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

Use of Artificial Intelligence on the Control of Vector-Borne Diseases

Artificial intelligence has many fields of application with an increasing computational processing power, and the algorithms are reaching human performance on complex tasks. Entomological characterization of insects represents an essential activity to drive actions to control the vector-borne diseases. Identification of the species and sex of insects is essential to map and organize the control measurements by the public health system in most areas where transmission is actively occurring. In many places in the world, the methodology done for identification of the mosquitos is by visual examination from human trained researchers or technicians. This activity is time-consuming and requires several years of experience to have skills to do the job. This chapter addresses the application of artificial intelligence for identification of mosquitos associated with vector-borne diseases. Benefits, limitations, and challenges of the use of artificial intelligence on the control of vector-borne diseases are discussed in this review

Acoustic Identification of Ae. aegypti Mosquitoes using Smartphone Apps and Residual Convolutional Neural Networks

In this paper, we advocate in favor of smartphone apps as low-cost, easy-to-deploy solution for raising awareness among the population on the proliferation of Aedes aegypti mosquitoes. Nevertheless, devising such a smartphone app is challenging, for many reasons, including the required maturity level of techniques for identifying mosquitoes based on features that can be captured using smartphone resources. In this paper, we identify a set of (non-exhaustive) requirements that smartphone apps must meet to become an effective tooling in the fight against Ae. aegypti, and advance the state-of-the-art with (i) a residual convolutional neural network for classifying Ae. aegypti mosquitoes from their wingbeat sound, (ii) a methodology for reducing the influence of background noise in the classification process, and (iii) a dataset for benchmarking solutions for detecting Ae. aegypti mosquitoes from wingbeat sound recordings. From the analysis of accuracy and recall, we provide evidence that convolutional neural networks have potential as a cornerstone for tracking mosquito apps for smartphones

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

Signal classification by similarity and feature extraction allows an important application in insect recognition

Insects have a strong relationship with the humanity, in both positive and negative ways. It is estimated that insects, particularly bees, pollinate at least twothirds of all food consumed in the world. In contrast, mosquito borne diseases kill millions of people every year. Due to such a complex relationship, insect control attempts must be carefully planned. Otherwise, there is the risk of eliminating beneficial species, such as the recent threat of bee extinction. We are developing a\ud novel sensor as a tool to control disease vectors and agricultural pests. This sensor captures insect flight information using laser light and classify the insects according to their species. Therefore, the sensor will provide real-time population estimates of species. Such information is the key to enable effective alarming systems for outbreaks, the intelligent use of insect\ud control techniques, such as insecticides, and will be the heart of the next generation of insect traps that will capture only species of interest. In this paper, we demonstrate how we overtook the most importante challenge to make this sensor practical: the creation of accurate classification systems. The sensor generates\ud a very brief signal as result of the instant that the insect crosses the laser. Such events last for tenths of a second and have a very simple structure, consequence of the wings movements. Nevertheless, we managed to successfully identify relevant features using speech and audio analysis techniques. Even with the described challenges, we show that we can achieve an accuracy of 98% in the task of disease vector mosquitoes identification.São Paulo Research Foundation (FAPESP) (Grants #2011/04054-2 and #2012/50714-7

Recognition and Early Stage Detection of <em>Phytophthora</em> in a Crop Farm Using IoT

Detection of agricultural plant pests is seen as one of the farmers’ problems. Automated Pest Detection Machine enables early detection of crop insects with advanced computer vision and image recognition. Innovative research in the field of agriculture has demonstrated a new direction by Internet of Things (IoT). IoT needs to be widely experienced at the early stage, so that it is widely used in different farming applications. It allows farmers increase their crop yield with reduced time and greater precision. For the past decade, climate change and precipitation have been unpredictable. Due to this, many Indian farmers are adopting smart methods for environment known as intelligent farming. Smart farming is an automated and IOT-based information technology (Internet of Things). In all wireless environments IOT is developing quickly and widely. The Internet of Things helps to monitor agricultural crops and thus quickly and effectively increase farmers’ income. This paper presents a literature review on IoT devices for recognizing and detecting insects in crop fields. Different types of framework/models are present which are explaining the procedure of insect detection

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

Automated classification of bees and hornet using acoustic analysis of their flight sounds

International audienceAbstractTo investigate how to accurately identify bee species using their sounds, we conducted acoustic analysis to identify three pollinating bee species (Apis mellifera, Bombus ardens, Tetralonia nipponensis) and a hornet (Vespa simillima xanthoptera) by their flight sounds. Sounds of the insects and their environment (background noises and birdsong) were recorded in the field. The use of fundamental frequency and mel-frequency cepstral coefficients to describe feature values of the sounds, and supported vector machines to classify the sounds, correctly distinguished sound samples from environmental sounds with high recalls and precision (0.96–1.00). At the species level, our approach could classify the insect species with relatively high recalls and precisions (0.7–1.0). The flight sounds of V.s. xanthoptera, in particular, were perfectly identified (precision and recall 1.0). Our results suggest that insect flight sounds are potentially useful for detecting bees and quantifying their activity