Mosquito Detection with Neural Networks: The Buzz of Deep Learning

Many real-world time-series analysis problems are characterised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied with classification or regression engines which condition on this (often low-dimensional) feature vector. The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets. This paper presents an application of deep learning for acoustic event detection in a challenging, data-scarce, real-world problem. Our candidate challenge is to accurately detect the presence of a mosquito from its acoustic signature. We develop convolutional neural networks (CNNs) operating on wavelet transformations of audio recordings. Furthermore, we interrogate the network's predictive power by visualising statistics of network-excitatory samples. These visualisations offer a deep insight into the relative informativeness of components in the detection problem. We include comparisons with conventional classifiers, conditioned on both hand-tuned and generic features, to stress the strength of automatic deep feature learning. Detection is achieved with performance metrics significantly surpassing those of existing algorithmic methods, as well as marginally exceeding those attained by individual human experts.Comment: For data and software related to this paper, see http://humbug.ac.uk/kiskin2017/. Submitted as a conference paper to ECML 201

An Inexpensive Flying Robot Design for Embodied Robotics Research

Flying insects are capable of a wide-range of flight and cognitive behaviors which are not currently understood. The replication of these capabilities is of interest to miniaturized robotics, because they share similar size, weight, and energy constraints. Currently, embodiment of insect behavior is primarily done on ground robots which utilize simplistic sensors and have different constraints to flying insects. This limits how much progress can be made on understanding how biological systems fundamentally work. To address this gap, we have developed an inexpensive robotic solution in the form of a quadcopter aptly named BeeBot. Our work shows that BeeBot can support the necessary payload to replicate the sensing capabilities which are vital to bees' flight navigation, including chemical sensing and a wide visual field-of-view. BeeBot is controlled wirelessly in order to process this sensor data off-board; for example, in neural networks. Our results demonstrate the suitability of the proposed approach for further study of the development of navigation algorithms and of embodiment of insect cognition

The use of artificial intelligence and automatic remote monitoring for mosquito surveillance

Mosquito surveillance consists in the routine monitoring of mosquito populations: to determine the presence/absence of certain mosquito species; to identify changes in the abundance and/or composition of mosquito populations; to detect the presence of invasive species; to screen for mosquito-borne pathogens; and, finally, to evaluate the effectiveness of control measures. This kind of surveillance is typically performed by means of traps, which are regularly collected and manually inspected by expert entomologists for the taxonomical identification of the samples. The main problems with traditional surveillance systems are the cost in terms of time and human resources and the lag that is created between the time the trap is placed and collected. This lag can be crucial for the accurate time monitoring of mosquito population dynamics in the field, which is determinant for the precise design and implementation of risk assessment programs. New perspectives in this field include the use of smart traps and remote monitoring systems, which generate data completely interoperable and thus available for the automatic running of prediction models; the performance of risk assessments; the issuing of warnings; and the undertaking of historical analyses of infested areas. In this way, entomological surveillance could be done automatically with unprecedented accuracy and responsiveness, overcoming the problem of manual inspection labour costs. As a result, disease vector species could be detected earlier and with greater precision, enabling an improved control of outbreaks and a greater protection from diseases, thereby saving lives and millions of Euros in health costs.info:eu-repo/semantics/publishedVersio

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 mosqui‑ toes. Vector surveillance plays a major role in the control of these diseases and requires accurate and rapid taxo‑ nomical identifcation. New approaches to mosquito surveillance include the use of acoustic and optical sensors in combination with machine learning techniques to provide an automatic classifcation of mosquitoes based on their fight characteristics, including wingbeat frequency. The development and application of these methods could enable the remote monitoring of mosquito populations in the feld, which could lead to signifcant improvements in vector surveillance. Methods: A novel optical sensor prototype coupled to a commercial mosquito trap was tested in laboratory conditions for the automatic classifcation 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 fve diferent machine learning algorithms to achieve the best model for mosquito classifcation. Results: The best accuracy results achieved using machine learning were: 94.2% for genus classifcation, 99.4% for sex classifcation of Aedes, and 100% for sex classifcation of Culex. The best algorithms and features were deep neural network with spectrogram for genus classifcation and gradient boosting with Mel Frequency Cepstrum Coefcients among others for sex classifcation of either genus. Conclusions: To our knowledge, this is the frst time that a sensor coupled to a standard mosquito suction trap has provided automatic classifcation 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.info:eu-repo/semantics/publishedVersio

The potential of bioacoustics for surveying carrion insects

Knowledge of the sequential cadaver colonization by carrion insects is fundamental for post-mortem interval (PMI) estimation. Creating local empirical data on succession by trapping insects is time consuming, dependent on accessibility/environmental conditions and can be biased by sampling practices including disturbance to decomposing remains and sampling interval. To overcome these limitations, audio identification of species using their wing beats is being evaluated as a potential tool to survey and build local databases of carrion species. The results could guide the focus of forensic entomologists for further developmental studies on the local dominant species, and ultimately to improve PMI estimations. However, there are challenges associated with this approach that must be addressed. Wing beat frequency is influenced by both abiotic and biotic factors including temperature, humidity, age, size, and sex. The audio recording and post-processing must be customized for different species and their influencing factors. Furthermore, detecting flight sounds amid background noise and a multitude of species in the field can pose an additional challenge. Nonetheless, previous studies have successfully identified several fly species based on wing beat sounds. Combined with advances in machine learning, the analysis of bioacoustics data is likely to offer a powerful diagnostic tool for use in species identification.</p

A novel methodology for recording wing beat frequencies of untethered male and female Aedes aegypti

Aedes aegypti is a vector of many significant arboviruses worldwide including dengue, Zika, chikungunya and yellow fever viruses. With vector control methodology pivoting towards rearing and releasing large numbers of insects for either population suppression or virus-blocking, economical remote (sentinel) surveillance methods for release tracking become increasingly necessary. Recent steps in this direction include advances in optical sensors that identify and classify insects based on their wing beat frequency (WBF). As these traps are being developed, there is a strong need to better understand the environmental and biological factors influencing mosquito WBFs. Here, we developed new untethered-subject methodology to detect changes in WBFs of male and female Ae. aegypti. This new methodology involves directing an ultrasonic transducer at a free-flying subject and measuring the Doppler shift of the reflected ultrasonic continuous wave signal. This system’s utility was assessed by determining its ability to confirm previous reports on the effect of temperature, body size and age on the WBFs generated from acoustic or optical-based experiments. The presented ultrasonic method successfully detected expected trends for each factor for both male and female Ae. aegypti without the need for subject manipulation and potential impediment of natural flight dynamics due to tethering. As a result, this ultrasonic methodology provides a new method for understanding the environmental and physiological determinants of male and female WBFs which can inform the design of remote mosquito surveillance systems

Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology&nbsp;at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution&nbsp;of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing&nbsp;technology, which enables the observation of the Earth’s surface and is currently very common in environmental&nbsp;research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of&nbsp;data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned&nbsp;aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature&nbsp;dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and&nbsp;conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and&nbsp;which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the&nbsp;vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of&nbsp;the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count&nbsp;birds and mammals, especially those living in the water. Generally, the analytical part of the present study was&nbsp;divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring&nbsp;the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and&nbsp;(4) population and behaviour studies of animals. At the end, we also synthesised all the information showing,&nbsp;amongst others, the advances in environmental biology because of UAV application. Considering that 33% of&nbsp;studies found and included in this review were published in 2017 and 2018, it is expected that the number and&nbsp;variety of applications of UAVs in environmental biology will increase in the future

Detecting Invasive Insects with Unmanned Aerial Vehicles

A key aspect to controlling and reducing the effects invasive insect species have on agriculture is to obtain knowledge about the migration patterns of these species. Current state-of-the-art methods of studying these migration patterns involve a mark-release-recapture technique, in which insects are released after being marked and researchers attempt to recapture them later. However, this approach involves a human researcher manually searching for these insects in large fields and results in very low recapture rates. In this paper, we propose an automated system for detecting released insects using an unmanned aerial vehicle. This system utilizes ultraviolet lighting technology, digital cameras, and lightweight computer vision algorithms to more quickly and accurately detect insects compared to the current state of the art. The efficiency and accuracy that this system provides will allow for a more comprehensive understanding of invasive insect species migration patterns. Our experimental results demonstrate that our system can detect real target insects in field conditions with high precision and recall rates.Comment: IEEE ICRA 2019. 7 page