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

Photonic monitoring of atmospheric fauna

Insects play a quintessential role in the Earth’s ecosystems and their recent decline in abundance and diversity is alarming. Monitoring their population is paramount to understand the causes of their decline, as well as to guide and evaluate the efficiency of conservation policies. Monitoring populations of flying insects is generally done using physical traps, but this method requires long and expensive laboratory analysis where each insect must be identified by qualified personnel. Lack of reliable data on insect populations is now considered a significant issue in the field of entomology, often referred to as a “data crisis” in the field. This doctoral work explores the potential of entomological photonic sensors to unlock some of the limitations of traditional methods. This work focuses on the development of optical instruments similar in essence to lidar systems, with the goal of counting and identifying flying insects from a distance in their natural habitat. Those systems rely on the interactions between the near-infrared laser light and insects flying through the laser beam. Each insect is characterized by retrieving its optical and morphological properties, such as wingbeat frequency, optical cross sections, or depolarization ratios. This project ran in parallel a series of laboratory and field experiments. In the laboratory, prototypes were tested and used to create a database of insects’ properties. The data were used to train machine learning classifiers aiming at identifying insects from optical signals. In the case of mosquitoes, the sex and species of an unknown specimen was predicted with a 99% and 80% accuracy respectively. It also showed that the presence of eggs within the abdomen of a female mosquito could be detected from several meters away with 87% accuracy. In the field, instruments were deployed in real-world conditions for a total of 520 days over three years. More than a million insects were observed, allowing to continuously monitor their aerial density over months with a temporal resolution down to the minute. While this approach remains very new, this work demonstrated that photonic sensors could become a powerful tool to tackle the current lack of data in the field of entomology

Living IoT: A Flying Wireless Platform on Live Insects

Sensor networks with devices capable of moving could enable applications ranging from precision irrigation to environmental sensing. Using mechanical drones to move sensors, however, severely limits operation time since flight time is limited by the energy density of current battery technology. We explore an alternative, biology-based solution: integrate sensing, computing and communication functionalities onto live flying insects to create a mobile IoT platform. Such an approach takes advantage of these tiny, highly efficient biological insects which are ubiquitous in many outdoor ecosystems, to essentially provide mobility for free. Doing so however requires addressing key technical challenges of power, size, weight and self-localization in order for the insects to perform location-dependent sensing operations as they carry our IoT payload through the environment. We develop and deploy our platform on bumblebees which includes backscatter communication, low-power self-localization hardware, sensors, and a power source. We show that our platform is capable of sensing, backscattering data at 1 kbps when the insects are back at the hive, and localizing itself up to distances of 80 m from the access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang, In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201

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

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

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs