ae. aegypti, Cx. quinquefasciatus, and An. stephensi) could be classified at accuracies exceeding 90% (% error = 3.7-7.1%). Conversely, error rates increased when analysing individual replicates (mean % error = 48.6; 95% CI 8.1-68.6) representative of daily trap captures and at the aggregate level when Ae. albopictus was released in the presence of Ae. aegypti and Cx. quinquefasciatus (% error = 7.8-31.2%). These findings highlight the many challenges yet to be overcome but also the potential operational utility of optoacoustic surveillance in low diversity settings typical of urban environments.

Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond

Climate change is one of several drivers of recurrent outbreaks and geographical range expansion of infectious diseases in Europe. We propose a framework for the co-production of policy-relevant indicators and decision-support tools that track past, present, and future climate-induced disease risks across hazard, exposure, and vulnerability domains at the animal, human, and environmental interface. This entails the co-development of early warning and response systems and tools to assess the costs and benefits of climate change adaptation and mitigation measures across sectors, to increase health system resilience at regional and local levels and reveal novel policy entry points and opportunities. Our approach involves multi-level engagement, innovative methodologies, and novel data streams. We take advantage of intelligence generated locally and empirically to quantify effects in areas experiencing rapid urban transformation and heterogeneous climate-induced disease threats. Our goal is to reduce the knowledge-to-action gap by developing an integrated One Health-Climate Risk framework.

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

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. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Insect biomass density: measurement of seasonal and daily variations using an entomological optical sensor

Insects are major actors in Earth's ecosystems and their recent decline in abundance and diversity is alarming. The monitoring of insects is paramount to understand the cause of this decline and guide conservation policies. In this contribution, an infrared laser-based system is used to remotely monitor the biomass density of flying insects in the wild. By measuring the optical extinction caused by insects crossing the 36-m long laser beam, the Entomological Bistatic Optical Sensor System used in this study can evaluate the mass of each specimen. At the field location, between July and December 2021, the instrument made a total of 262,870 observations of insects for which the average dry mass was 17.1 mg and the median 3.4 mg. The daily average mass of flying insects per meter cube of air at the field location has been retrieved throughout the season and ranged between near 0 to 1.2 mg/m³. Thanks to its temporal resolution in the minute range, daily variations of biomass density have been observed as well. These measurements show daily activity patterns changing with the season, as large increases in biomass density were evident around sunset and sunrise during Summer but not during Fall.