Pest detection system and method

Abstract

A device for sensing the presence of pests including bed bugs comprises a sensing arrangement comprising one or more volatile organic compound gas sensors configured to detect the presence of one or m

Description

The present invention provides a device for sensing the presence of pests including, but not limited to, bed bugs, and the use of such devices in the methods for the detection of pest insect infestations. In particular, the device is a smart device that comprises a sensing arrangement and an associated controller that together operate to autonomously detect the presence of said pests and, optionally, distinguish between pests of different species. Also provided is a method for training said smart device to detect the current or past presence of a live population of pests. Background of the Invention A wide variety of pests have been biting, or otherwise causing nuisance to, people since the beginning of recorded time. A prime example of such a pest is the common bed bug, Cimex lectularius, which feeds upon human blood, and has a worldwide distribution. The biting nuisance caused by an infestation of such a pest can have physical and mental effects on the human host. Blood feeding can result in allergic reactions to vasodilatory substances in the bed bug saliva and insect bite wounds can become infected by opportunistic pathogens, if inappropriately treated or in immunocompromised individuals. Furthermore, realisation of a domestic bed bug infestation can affect mental health, causing a range of problems, including emotional distress, anxiety, insomnia and paranoia. For most of the past 40 years, C. lectularius has been an unimportant pest in developed countries. However, in recent years, bed bugs have re-emerged as a significant global pest, with increased rates of infestations occurring in North America, Europe, Australia and elsewhere. The banning of certain insecticides, resistance to other insecticides and the increase in international travel are cited as the main reasons for the re-emergence of this pest problem. Bed bug infestations have a large impact on the hotel and hospitality industry which can be extremely costly. In the US, it is reported to represent around 20% of the US$4 billion extermination insect and pest control services sector. It has grown to be the third largest segment of this market. Monitoring of bed bug and other pest infestations, both spatially and temporally, is essential for the development of targeted treatments. Early detection of bed bug activity 1 is acknowledged as a key factor in reducing the disruption, cost and effort of remedial treatment. The monitoring of bed bug populations can also be used to assess the efficacy of remedial and preventative treatment. However, many currently available pest trap devices, such as simple sticky traps, are based on the principle of random interception and are not efficient at monitoring or controlling pest populations. Many pests, including bed bugs, will preferentially crawl under them, and they are not sufficiently sensitive to detect low-level infestations. Refuge-type monitors also detect the presence of an infestation if the pest insects use the device as a temporary or permanent refuge. However, as with interceptive devices, these will be unlikely to detect low-level infestations. Traps that claim to attract bed bugs currently on the market use either heat or chemical lures or both. However, there are few scientific studies that have tested the efficacy of these devices. Some devices use CO2 to attract bed bugs. However, traps baited with CO2 are expensive and their maintenance is time consuming. Therefore, it is unlikely that such a device would be suitable for long term, wide scale bed bug surveillance. Furthermore, there are safety issues associated with the use of pressurised gas and / or chemical reactions for the production of CO2. In addition, surveillance of pest infestations using these conventional traps requires the user to regularly inspect deployed traps for evidence of infestation which, depending on the size of the surveillance area and number of traps deployed, may be a hugely time consuming and almost unsurmountable task. Therefore, with the high and increasing global prevalence of the common bed bug and other pest insects, there is an urgent need for a discrete, quick and highly sensitive pest detection device that is capable of being trained to automatically detect, distinguish and notify a user of the presence of an infestation of as few as a couple or even a single pest organism, ideally without needing to physically inspect the device for evidence of an infestation. We herein disclose a device that addresses these needs, the device comprising a sensing arrangement and an associated controller that together operate to autonomously detect and indicate the presence of said pests and, optionally, distinguish between pests of different species. Statements of Invention The present invention, in its various aspects, is as set out in the accompanying claims. According to a first aspect of the invention there is provided a pest detection device, said device comprising: a. A sensing arrangement configured to detect the presence of one or more volatile organic compounds (VOCs) or other airborne indicators of pest presence, and to generate one or more signals; b. A controller in communication with said sensing arrangement, configured to receive said signals to indicate the presence of a live population of said pests; and c. A power source configured to provide power to said controller and said sensing arrangement, wherein said sensing arrangement comprises one or more VOC gas sensors. Such an arrangement has been shown to be a highly sensitive pest detection device, that is capable of being trained to automatically detect the presence of as few as a couple or even a single pest organism in a test environment. It will be appreciated that the device can be trained to automatically detect the presence of a pest organism based on species specific VOC based indicator signature(s). Therefore, the device can be adapted to detect and, optionally, distinguish between, any of a wide variety of pests whose presence, either current or past, will result in the generation of a measurable indicative VOC signature. The device of the present invention is not, therefore, limited in its broadest embodiments to the detection of any particular pest species. However, in preferred embodiments, the pest is an arthropod, and more preferably is an arachnid or an insect. Moreover, test data has shown that the device provides exceptional sensitivity in the specific detection of insects, in particular bed bugs. Therefore, in particularly preferred embodiments, the pest detection device is an insect detection device, and still more preferably is a bed bug detection device. As used herein, the term “bed bug” denotes insects in the family Cimicidae, and preferably Cimex spp. A particularly preferred Cimex spp is Cimex lectularius, the common bed bug. However, the device of the present invention is also provided for use in detecting other Cimex spp, including Cimex hemipterus, the tropical bed bug. 3 However, as noted above other pest insects could also be detected, aggregated, and / or otherwise monitored using the device of the invention, and so the devices can therefore also be used to detect infestations of pest insects other than bed bugs such as pests of the Blattidae family, and preferably of the order Blatella; pests of the Lepismatidae family, and preferably of the order Lepisma; or pests of the Tineidae family, and preferably of the order Tineola. As used herein the term “airborne indicators of pest presence” refers to any measurable VOC signal that is generated within an environment as a consequence of, and attributable to, the presence of said pest. In preferred embodiments, said indicators are VOCs that are emitted by a live population of said pests, and / or are VOCs that are emitted as a consequence of the past presence of said pests such as VOCs emitted from faecal matter and / or dead pests. However, suitable indicators may alternatively or additionally include VOCs emitted by the environment as a consequence of the presence of said pests (e.g. as a result of damage to crops caused by feeding). The sensitivity of the device may be further enhanced by the addition of supplementary sensors to the sensing arrangement. Therefore, in preferred embodiments, the sensing arrangement further comprises one or more supplementary sensors selected from optical sensors, movement sensors, heat sensors and combinations thereof. In order to maximise device sensitivity, the sensing arrangement may be housed within a partially enclosed environment, so that the VOC and / or other airborne indicators of pest presence can flow towards and be spatially restrained in or around the proximity of the VOC gas sensors. Therefore, in preferred embodiments, the sensing arrangement is housed within a chambered structure comprising but not limited to at least one exterior wall defining an internal chamber having at least one inlet in fluid communication with the exterior surface of the chambered structure. Alternatively or additionally, the device may be disposed in an outer housing. Such a housing may advantageously be adapted to include one or more handles to assist with transporting the device, and / or include one or more mounting points. The device of the invention comprises one or more VOC gas sensors, which detect the presence of VOCs or other airborne indicators of pest presence. However, it has been found that not all VOC gas sensors show the necessary sensitivity to distinguish live pest odour profiles from clean air and / or previous pest odour profiles. For example, thermal conductivity, photo-ionization and infrared based sensors have been shown to be unsuitable for the detection of insects, in particular bed bugs. Therefore, in preferred embodiments, the one or more VOC gas sensors are independently selected from electrochemical gas sensors and metal oxide (MOX) gas sensors. In exemplary embodiments, the one or more VOC gas sensors are MOX sensors, which have been surprisingly found to show the best accuracy, both in single and multiple sensor configurations. As will be readily appreciated, a MOX sensor refers a sensor comprising a heatable metal oxide surface that changes its electrical resistance depending on the oxygen content on its surface, with reducing gases such as VOCs (which would consume oxygen by being combusted on the heated metal oxide surface) reducing resistance. A wide variety of MOX sensors are available commercially and / or could be easily manufactured, any of which can be incorporated into the device of the present invention. Examples of suitable, commercially available MOX sensors include the following: SGP41 (Sensiron); ZMOD4510 (Renesas); ZMOD4410 (Renesas); ZMOD4450 (Renesas); BME668 (Bosch); ENS160 (Sciosense); and MiCS-6814 (SGX Sensortech). In exemplary devices, the MOX sensors are independently selected from SGP41 and ZMOD4510 MOX sensors. The detection accuracy of the device of the invention can be increased by including multiple VOC gas sensors. Therefore, in preferred embodiments, the device comprises at least 2 VOC gas sensors, and more preferably at least 2 MOX sensors. However, it is believed that, if the total number of sensors increases, the risk of false positive results may also increase due to, e.g., signal noise. Therefore, in preferred embodiments, the device preferably comprises no more than 10, more preferably no more than 8 and still more preferably no more than 4 of said VOC gas sensors. As noted above, the invention provides a highly sensitive pest detection device, that can automatically detect the presence of as few as a couple, or even a single, pest organism in a test environment. To achieve automated detection, the device preferably comprises a controller that is configured to receive signals from the VOC gas sensors, and apply a pest specific detection algorithm to determine the current or past presence of a population of said pest. In particularly preferred embodiments, the controller is configured to apply a pest specific detection algorithm to determine the presence of a 5 live or dead population of said pests, or to determine contamination or damage caused by said pests. As will be readily appreciated, the invention is not limited to any particular pest specific detection algorithm, which will depend upon multiple factors including but not limited to, the pest to be identified along with sensor type, positioning and heating rates. Therefore, the pest specific detection algorithm is derived by implementing a device training method wherein any suitable machine learning or mathematically derived model is applied to training data obtained in the presence and / or absence of said pest. Therefore, this device training method forms a separate aspect of the invention, and is described elsewhere in more detail. However, in an exemplary embodiment of the first aspect, the device of the invention includes a sensing arrangement comprising two or more MOX sensors, and said algorithm is a machine learning model or a mathematically derived model to determine and / or distinguish between the presence of a live population of said pests, evidence of contamination or damage caused by said pests, or no evidence of current or past pest presence. The device of the present invention may also comprise at least one indicator that is configured to provide an output signal to a user of the device in response to the controller determining the current or past presence of a live population of said pests. As will be readily appreciated, such an indicator may produce a visual, audio and / or haptic alert signal. Therefore, in preferred embodiments, said indicator comprises one or more lights, speakers and / or motors that are configured to produce a visual, audio and / or haptic alert signal. Such indicators may include device specific hardware or may be in form of a multi-purpose device such as a computer or telephone, that produce an alert signal in the form of a notification delivered through an application or message. Preferably, the at least one indicator is positioned remote from the controller and, in particularly preferred embodiments, is configured to connect to said controller via wireless communication such as by Bluetooth, Wi-Fi or radio. However, in an alternative embodiment, the controller indicator is configured to connect to the controller via wired, e.g. electrical or optical fibre, connection. Alternatively, the indicator may be positioned on or within the device. According to a second aspect, the invention provides a method for training a pest detection device to detect the current or past presence of a live population of pests, wherein said method comprises: a. providing a pest detection device according to the first aspect of the invention; b. obtaining one or more first training data sets by recording said one or more sensor signal outputs when said live pests are not present; c. obtaining one or more second training data sets by recording said one or more sensor signal outputs in the presence of a live population of said pests, or in an environment damaged or contaminated by the past presence of said pests; d. implementing a machine learning model or mathematically derived model using said first and second training data sets obtained in steps b) and c) as inputs to generate a pest specific detection algorithm; and e. configuring said controller to apply, when the device is in use, said pest specific detection algorithm to said one or more sensor signals to determine the presence or absence of a live population of said pests; and / or contamination or damage caused by said pests, and, based on the determined presence of said live pests, or on said contamination or damage caused by said pests, provide an output signal confirming the need for corrective or control measures. It will be appreciated that the device of the invention may on occasion be used together with one or more pest attractant VOC compounds, and that such attractant compounds may mask the VOC signature of the target pest. Therefore, in preferred embodiments, the first training data sets and / or the said second training data sets are obtained in the presence of a pest attractant chemical lure. However, in alternative embodiments, the first training data sets and said second training data sets are obtained in the absence of a pest attractant chemical lure, particularly if the device will not be used together with pest attractant VOC compounds. Preferably, the method trains the device to detect the presence of an arthropod, more preferably an arachnid or an insect, and still more preferably a bed bug. However, in alternative embodiments, the method may train the device to detect the presence of a pest insect other than bed bugs such as pests of the Blattidae, Lepismatidae, and / or Tineidae family. Preferably, the first training data sets comprise at least one data set collected when no pests of any species are present, and at least one data set collected when a population of pests of a species other than that to be detected is present. Such a combination of negative control data helps to develop a detection algorithm that is capable of distinguishing between pest infestations from different species. Alternatively or additionally, the said second training data sets preferably comprises at least one data set collected on the presence of a live population of said pests, and at least one data set collected in the presence of faeces and / or exuviae indicative of a past infestation of said pest to be detected. Such a combination of test data sets helps to develop a detection algorithm that is capable of distinguishing between current and past pest infestations. According to a third aspect, the invention provides a pest detection device that is trained to detect the presence of a population of pests according to the method of the second aspect of the invention. According to a fourth aspect, the invention provides a method for detecting the presence of a live population of pests, said method comprising placing one or more pest detection devices according to the first or third aspect of the invention in an environment to be monitored. In preferred embodiments, the device is coupled to a dedicated communication hub that allows for a remote alert to pest presence to be sent securely to a user. As will be readily appreciated, the pest detection device may be disposed in any environment in which pest detection is required. Therefore, in some embodiments, the device is placed in an enclosed space such as room or vehicle, and is optionally mounted to a piece of furniture such as a bed. Suitable enclosed spaces include, but are not limited to, any lodgings or sleeping quarters, as well as shipping containers, livestock barns, supermarket stockrooms, shopfloors, and commercial vehicles. Alternatively, the device may be placed in an outdoor environment, and can be used, e.g., to detect forestry and crop damaging pests. Preferred features of each aspect of the invention may be as described in connection with any of the other aspects. Throughout the description and claims of this specification, except where the context requires otherwise due to express language or necessary implication, the words “comprise” and “contain” and variations of the words, for example “comprising” and “comprises” mean “including but not limited to” and do not exclude other moieties, additives, components, integers or steps. Throughout the description and claims of this specification, the term “and / or” includes any and all combinations of one or more of the associated listed elements. Throughout the description and claims of this specification, the singular encompasses the plural unless the context otherwise requires. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise. All references, including any patent or patent application, cited in this specification are hereby incorporated by reference. No admission is made that any reference constitutes prior art. Further, no admission is made that any of the prior art constitutes part of the common general knowledge in the art. Other features of the present invention will become apparent from the following examples. Generally speaking, the invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including the accompanying claims and drawings). Thus, features, integers, characteristics, compounds or chemical moieties described in conjunction with a particular aspect, embodiment or example of the invention are to be understood to be applicable to any other aspect, embodiment or example described herein, unless incompatible therewith. Moreover, unless stated otherwise, any feature disclosed herein may be replaced by an alternative feature serving the same or a similar purpose. An embodiment of the present invention will now be described by way of example only with reference to the following, wherein: Figure 1. Single Sensor Odour Sampling Test Chamber; Figure 2. Customised Mount for Gas Sensor in Odour Sampling Test Chamber (inverted orientation); Figure 3. Multi-Sensor Odour Sampling Test System; Figure 4 Bespoke airflow system within outer shell of the Multi-Sensor Odour Sampling Test System of Figure 3; Figure 5. Heating cycles utilised during collection of Single Sensor training data sets: HP-301 (A); HP-354 (B); HP-411 (C); and HP-501 (D); Figure 6. Single sensor Odour Sampling Test Chamber Training Workflow; Figure 7. Scores plot for PCA on all 14 sensors; Figure 8. Loading plot for PCA with data from all 14 sensors; Figure 9. PCA Scores plot for a thermal conductivity based sensor (A); an infrared based sensor (B); and a photoionisation based sensor (C); Figure 10. PCA Scores plot for an electrochemical sensor; Figure 11. PCA Scores plot for single metal oxide (SGP41) sensor (A); and a combination of 2 metal oxide (SGP41 and ZMOD4510) sensors; Figure 12. PCA Scores plots for a combination of seven metal oxide sensors: Blatella germanica (A); Tineola bisselliella (B); and Lepisma saccharinum (C); Table 1. Sensors Provided in Multi-Sensor Air Flow System of Figure 4; Table 2. Bed Bug Detection Results of Al derived model challenge using the Single Sensor Odour Sampling Test Chamber of Figure 1. Experimental As a proof of concept, and to demonstrate the effective training of prototype smart pest detection devices, single and multi-sensor systems were prepared, and detection sensitivity was assessed using bed bugs (Cimex lectularius) as a model pest organism. However, it is expected that the said devices can be similarly designed or trained for the detection of other pests, in particular pest insects such as other members of the Cimicidae family and / or members of the Blattidae, Lepismatidae or Tineidae family. Indeed, additional proof of concept experiments were conducted using the multi-sensor system to assess detection sensitivity using cockroaches (Blatella germanica), Silverfish (Lepisma saccharinum) and Clothes Moths (Tineola bisselliella). MATERIALS Sensors Single Sensor System For initial proof of principle experiments, a commercial (Bosch Sensortec BME688 Al Gas Sensor) gas sensor, which allows users to train sensors to detect VOC odour profiles and, using the associated software (BME Al-Studio), build algorithms to automatically determine the presence of specific pests based on detected odour profiles, was incorporated into a still air odour detection apparatus (Figure 1) that was designed to allow full cleaning between uses. Specifically, a customised mounting method was designed and fabricated to repeatedly locate the development board and a single sensor board (which comprises eight copies of the same BME688 sensor to allow simultaneous data collection using different heating profiles) to the test chamber lid (Figure 2). The mounting brackets were manufactured from aluminium, as this material can be odour neutralised with IPA, unlike tape and / or plastic brackets. Further, a range of interchangeable spacer tubes were prepared to allow the vertical offset distance of the sensor boards relative to the lid (and so the depth of penetration into the detection apparatus) to be altered as required. Multi-Sensor System A bespoke air sampling system was designed and developed to assess the sensitivity of combinations of gas sensor types (Figure 3). In this apparatus, air is pumped into a bespoke airflow chamber (Figure 4), linked with 14 different sensors (Table 1). Data was collected as text files which permitted separate analysis of the data for each individual sensor. Test Insects Cimex lectularius, the common bed bug, is a nocturnal blood-feeding parasite of humans and animals. Male and female, Sweden Field Strain, bed bugs (Cimex lectularius) were obtained from CimexStore. They were stored separately sexed in plastic colony pots (60 x 40 mm) in a controlled environment (27°C±2°C, 70% humidity ± 10%, 12h:12hr Light:Dark cycle) inside an incubator until used in testing. Similarly, Blatella germanica (cockroaches), Lepisma saccharinum (Silverfish) and Tineola bisselliella (clothes moths) were obtained from commercial sources and stored, separately sexed, according to the supplier’s recommendations until used in testing. METHODS Single Sensor System The system was trained to detect bedbugs by collecting a series of training data sets or models. A single bed bug, a bed bug exposed paper (to simulate a previous bedbug infestation) and a commercial, VOC-based, lure (Bugscents™, Arctech Innovation) were sampled using the prototype single sensor system for 30 minutes each. Individual data sets were collected using four of the seventeen different sensor pre-set heating cycles, denoted HP-301, HP-354, HP-411 and HP501, respectively. The specific heating cycles used in these training data sets / models are shown in Figure 5(A) -5(D). Following data collection, the BME-AI studio was utilised to automatically conduct all sensor training, including internal training, to produce a classification algorithm specific for each of the four tested heat cycle data sets. Each of the classification algorithms produced by the BME-AI studio could then be downloaded as a BSEC Config file, describing a combination of sensor presets and data processing, for subsequent use in “trained” BME688 sensor-based detection systems. A schematic of the sensor training and testing workflows is provided in Figure 6. Multi-Sensor System Initially, the multi-Sensor system was also trained by collecting the following training data sets: A single bed bug; a bed bug exposed paper (to simulate a previous bed bug infestation); and air. Sampling was conducted using the multi-sensor system for 30 seconds each (10 replicates). Subsequently, the same system was trained by collecting the following training data sets: (i) a single cockroach vs. air; (ii) a single silverfish vs. air; and (iii) a single clothes moth vs. air. Sampling was again conducted using the multi-sensor system for 30 seconds each (16-20 replicates). Statistical Analysis For the single sensor system data, BME Al-Studio software was utilized to develop heat profile specific pest detection / classification algorithms. Data were classified using the proprietary neural net architecture (ADAM optimizer). The samples were exposed to the sensors for a total of 30 minutes, depending on the individual heat profile, allowing between 391 and 783 individual heat cycles to run per data set. These data were then split 30:70, with 70% of the data used to train the algorithm and 30% used to validate the algorithm. By comparing the model predictions of the validation data set with the actual categories, accuracy rates were calculated using the formula: sum of all correct categorisations Accuracy =------—-------------------------------- Sum of all correct and incorrect categorisations In the multi-sensor system, there were 31 data outputs from the 14 test sensors (with variable temperature settings available for each sensor). These data were analysed using a three component Principle Component Analysis on Multisens Analyzer (Standard 2.1.0.54, JLM). Example 1: Single Sensor System Four algorithms, one for each of the tested heat cycles, were assessed mathematically, and detection accuracy varied from 87% to 94%. The data for the most accurate model, i.e. HP-411, is shown in Table 2. Example 2: Multi-Sensor System (Cimex lectularius) Initially, a PCA was run with all available sensors and data outputs. The resultant scores plot showed good although not ideal resolution between the three odour profiles, i.e. single bed bug; a bed bug exposed paper; and air (Figure 7), while the loadings plot indicated that several sensors were of no importance to the accuracy of the model (Figure 8). Subsequent PCA analysis, using subsets of available sensors and data outputs, confirmed that sensitivity was dependent on sensor type. Notably, thermal conductivity, photo-ionization and infrared based sensors did not show any sensitivity to distinguishing live bedbug odours from air or bedbug associated odours (Figure 9(A), 9B, 9(C)). Electrochemical sensors performed better, with the best performing examples of such sensors showing some resolution, but not full separation of the odour signals (Figure 10). However, metal oxide-based sensors were found to show the best lone function, being able to distinguish the bed bug and bed bug associated odours from an air sample (Figure 11(A)). Further, a combination of two such metal oxide sensors provided enhanced discrimination between the bed bug and bed bug associated odours (Figure 11(B)). Example 3: Multi-Sensor System (Blatella germanica, Lepisma saccharinum, Tineola bisselliella) For each pest insect test, a PCA was run with seven available metal oxide sensors and data outputs. The resultant scores plots showed, for each insect, good resolution between the two tested odour profiles (Figure 12), with full separation of the odour signals observed in both the clothes moth (Figure 12(B)) and silverfish (Figure 12(C)) tests. SUMMARY Proof of principle experiments have successfully demonstrated that, when incorporated into a smart pest detection device, VOC gas sensors, in particular metal oxide VOC sensors, are capable of detecting pest insect, in particular bed bug, odours, and distinguishing them from VOC-based chemical lures and / or from odours relating to previous infestations. In contrast, alternative VOC gas sensors based on alternative technology, e.g. thermal conductivity, photo-ionization, infrared or electrochemical based sensors, were shown to provide vastly inferior detection sensitivity. Further, it has been found that system sensitivity can be improved by the inclusion of more than one, e.g. at least two, such metal oxide VOC sensors. In this regard, it is believed that the inclusion of a combination of several (e.g. more than 10, 8 or 4), such sensors may have a deleterious effect on overall sensitivity of the system, not least due to the generation of false positive results due to signal noise. In addition, and whilst the proof of principle work provided herein has been completed primarily using bedbugs as the pest subject it is expected, and has been shown that, following exposure to appropriate pest-specific training data, the smart detection device would be able to likewise detect and distinguish between different target pest organisms. 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Claims

1) A pest detection device, comprising:a. A sensing arrangement configured to detect the presence of one or more volatile organic compounds (VOCs) or other airborne indicators of pest presence, and to generate one or more signals;b. A controller in communication with said sensing arrangement, configured to receive said signals to indicate the presence of a live population of said pests; andc. A power source configured to provide power to said controller and said sensing arrangement,wherein said sensing arrangement comprises, but is not limited to one or more VOC gas sensors.2) The pest detection device according to claim 1, wherein said sensing arrangement further comprises one or more supplementary sensors selected from optical sensors, movement sensors heat sensors and combinations thereof.3) The pest detection device according to claim 1 or claim 2, wherein the sensing arrangement is housed within a chambered structure comprising at least one exterior wall defining an internal chamber having at least one inlet in fluid communication with the exterior surface of the chambered structure.4) The pest detection device according to any of the preceding claims, wherein the sensing arrangement is disposed in an outer housing.5) The pest detection device according to any of the preceding claims, wherein said one or more VOC gas sensors are independently selected from metal oxide (MOX) gas sensors and electrochemical gas sensors.6) The pest detection device according to claim 5, wherein said one or more VOC gas sensors are MOX gas sensors.7) The pest detection device according to any of the preceding claims, wherein said sensing arrangement comprises at least 2, and preferably no more than 10, VOC gas sensors.8) The pest detection device according to any of the preceding claims, wherein said controller is configured to receive said one or more sensor signals and apply a pest specific detection algorithm to determine the current or past presence of a population of said pest.9) The pest detection device according to claim 8, wherein said controller is configured to apply a pest specific detection algorithm to determine the presence of a live or dead population of said pests, or to determine contamination or damage caused by said pests.10)The pest detection device according to claim 9, wherein said sensing arrangement comprises two or more MOX sensors, and said algorithm is a machine learning model or a mathematically derived model to determine and / or distinguish between the presence of a live population of said pests, evidence of contamination or damage caused by said pests, or no evidence of current or past pest presence.11)The pest detection device according to any of the preceding claims, further comprising at least one indicator configured to provide an output signal to a user of the device in response to said controller determining the current or past presence of a live population of said pests.12)The pest detection device according to claim 11, wherein said indicator comprises one or more lights, speakers and / or motors that are configured to produce a visual, audio and / or haptic alert signal.13)The pest detection device according to claim 12, wherein said indicator is positioned remote from the controller and wherein said indicator is optionally configured to connect to said controller via wireless communication such as by Bluetooth, Wi-Fi or radio.14)The pest detection device according to claim 11 or claim 12, wherein said indicator is positioned on or within the device.15)The pest detection device according to any of the preceding claims, wherein said pests are:a. of the Cimicidae family, and are preferably of the order Cimex;b. of the Blattidae family, and are preferably of the order Blatella;c. of the Lepismatidae family, and are preferably of the order Lepisma; ord. of the Tineidae family, and are preferably of the order Tineola16)A method for training a pest detection device to detect the current or past presence of a live population of pests, said method comprising:a. providing a pest detection device according to any of claims 1 to 15;b. obtaining one or more first training data sets by recording said one or more sensor signal outputs when said live pests are not present;c. obtaining one or more second training data sets by recording said one or more sensor signal outputs in the presence of a live population of said pests, or in an environment damaged or contaminated by the past presence of said pests;d. implementing a machine learning model or mathematically derived model using said first and second training data sets obtained in steps b) and c) as inputs to generate a pest specific detection algorithm; ande. configuring said controller to apply, when the device is in use, said pest specific detection algorithm to said one or more sensor signals to determine the presence or absence of a live population of said pests; and / or contamination or damage caused by said pests, and, based on the determined presence of said live pests, or on said contamination or damage caused by said pests, provide an output signal confirming the need for corrective or control measures.17)The method according to claim 16, wherein said first training data sets and said second training data sets are obtained in the presence of a pest attractant chemical lure.18)The method according to claim 16, wherein said first training data sets and said second training data sets are obtained in the absence of a pest attractant chemical lure.19)The method according to any of claims 16 to 18, wherein said pests are of the Cimicidae family, and are preferably of the order Cimex.20)The method according to an of claims 16 to 19, wherein said one or more first training data sets comprises at least one data set collected when no pests of any species are present, and at least one data set collected when a population of pests of a species other than that to be detected are present.21)The method according to any of claims 16 to 20, wherein said second training data sets comprises at least one data set collected in the presence of faeces and / or exuviae indicative of a past infestation of said pest to be detected.22)A pest detection device trained to detect the presence of a population of pests according to the method of any of claims 16 to 21.23)A method for detecting the presence of a live population of pests, said method comprising placing one or more pest detection device(s) according to any of claims 1 to 15 or 22 in an environment to be monitored, optionally wherein said device is coupled to a dedicated communication hub that allows for a remote alert to pest presence to be sent securely to a user.24)The method according to claim 23, wherein said device is placed in an enclosed space such as room or vehicle, and is optionally mounted to a piece of furniture such as a bed.25)The method according to claim 23, wherein said device is placed in an outdoor environment.

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