Optical remote sensing for monitoring flying mosquitoes, gender identification and discussion on species identification

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.

Differences in male Aedes aegypti and Aedes albopictus hearing systems facilitate recognition of conspecific female flight tones

When Aedes albopictus mosquitoes invade regions predominated by Aedes aegypti, either the latter can be displaced or the species can coexist, with potential consequences on disease transmission. Males from both species identify females by listening for her flight sounds. Comparing male hearing systems may provide insight into how hearing could prevent interspecific mating. Here, we show that species-specific differences in female wing beat frequencies are reflected in differences in male ear mechanical tuning frequencies and sound response profiles. Though Aedes albopictus males are attracted to sound, they do not readily display abdominal bending, unlike Aedes aegypti. We observed interspecific differences in male ear mechanical, but not electrical, tuning, suggesting a conserved primary auditory processing pathway. Our work suggests a potential role for hearing in the premating isolation of Aedes aegypti and Aedes albopictus, with implications for predicting future dynamics in their sympatric relationships and our understanding of mosquito acoustic communication.

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.

Optical multiband polarimetric modulation sensing for gender and species identification of flying native solitary pollinators

Native pollinators are crucial to local ecosystems but are under threat with the introduction of managed pollinators, e.g., honeybees (Apis mellifera). We explored the feasibility of employing the entomological lidar technique in native pollinator abundance studies. This study included individuals of both genders of three common solitary bee species, Osmia californica, Osmia lignaria, and Osmia ribifloris, native to North America. Properties including optical cross-section, degree of linear polarization, and wingbeat power spectra at all three wavelengths have been extracted from the insect signals collected by a compact stand-off sensing system. These properties are then used in the classification analysis. For species with temporal and spatial overlapping, the highest accuracies of our method exceed 96% (O. ribifloris & O. lignaria) and 93% (O. lignaria & O. californica). The benefit of employing the seasonal activity and foraging preference information in enhancing identification accuracy has been emphasized.

Arbovirus surveillance in mosquitoes: Historical methods, emerging technologies, and challenges ahead

Arboviruses cause millions of infections each year; however, only limited options are available for treatment and pharmacological prevention. Mosquitoes are among the most important vectors for the transmission of several pathogens to humans. Despite advances, the sampling, viral detection, and control methods for these insects remain ineffective. Challenges arise with the increase in mosquito populations due to climate change, insecticide resistance, and human interference affecting natural habitats, which contribute to the increasing difficulty in controlling the spread of arboviruses. Therefore, prioritizing arbovirus surveillance is essential for effective epidemic preparedness. In this review, we offer a concise historical account of the discovery and monitoring of arboviruses in mosquitoes, from mosquito capture to viral detection. We then analyzed the advantages and limitations of these traditional methods. Furthermore, we investigated the potential of emerging technologies to address these limitations, including the implementation of next-generation sequencing, paper-based devices, spectroscopic detectors, and synthetic biosensors. We also provide perspectives on recurring issues and areas of interest such as insect-specific viruses.

Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest

Hematophagous insects belonging to the Aedes genus are proven vectors of viral and filarial pathogens of medical interest. Aedes albopictus is an increasingly important vector because of its rapid worldwide expansion. In the context of global climate change and the emergence of zoonotic infectious diseases, identification tools with field application are required to strengthen efforts in the entomological survey of arthropods with medical interest. Large scales and proactive entomological surveys of Aedes mosquitoes need skilled technicians and/or costly technical equipment, further puzzled by the vast amount of named species. In this study, we developed an automatic classification system of Aedes species by taking advantage of the species-specific marker displayed by Wing Interferential Patterns. A database holding 494 photomicrographs of 24 Aedes spp. from which those documented with more than ten pictures have undergone a deep learning methodology to train a convolutional neural network and test its accuracy to classify samples at the genus, subgenus, and species taxonomic levels. We recorded an accuracy of 95% at the genus level and > 85% for two (Ochlerotatus and Stegomyia) out of three subgenera tested. Lastly, eight were accurately classified among the 10 Aedes sp. that have undergone a training process with an overall accuracy of > 70%. Altogether, these results demonstrate the potential of this methodology for Aedes species identification and will represent a tool for the future implementation of large-scale entomological surveys.

Monitoring the abundance of flying insects and atmospheric conditions during a 9-month campaign using an entomological optical sensor

Monitoring the dynamics of insect populations is key to assessing the impact of human activities on insect populations. However, traditional methodologies relying on physical traps have inherent limitations in accurately monitoring insect abundance. Here, we present findings from a 9-month campaign conducted in New Jersey, USA, utilizing a near-infrared optical sensor known as eBoss. From April to December 2022, the eBoss derived the aerial density (insect/m3) and biomass density (mg/m3) with a 1-min resolution from a total of 302,093 insect observations. The data collected were analyzed in relation to air temperature, relative humidity, and wind speed. The results revealed that the abundance of flying insects exhibited an initial increase from April to June, reaching a peak of 0.094 insect/m3 and 1.34 mg/m3, followed by a subsequent decline towards the end of the year. Our investigation showed a surge in insect abundance above 12.5 °C, with particularly high levels observed between 19 and 31 °C. The impact of relative humidity and wind speed on insect populations was also explored. Overall, this campaign demonstrated the efficacy of photonic sensors in gathering novel and extensive data for the field of entomology, paving the way for improved understanding and management of insect populations.

Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species

We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additional evidence that this approach can identify Anopheles spp. with an accuracy of up to 100% for ten out of 20 species. Although, this accuracy was moderate (> 65%) or weak (50%) for three and seven species. The accuracy of the process to discriminate cryptic or sibling species is also assessed on three species belonging to the Gambiae complex. Strikingly, An. gambiae, An. arabiensis and An. coluzzii, morphologically indistinguishable species belonging to the Gambiae complex, were distinguished with 100%, 100%, and 88% accuracy respectively. Therefore, this tool would help entomological surveys of malaria vectors and vector control implementation. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.

Dual-Band Infrared Scheimpflug Lidar Reveals Insect Activity in a Tropical Cloud Forest

We describe an entomological dual-band 808 and 980 nm lidar system which has been implemented in a tropical cloud forest (Ecuador). The system was successfully tested at a sample rate of 5 kHz in a cloud forest during challenging foggy conditions (extinction coefficients up to 20 km^-1). At times, the backscattered signal could be retrieved from a distance of 2.929 km. We present insect and bat observations up to 200 m during a single night with an emphasis on fog aspects, potentials, and benefits of such dual-band systems. We demonstrate that the modulation contrast between insects and fog is high in the frequency domain compared to intensity in the time domain, thus allowing for better identification and quantification in misty forests. Oscillatory lidar extinction effects are shown in this work for the first time, caused by the combination of dense fog and large moths partially obstructing the beam. We demonstrate here an interesting case of a moth where left- and right-wing movements induced oscillations in both intensity and pixel spread. In addition, we were able to identify the dorsal and ventral sides of the wings by estimating the corresponding melanization with the dual-band lidar. We demonstrate that the wing beat trajectories in the dual-band parameter space are complementary rather than covarying or redundant, thus a dual-band entomological lidar approach to biodiversity studies is feasible in situ and endows species specificity differentiation. Future improvements are discussed. The introduction of these methodologies opens the door to a wealth of possible experiments to monitor, understand, and safeguard the biological resources of one of the most biodiverse countries on Earth.

A Deep-Learning-Based Detection Approach for the Identification of Insect Species of Economic Importance

Artificial Intelligence (AI) and automation are fostering more sustainable and effective solutions for a wide spectrum of agricultural problems. Pest management is a major challenge for crop production that can benefit from machine learning techniques to detect and monitor specific pests and diseases. Traditional monitoring is labor intensive, time demanding, and expensive, while machine learning paradigms may support cost-effective crop protection decisions. However, previous studies mainly relied on morphological images of stationary or immobilized animals. Other features related to living animals behaving in the environment (e.g., walking trajectories, different postures, etc.) have been overlooked so far. In this study, we developed a detection method based on convolutional neural network (CNN) that can accurately classify in real-time two tephritid species (Ceratitis capitata and Bactrocera oleae) free to move and change their posture. Results showed a successful automatic detection (i.e., precision rate about 93%) in real-time of C. capitata and B. oleae adults using a camera sensor at a fixed height. In addition, the similar shape and movement patterns of the two insects did not interfere with the network precision. The proposed method can be extended to other pest species, needing minimal data pre-processing and similar architecture.

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.

Spatiotemporal mapping of malaria incidence in Sudan using routine surveillance data

Malaria is a serious threat to global health, with over \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$95\%$$\end{document}95% of the cases reported in 2020 by the World Health Organization in African countries, including Sudan. Sudan is a low-income country with a limited healthcare system and a substantial burden of malaria. The epidemiology of malaria in Sudan is rapidly changing due to factors including the rapidly developing resistance to drugs and insecticides among the parasites and vectors, respectively; the growing population living in humanitarian settings due to political instability; and the recent emergence of Anopheles stephensi in the country. These factors contribute to changes in the distribution of the parasites species as well as malaria vectors in Sudan, and the shifting patterns of malaria epidemiology underscore the need for investment in improved situational awareness, early preparedness, and a national prevention and control strategy that is updated, evidence based, and proactive. A key component of this strategy is accurate, high-resolution endemicity maps of species-specific malaria. Here, we present a spatiotemporal Bayesian model, developed in collaboration with the Sudanese Ministry of Health, that predicts a fine-scale (1 km \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document}× 1 km) clinical incidence and seasonality profiles for Plasmodium falciparum and Plasmodium vivax across the country. We use monthly malaria case counts for both species collected via routine surveillance between January 2017 and December 2019, as well as a suite of high-resolution environmental covariates to inform our predictions. These epidemiological maps provide a useful resource for strategic planning and cost-effective implementation of malaria interventions, thus informing policymakers in Sudan to achieve success in malaria control and elimination.

A ResNet attention model for classifying mosquitoes from wing-beating sounds

Mosquitoes are vectors of numerous deadly diseases, and mosquito classification task is vital for their control programs. To ease manual labor and time-consuming classification tasks, numerous image-based machine-learning (ML) models have been developed to classify different mosquito species. Mosquito wing-beating sounds can serve as a unique classifier for mosquito classification tasks, which can be adopted easily in field applications. The current study aims to develop a deep neural network model to identify six mosquito species of three different genera, based on their wing-beating sounds. While existing models focused on raw audios, we developed a comprehensive pre-processing step to convert raw audios into more informative Mel-spectrograms, resulting in more robust and noise-free extracted features. Our model, namely 'Wing-beating Network' or 'WbNet', combines the state-of-art residual neural network (ResNet) model as a baseline, with self-attention mechanism and data-augmentation technique, and outperformed other existing models. The WbNet achieved the highest performance of 89.9% and 98.9% for WINGBEATS and ABUZZ data respectively. For species of Aedes and Culex genera, our model achieved 100% precision, recall and F1-scores, whereas, for Anopheles, the WbNet reached above 95%. We also compared two existing wing-beating datasets, namely WINGBEATS and ABUZZ, and found our model does not need sophisticated audio devices, hence performed better on ABUZZ audios, captured on usual mobile devices. Overall, our model has potential to serve in mosquito monitoring and prevalence studies in mosquito eradication programs, along with potential implementation in classification tasks of insect pests or other sound-based classifications.

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

Background

Every year, more than 700,000 people die from vector-borne diseases, mainly transmitted by mosquitoes. Vector surveillance plays a major role in the control of these diseases and requires accurate and rapid taxonomical identification. New approaches to mosquito surveillance include the use of acoustic and optical sensors in combination with machine learning techniques to provide an automatic classification of mosquitoes based on their flight characteristics, including wingbeat frequency. The development and application of these methods could enable the remote monitoring of mosquito populations in the field, which could lead to significant improvements in vector surveillance.

Methods

A novel optical sensor prototype coupled to a commercial mosquito trap was tested in laboratory conditions for the automatic classification of mosquitoes by genus and sex. Recordings of > 4300 laboratory-reared mosquitoes of Aedes and Culex genera were made using the sensor. The chosen genera include mosquito species that have a major impact on public health in many parts of the world. Five features were extracted from each recording to form balanced datasets and used for the training and evaluation of five different machine learning algorithms to achieve the best model for mosquito classification.

Results

The best accuracy results achieved using machine learning were: 94.2% for genus classification, 99.4% for sex classification of Aedes, and 100% for sex classification of Culex. The best algorithms and features were deep neural network with spectrogram for genus classification and gradient boosting with Mel Frequency Cepstrum Coefficients among others for sex classification of either genus.

Conclusions

To our knowledge, this is the first time that a sensor coupled to a standard mosquito suction trap has provided automatic classification of mosquito genus and sex with high accuracy using a large number of unique samples with class balance. This system represents an improvement of the state of the art in mosquito surveillance and encourages future use of the sensor for remote, real-time characterization of mosquito populations.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05324-5.

Optical Identification of Fruitfly Species Based on Their Wingbeats Using Convolutional Neural Networks

The spotted wing Drosophila (SWD), Drosophila suzukii, is a significant invasive pest of berries and soft-skinned fruits that causes major economic losses in fruit production worldwide. Automatic identification and monitoring strategies would allow to detect the emergence of this pest in an early stage and minimize its impact. The small size of Drosophila suzukii and similar flying insects makes it difficult to identify them using camera systems. Therefore, an optical sensor recording wingbeats was investigated in this study. We trained convolutional neural network (CNN) classifiers to distinguish D. suzukii insects from one of their closest relatives, Drosophila Melanogaster, based on their wingbeat patterns recorded by the optical sensor. Apart from the original wingbeat time signals, we modeled their frequency (power spectral density) and time-frequency (spectrogram) representations. A strict validation procedure was followed to estimate the models' performance in field-conditions. First, we validated each model on wingbeat data that was collected under the same conditions using different insect populations to train and test them. Next, we evaluated their robustness on a second independent dataset which was acquired under more variable environmental conditions. The best performing model, named "InceptionFly," was trained on wingbeat time signals. It was able to discriminate between our two target insects with a balanced accuracy of 92.1% on the test set and 91.7% on the second independent dataset. This paves the way towards early, automated detection of D. suzukii infestation in fruit orchards.

Traditional Knowledge to Contemporary Medication in the Treatment of Infectious Disease Dengue: A Review

Dengue has become a worldwide affliction despite incessant efforts to search for a cure for this long-lived disease. Optimistic consequences for dengue vaccine are implausible as the efficiency is tied to previous dengue virus (DENV) exposure and a very high cost is required for large-scale production of vaccine. Medicinal plants are idyllic substitutes to fight DENV infection since they constitute important components of traditional medicine and show antiviral properties, although the mechanism behind the action of bioactive compounds to obstruct viral replication is less explored and yet to be discovered. This review includes the existing traditional knowledge on how DENV infects and multiplies in the host cells, conscripting different medicinal plants that obtained bioactive compounds with anti-dengue properties, and the probable mechanism on how bioactive compounds modulate the host immune system during DENV infection. Moreover, different plant species having such bioactive compounds reported for anti-DENV efficiency should be validated scientifically via different in vitro and in vivo studies.

Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them

Effective mosquito surveillance and control relies on rapid and accurate identification of mosquito vectors and confounding sympatric species. As adoption of modified mosquito (MM) control techniques has increased, the value of monitoring the success of interventions has gained recognition and has pushed the field away from traditional ‘spray and pray’ approaches. Field evaluation and monitoring of MM control techniques that target specific species require massive volumes of surveillance data involving species-level identifications. However, traditional surveillance methods remain time and labor-intensive, requiring highly trained, experienced personnel. Health districts often lack the resources needed to collect essential data, and conventional entomological species identification involves a significant learning curve to produce consistent high accuracy data. These needs led us to develop MosID: a device that allows for high-accuracy mosquito species identification to enhance capability and capacity of mosquito surveillance programs. The device features high-resolution optics and enables batch image capture and species identification of mosquito specimens using computer vision. While development is ongoing, we share an update on key metrics of the MosID system. The identification algorithm, tested internally across 16 species, achieved 98.4 ± 0.6% % macro F1-score on a dataset of known species, unknown species used in training, and species reserved for testing (species, specimens respectively: 12, 1302; 12, 603; 7, 222). Preliminary user testing showed specimens were processed with MosID at a rate ranging from 181-600 specimens per hour. We also discuss other metrics within technical scope, such as mosquito sex and fluorescence detection, that may further support MM programs.

The impact of body size on Aedes [Stegomyia] aegypti wingbeat frequency: implications for mosquito identification

Billions of people are at risk due to mosquito-borne diseases. Ideally, the control of mosquito-borne diseases should integrate mosquito control and surveillance to maximize transmission prevention while minimizing environmental impacts. Mosquito surveillance is often limited in scope by logistical constraints, especially the labour and expertise in identifying captured mosquitoes. Mosquito sounds, primarily the wingbeat frequencies (WBF), have been extensively studied in the literature, often targeting a straightforward assessment of this technology with species identification in laboratory conditions. Optical sensors for measuring the WBF of free-flying mosquitoes are the most recent proposal to automate species identification. However, many of the factors that may influence WBF within and between species have not been fully examined, resulting in failures in the species identification. Here we show that body size and temperature modify the wingbeat frequency of female Aedes [Stegomyia] aegypti Linnaeus (Diptera:Culicidae) and such an optical sensor can capture these alterations. We demonstrate that this study's optical sensor can distinguish wingbeat frequency from large and small mosquitoes at different temperatures. The relationship between WBF and size should be taken into account to improve the accuracy of devices that automatically identify species using WBF.

Continuous monitoring of aerial density and circadian rhythms of flying insects in a semi-urban environment

Although small in size, insects are a quintessential part of terrestrial ecosystems due to their large number and diversity. While captured insects can be thoroughly studied in laboratory conditions, their population dynamics and abundance in the wild remain largely unknown due to the lack of accurate methodologies to count them. Here, we present the results of a field experiment where the activity of insects has been monitored continuously over 3 months using an entomological stand-off optical sensor (ESOS). Because its near-infrared laser is imperceptible to insects, the instrument provides an unbiased and absolute measurement of the aerial density (flying insect/m3) with a temporal resolution down to the minute. Multiple clusters of insects are differentiated based on their wingbeat frequency and ratios between wing and body optical cross-sections. The collected data allowed for the study of the circadian rhythm and daily activities as well as the aerial density dynamic over the whole campaign for each cluster individually. These measurements have been compared with traps for validation of this new methodology. We believe that this new type of data can unlock many of the current limitations in the collection of entomological data, especially when studying the population dynamics of insects with large impacts on our society, such as pollinators or vectors of infectious diseases.

Recent advances in the remote sensing of insects

Remote sensing has revolutionised many aspects of ecological research, enabling spatiotemporal data to be collected in an efficient and highly automated manner. The last two decades have seen phenomenal growth in capabilities for high-resolution remote sensing that increasingly offers opportunities to study small, but ecologically important organisms, such as insects. Here we review current applications for using remote sensing within entomological research, highlighting the emerging opportunities that now arise through advances in spatial, temporal and spectral resolution. Remote sensing can be used to map environmental variables, such as habitat, microclimate and light pollution, capturing data on topography, vegetation structure and composition, and luminosity at spatial scales appropriate to insects. Such data can also be used to detect insects indirectly from the influences that they have on the environment, such as feeding damage or nest structures, whilst opportunities for directly detecting insects are also increasingly available. Entomological radar and light detection and ranging (LiDAR), for example, are transforming our understanding of aerial insect abundance and movement ecology, whilst ultra-high spatial resolution drone imagery presents tantalising new opportunities for direct observation. Remote sensing is rapidly developing into a powerful toolkit for entomologists, that we envisage will soon become an integral part of insect science.

Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection

With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.

Identification of Flying Insects in the Spatial, Spectral, and Time Domains with Focus on Mosquito Imaging

Insects constitute a very important part of the global ecosystem and include pollinators, disease vectors, and agricultural pests, all with pivotal influence on society. Monitoring and control of such insects has high priority, and automatic systems are highly desirable. While capture and analysis by biologists constitute the gold standard in insect identification, optical and laser techniques have the potential for high-speed detection and automatic identification based on shape, spectroscopic properties such as reflectance and fluorescence, as well as wing-beat frequency analysis. The present paper discusses these approaches, and in particular presents a novel method for automatic identification of mosquitos based on image analysis, as the insects enter a trap based on a combination of chemical and suction attraction. Details of the analysis procedure are presented, and selectivity is discussed. An accuracy of 93% is achieved by our proposed method from a data set containing 122 insect images (mosquitoes and bees). As a powerful and cost-effective method, we finally propose the combination of imaging and wing-beat frequency analysis in an integrated instrument.

Infrared light sensors permit rapid recording of wingbeat frequency and bioacoustic species identification of mosquitoes

Recognition and classification of mosquitoes is a critical component of vector-borne disease management. Vector surveillance, based on wingbeat frequency and other parameters, is becoming increasingly important in the development of automated identification systems, but inconsistent data quality and results frequently emerge from different techniques and data processing methods which have not been standardized on wingbeat collection of numerous species. We developed a simple method to detect and record mosquito wingbeat by multi-dimensional optical sensors and collected 21,825 wingbeat files from 29 North American mosquito species. In pairwise comparisons, wingbeat frequency of twenty six species overlapped with at least one other species. No significant differences were observed in wingbeat frequencies between and within individuals of Culex quinquefasciatus over time. This work demonstrates the potential utility of quantifying mosquito wingbeat frequency by infrared light sensors as a component of an automated mosquito identification system. Due to species overlap, wingbeat frequency will need to integrate with other parameters to accurately delineate species in support of efficient mosquito surveillance, an important component of disease intervention.

Flight tone characterisation of the South American malaria vector Anopheles darlingi (Diptera: Culicidae)

BACKGROUND

Flight tones play important roles in mosquito reproduction. Several mosquito species utilise flight tones for mate localisation and attraction. Typically, the female wingbeat frequency (WBF) is lower than males, and stereotypic acoustic behaviors are instrumental for successful copulation. Mosquito WBFs are usually an important species characteristic, with female flight tones used as male attractants in surveillance traps for species identification. Anopheles darlingi is an important Latin American malaria vector, but we know little about its mating behaviors.

OBJECTIVES

We characterised An. darlingi WBFs and examined male acoustic responses to immobilised females.

METHODS

Tethered and free flying male and female An. darlingi were recorded individually to determine their WBF distributions. Male-female acoustic interactions were analysed using tethered females and free flying males.

FINDINGS

Contrary to most mosquito species, An. darlingi females are smaller than males. However, the male's WBF is ~1.5 times higher than the females, a common ratio in species with larger females. When in proximity to a female, males displayed rapid frequency modulations that decreased upon genitalia engagement. Tethered females also modulated their frequency upon male approach, being distinct if the interaction ended in copulation or only contact.

MAIN CONCLUSIONS

This is the first report of An. darlingi flight acoustics, showing that its precopulatory acoustics are similar to other mosquitoes despite the uncommon male:female size ratio, suggesting that WBF ratios are common communication strategies rather than a physical constraint imposed by size.

Real-time dispersal of malaria vectors in rural Africa monitored with lidar

Lack of tools for detailed, real-time observation of mosquito behavior with high spatio-temporal resolution limits progress towards improved malaria vector control. We deployed a high-resolution entomological lidar to monitor a half-kilometer static transect positioned over rice fields outside a Tanzanian village. A quarter of a million in situ insect observations were classified, and several insect taxa were identified based on their modulation signatures. We observed distinct range distributions of male and female mosquitoes in relation to the village periphery, and spatio-temporal behavioral features, such as swarming. Furthermore, we observed that the spatial distributions of males and females change independently of each other during the day, and were able to estimate the daily dispersal of mosquitoes towards and away from the village. The findings of this study demonstrate how lidar-based monitoring could dramatically improve our understanding of malaria vector ecology and control options.

Deep learning approaches for challenging species and gender identification of mosquito vectors

Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.

Outcomes from international field trials with Male Aedes Sound Traps: Frequency-dependent effectiveness in capturing target species in relation to bycatch abundance

Aedes aegypti and Aedes albopictus vector dengue, chikungunya and Zika viruses. With both species expanding their global distributions at alarming rates, developing effective surveillance equipment is a continuing priority for public health researchers. Sound traps have been shown, in limited testing, to be highly species-specific when emitting a frequency corresponding to a female mosquito wingbeat. Determining male mosquito capture rates in sound traps based on lure frequencies in endemic settings is the next step for informed deployment of these surveillance tools. We field-evaluated Male Aedes Sound Traps (MASTs) set to either 450 Hz, 500 Hz, 550 Hz or 600 Hz for sampling Aedes aegypti and/or Aedes albopictus and compared catch rates to BG-Sentinel traps within Pacific (Madang, Papua New Guinea) and Latin American (Molas, Mexico and Orange Walk Town, Belize) locations. MASTs set to 450-550 Hz consistently caught male Ae. aegypti at rates comparable to BG-Sentinel traps in all locations. A peak in male Ae. albopictus captures in MASTs set at 550 Hz was observed, with the lowest mean abundance recorded in MASTs set to 450 Hz. While significantly higher abundances of male Culex were sampled in MASTs emitting lower relative frequencies in Molas, overall male Culex were captured in significantly lower abundances in the MASTs, relative to BG-Sentinel traps within all locations. Finally, significant differences in rates at which male Aedes and Culex were positively detected in trap-types per weekly collections were broadly consistent with trends in abundance data per trap-type. MASTs at 550 Hz effectively captured both male Ae. aegypti and Ae. albopictus while greatly reducing bycatch, especially male Culex, in locations where dengue transmission has occurred. This high species-specificity of the MAST not only reduces staff-time required to sort samples, but can also be exploited to develop an accurate smart-trap system-both outcomes potentially reducing public health program expenses.

Advances in automatic identification of flying insects using optical sensors and machine learning

Worldwide, farmers use insecticides to prevent crop damage caused by insect pests, while they also rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests. In order to target pesticides to pests only, farmers must know exactly where and when pests and beneficial insects are present in the field. A promising solution to this problem could be optical sensors combined with machine learning. We obtained around 10,000 records of flying insects found in oilseed rape (Brassica napus) crops, using an optical remote sensor and evaluated three different classification methods for the obtained signals, reaching over 80% accuracy. We demonstrate that it is possible to classify insects in flight, making it possible to optimize the application of insecticides in space and time. This will enable a technological leap in precision agriculture, where focus on prudent and environmentally-sensitive use of pesticides is a top priority.

Next-generation tools to control biting midge populations and reduce pathogen transmission

Biting midges of the genus Culicoides transmit disease-causing agents resulting in a significant economic impact on livestock industries in many parts of the world. Localized control efforts, such as removal of larval habitat or pesticide application, can be logistically difficult, expensive and ineffective if not instituted and maintained properly. With these limitations, a population-level approach to the management of Culicoides midges should be investigated as a means to replace or supplement existing control strategies. Next-generation control methods such as Wolbachia- and genetic-based population suppression and replacement are being investigated in several vector species. Here we assess the feasibility and applicability of these approaches for use against biting midges. We also discuss the technical and logistical hurdles needing to be addressed for each method to be successful, as well as emphasize the importance of addressing community engagement and involving stakeholders in the investigation and development of these approaches.

Lidar reveals activity anomaly of malaria vectors during pan-African eclipse

Yearly, a quarter billion people are infected and a half a million killed by the mosquito-borne disease malaria. Lack of real-time observational tools for continuously assessing the unperturbed mosquito flight activity in situ limits progress toward improved vector control. We deployed a high-resolution entomological lidar to monitor a half-kilometer static transect adjacent to a Tanzanian village. We evaluated one-third million insect observations during five nights, four days, and one annular solar eclipse. We demonstrate in situ lidar classification of several insect families and their sexes based on their modulation signatures. We were able to compare the fine-scale spatiotemporal activity patterns of malaria vectors during ordinary days and an eclipse to disentangle phototactic activity patterns from the circadian mechanism. We observed an increased insect activity during the eclipse attributable to mosquitoes. These unprecedented findings demonstrate how lidar-based monitoring of distinct mosquito activities could advance our understanding of vector ecology.

Identification of gravid mosquitoes from changes in spectral and polarimetric backscatter cross sections

Improving the survey of mosquito populations is of the utmost importance to further enhance mitigation techniques that protect human populations from mosquito-borne diseases. While mosquito populations are generally studied using physical traps, stand-off optical sensors allow to study insect ecosystems with potentially better spatial and temporal resolution. This can be greatly beneficial to eco-epidemiological models and various mosquito control programs. In this contribution, we demonstrate that the gravidity of female mosquitoes can be identified from changes in their spectral and polarimetric backscatter cross sections. Among other predictive variables, the wing beat frequency and the depolarization ratio of the mosquito body allows for the identification of gravid females with a precision and recall of 86% and 87%, respectively. Since female mosquitoes need a blood meal to become gravid, statistics on gravidity is of prime importance as only females that have been gravid might carry infectious diseases. In addition, it allows to detect possible breeding habitat, predict a potential increase in the mosquito population and provide a better overall understanding of the ecosystem dynamics. As a result, targeted and localized mitigation techniques can be used, reducing the cost and improving the efficiency of mosquito population control.

A Multispectral Backscattered Light Recorder of Insects’ Wingbeats

Most reported optical recorders of the wingbeat of insects are based on the so-called extinction light, which is the variation of light in the receiver due to the cast shadow of the insect’s wings and main body. In this type of recording devices, the emitter uses light and is placed opposite to the receiver, which is usually a single (or multiple) photodiode. In this work, we present a different kind of wingbeat sensor and its associated recorder that aims to extract a deeper representational signal of the wingbeat event and color characterization of the main body of the insect, namely: a) we record the backscattered light that is richer in harmonics than the extinction light, b) we use three different spectral bands, i.e., a multispectral approach that aims to grasp the melanization and microstructural and color features of the wing and body of the insects, and c) we average at the receiver’s level the backscattered signal from many LEDs that illuminate the wingbeating insect from multiple orientations and thus offer a smoother and more complete signal than one based on a single snapshot. We present all the necessary details to reproduce the device and we analyze many insects of interest like the bee Apis mellifera, the wasp Polistes gallicus, and some insects whose wingbeating characteristics are pending in the current literature, like Drosophila suzukii and Zaprionus, another member of the drosophilidae family.

Analysis of predictor variables for mosquito species identification from dual-wavelength polarization-sensitive lidar measurements

Mosquito-borne diseases are a major challenge for Human health as they affect nearly 700 million people every year. Monitoring insects is generally done through trapping methods that are tedious to set up, costly and present scientific biases. Entomological lidars are a potential solution to remotely count and identify mosquito species and gender in real-time. In this contribution, a dual-wavelength polarization sensitive lidar is used in laboratory conditions to retrieve the wingbeat frequency as well as optical properties of flying mosquitoes transiting through the laser beam. From the lidar signals, predictive variables are retrieved and used in a Bayesian classification. This paper focuses on determining the relative importance of the predictive variables used in the classification. Results show a strong dominance of the wingbeat frequency, the impact of predictive variables based on depolarization and backscattering ratios are discussed, showing a significant increase in classification accuracy.