WO2024236559A1 - Système, procédé et produit-programme d'ordinateur pour une détection de termites améliorée - Google Patents

Système, procédé et produit-programme d'ordinateur pour une détection de termites améliorée Download PDF

Info

Publication number
WO2024236559A1
WO2024236559A1 PCT/IL2024/050439 IL2024050439W WO2024236559A1 WO 2024236559 A1 WO2024236559 A1 WO 2024236559A1 IL 2024050439 W IL2024050439 W IL 2024050439W WO 2024236559 A1 WO2024236559 A1 WO 2024236559A1
Authority
WO
WIPO (PCT)
Prior art keywords
sensor
insect
insects
time
burrowing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IL2024/050439
Other languages
English (en)
Inventor
Yehonatan Ben Hamozeg
Yossef SIMONI
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Agrint Sensing Solutions Ltd
Original Assignee
Agrint Sensing Solutions Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Agrint Sensing Solutions Ltd filed Critical Agrint Sensing Solutions Ltd
Publication of WO2024236559A1 publication Critical patent/WO2024236559A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
    • A01M1/00—Stationary means for catching or killing insects
    • A01M1/02—Stationary means for catching or killing insects with devices or substances, e.g. food, pheronones attracting the insects
    • A01M1/026—Stationary means for catching or killing insects with devices or substances, e.g. food, pheronones attracting the insects combined with devices for monitoring insect presence, e.g. termites
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
    • A01M1/00—Stationary means for catching or killing insects
    • A01M1/02—Stationary means for catching or killing insects with devices or substances, e.g. food, pheronones attracting the insects

Definitions

  • the present invention relates generally to detection of burrowing insects, and more particularly to indoor termite detection.
  • Termites are a type of burrowing insect which inflict structural damage on wood structures, thus termite control and damage is a huge problem; for example repair costs in Indonesia alone are estimated at over IDR 8.7 trillion per year; and termites are said to cause billions of dollars of property damage in the United States annually - the resulting property damage being only rarely covered by homeowners insurance.
  • Termites wreak havoc, including on insulation in walls and in books on shelves. Termite infestation, if untreated, can fell an entire house. Moreover, the chemicals used to control termites are hazardous for persons suffering from asthma or allergies.
  • Visual inspection by a human inspector is known and requires training; other impediments include inaccessibility of buildings.
  • Other methods of termite-detecting are configured to detect temperature, acoustic content, moisture content, or gas.
  • Detecting a possible infestation in a noisy measurement scenario including use of a seismic-waves sensor is described; it is included “that it is possible to establish a higher-order pattern associated to the termite emissions, and resulting from the impulsive response of the sensor and the body or substratum through which the emitted waves propagate” (see e.g.
  • a second device this time a portable microwave sensor, can be passed held against the board or even drywall (be careful not to hold the device, best to use a tripod to minimize false positives due to vibrations) to aid in identifying the exact locations of galleries.
  • This detection strategy works best for local treatment, where the exact knowledge of active boards and gallery architecture is critical to success.” (see e.g. https://www.pestboard.ca.gov/howdoi/research/ucbfinal.pdf)
  • Termites’ acoustic signals have good detection potential with an accuracy of approximately 80%; detectable acoustics are generated by feeding-excavating and by headbanging by workers and soldiers (see e.g. https://www.tandfonline.eom/doi/full/10.1080/26895293.2023.2167866).
  • circuitry typically comprising at least one processor in communication with at least one memory, with instructions stored in such memory executed by the processor to provide functionalities which are described herein in detail. Any functionality described herein may be firmware-implemented or processor- implemented, as appropriate.
  • Certain embodiments herein seek to improve termite detection, thereby to facilitate termite control e.g., via pesticides.
  • Certain embodiments include a termite detection system that monitors for termites 24/7, as opposed to relying on relatively infrequent human inspection.
  • any reference herein to, or recitation of, an operation being performed is, e.g. if the operation is performed at least partly in software, is intended to include both an embodiment where the operation is performed in its entirety by a server A, and also to include any type of “outsourcing” or “cloud” embodiments in which the operation, or portions thereof, is or are performed by a remote processor P (or several such), which may be deployed off-shore or “on a cloud”, and an output of the operation is then communicated to, e.g. over a suitable computer network, and used by, server A.
  • the remote processor P may not, itself, perform all of the operations, and, instead, the remote processor P itself may receive output/s of portion/s of the operation from yet another processor/s P', may be deployed off-shore relative to P, or “on a cloud”, and so forth.
  • Embodiment 1 A system for controlling burrowing insects, the system comprising: a remote server typically configured to communicate with plural sets of seismic sensors respectively deployed at plural premises, each set at a given premises typically including seismic sensor/s deployed at respective location/s within the given premises; wherein each seismic sensor typically enables vibrations of an insect-consumable substance, into which the seismic sensor is driven, to be sensed, thereby to yield sensor output data, and/or a communication unit which communicates, from the sensor to the remote server, all or any subset of: i. the sensor output data, ii. an identifier unique to the individual seismic sensor; and iii.
  • the remote server is typically configured to differentiate, for at least one point in time, an insect-consumable substance in which burrowing insects are present from an insect-consumable substance in which no burrowing insects are present, and/or to store information associating identifiers of certain seismic sensors, with data indicating whether burrowing insects are present in the insectconsumable substance into which each of the certain seismic sensors are respectively driven, at given stored points in time corresponding to the time-stamp/s.
  • the sensor may comprise any suitable vibration detector, such as but not limited to a seismic detector.
  • Embodiment 5 The system according to any of the preceding embodiments wherein the seismic sensors are configured to be driven into a household object which functions as the insectconsumable substance.
  • the household object which is typically wooden, may for example comprise a wall, floor or deck, ceiling, stairs, frame or sill e.g. of a door or window, pillar/post, or banister, article of furniture or storage unit (closet or shelf e.g.), ornament, or recreational object such as a gaming table or musical instrument or toy.
  • Embodiment 6 A method for controlling burrowing insects, the method comprising: providing a remote server configured to communicate with plural sets of seismic sensors respectively deployed at plural premises, each set at a given premises including seismic sensor/s deployed at respective location/s within the given premises, wherein seismic sensors enable vibrations of an insect-consumable substance, into which the seismic sensor is driven, to be sensed, thereby to yield sensor output data, and/or providing a communication unit which communicates, from the sensor to the remote server, all or any subset of: i. the sensor output data, ii. an identifier unique to the individual seismic sensor; and iii.
  • the remote server typically includes at least one hardware processor configured to differentiate, for at least one point in time, an insect-consumable substance in which burrowing insects are present from an insect-consumable substance in which no burrowing insects are present, and to store information associating identifiers of certain seismic sensors, with data indicating whether burrowing insects are present in the insectconsumable substance into which each of the certain seismic sensors are respectively driven, at given stored points in time corresponding to the time-stamp/s.
  • the remote server typically includes at least one hardware processor configured to differentiate, for at least one point in time, an insect-consumable substance in which burrowing insects are present from an insect-consumable substance in which no burrowing insects are present, and to store information associating identifiers of certain seismic sensors, with data indicating whether burrowing insects are present in the insectconsumable substance into which each of the certain seismic sensors are respectively driven, at given stored points in time corresponding to the time-stamp/s.
  • Embodiment 8 The method according to any of the preceding embodiments wherein the outcomes of human inspections include indications of which species of burrowing insect were present and wherein the server is trained to differentiate substances in which burrowing insects of a first species are present from substances in which burrowing insects of the first species are absent using outcomes of human inspections of the plural sets of bait stations, which indicate whether the first species was present or absent, as tags, thereby to yield a first detection algorithm for the first species of burrowing insect.
  • Embodiment 9 The method according to any of the preceding embodiments wherein the server is also trained to differentiate substances in which burrowing insects of a second species are present from substances in which burrowing insects of the second species are absent using outcomes of human inspections of the plural sets of bait stations, which indicate whether the second species was present or absent, as tags, thereby to yield a second detection algorithm for the second species of burrowing insect.
  • Embodiment 10 The method according to any of the preceding embodiments wherein the detection algorithm is applied to sensor data collected by seismic sensors deployed indoors thereby to provide improved indoor detection by leveraging human inspections of bait stations deployed outdoors.
  • Embodiment 11 The method according to any of the preceding embodiments wherein the first and second detection algorithms are each applied to sensor data collected by seismic sensors deployed indoors to provide improved indoor detection of insects belonging to the first and second species by using separately optimized algorithms for various species, rather than a single general insect presence detection algorithm, by leveraging species-specific human inspections of bait stations deployed outdoors.
  • Embodiment 12 The system according to any of the preceding embodiments and wherein the communication unit communicates, from the sensor to the remote server, all of: i. the sensor output data, ii. the identifier unique to the individual seismic sensor; and iii. the time-stamp identifying the point in time at which the sensor output data was sensed.
  • Embodiment 13 The system according to any of the preceding embodiments wherein the insectconsumable substance comprises a termite-consumable substance such as wood, and wherein the remote server is configured to differentiate, for at least one point in time, a termite-consumable substance in which termites are present from a termite-consumable substance in which no termites are present, and to store information associating identifiers of certain seismic sensors, with data indicating whether termites are present in the termite-consumable substance into which each of the certain seismic sensors’ respective seismic sensors are respectively driven, at given stored points in time corresponding to the time-stamp/s.
  • the remote server is configured to differentiate, for at least one point in time, a termite-consumable substance in which termites are present from a termite-consumable substance in which no termites are present, and to store information associating identifiers of certain seismic sensors, with data indicating whether termites are present in the termite-consumable substance into which each of
  • Embodiment 14 A computer program product, comprising a non-transitory tangible computer readable medium having computer readable program code embodied therein, the computer readable program code adapted to be executed to implement a method for controlling burrowing insects, the method comprising: providing a remote server configured to communicate with plural sets of seismic sensors respectively deployed at plural premises, each set at a given premises including seismic sensor/s deployed at respective location/s within the given premises, wherein seismic sensors enable vibrations of an insect-consumable substance, into which the seismic sensor is driven, to be sensed, thereby to yield sensor output data, and providing a communication unit which communicates, from the sensor to the remote server, all or any subset of: i. the sensor output data, ii.
  • the remote server includes at least one hardware processor configured to differentiate, for at least one point in time, an insect-consumable substance in which burrowing insects are present from an insectconsumable substance in which no burrowing insects are present, and to store information associating identifiers of certain seismic sensors, with data indicating whether burrowing insects are present in the insect-consumable substance into which each of the certain seismic sensors are respectively driven, at given stored points in time corresponding to the time-stamp/s.
  • a computer program comprising computer program code means for performing any of the methods shown and described herein when the program is run on at least one computer; and a computer program product, comprising a typically non- transitory computer-usable or -readable medium e.g. non-transitory computer -usable or -readable storage medium, typically tangible, having a computer readable program code embodied therein, the computer readable program code adapted to be executed to implement any or all of the methods shown and described herein.
  • the operations in accordance with the teachings herein may be performed by at least one computer specially constructed for the desired purposes, or a general purpose computer specially configured for the desired purpose by at least one computer program stored in a typically non-transitory computer readable storage medium.
  • the term "non-transitory” is used herein to exclude transitory, propagating signals or waves, but to otherwise include any volatile or non-volatile computer memory technology suitable to the application.
  • processor/s, display and input means may be used to process, display e.g. on a computer screen or other computer output device, store, and accept information such as information used by or generated by any of the methods and apparatus shown and described herein; the above processor/s, display and input means including computer programs, in accordance with all or any subset of the embodiments of the present invention.
  • any or all functionalities of the invention shown and described herein, such as but not limited to operations within flowcharts, may be performed by any one or more of: at least one conventional personal computer processor, workstation, or other programmable device or computer or electronic computing device or processor, either general-purpose or specifically constructed, used for processing; a computer display screen and/or printer and/or speaker for displaying; machine- readable memory such as flash drives, optical disks, CDROMs, DVDs, BluRays, magnetic-optical discs or other discs; RAMs, ROMs, EPROMs, EEPROMs, magnetic or optical or other cards, for storing, and keyboard or mouse for accepting.
  • at least one conventional personal computer processor, workstation, or other programmable device or computer or electronic computing device or processor either general-purpose or specifically constructed, used for processing
  • a computer display screen and/or printer and/or speaker for displaying
  • machine- readable memory such as flash drives, optical disks, CDROMs, DVDs, BluRays, magnetic-optical discs or other disc
  • Modules illustrated and described herein may include any one or combination or plurality of: a server, a data processor, a memory/computer storage, a communication interface (wireless (e.g., BLE) or wired (e.g., USB)), a computer program stored in memory/computer storage.
  • a server e.g., a data processor
  • a memory/computer storage e.g., a hard disk drive
  • a communication interface e.g., BLE
  • wired e.g., USB
  • processor is intended to include any type of computation or manipulation or transformation of data represented as physical, e.g., electronic, phenomena which may occur or reside e.g., within registers and /or memories of at least one computer or processor.
  • processor is intended to include a plurality of processing units which may be distributed or remote
  • server is intended to include plural typically interconnected modules running on plural respective servers, and so forth.
  • the above devices may communicate via any conventional wired or wireless digital communication means, e.g., via a wired or cellular telephone network, or a computer network such as the Internet.
  • the apparatus of the present invention may include, according to certain embodiments of the invention, machine readable memory containing or otherwise storing a program of instructions which, when executed by the machine, implements all or any subset of the apparatus, methods, features, and functionalities of the invention shown and described herein.
  • the apparatus of the present invention may include, according to certain embodiments of the invention, a program as above, which may be written in any conventional programming language, and optionally a machine for executing the program, such as but not limited to a general- purpose computer, which may optionally be configured or activated in accordance with the teachings of the present invention. Any of the teachings incorporated herein may, wherever suitable, operate on signals representative of physical objects or substances.
  • the term “computer” should be broadly construed to cover any kind of electronic device with data processing capabilities, including, by way of non-limiting example, personal computers, servers, embedded cores, computing systems, communication devices, processors (e.g. digital signal processor (DSP), microcontrollers, field programmable gate array (FPGA), application specific integrated circuit (ASIC), etc.) and other electronic computing devices.
  • DSP digital signal processor
  • FPGA field programmable gate array
  • ASIC application specific integrated circuit
  • Any reference to a computer, controller, or processor is intended to include one or more hardware devices e.g., chips, which may be co-located, or remote from one another.
  • Any controller or processor may for example comprise at least one CPU, DSP, FPGA, or ASIC, suitably configured in accordance with the logic and functionalities described herein.
  • processor/s or controller/s configured as per the described feature or logic or functionality, even if the processor/s or controller/s are not specifically illustrated for simplicity.
  • the controller or processor may be implemented in hardware, e.g., using one or more Application-Specific Integrated Circuits (ASICs) or Field-Programmable Gate Arrays (FPGAs), or may comprise a microprocessor that runs suitable software, or a combination of hardware and software elements.
  • ASICs Application-Specific Integrated Circuits
  • FPGAs Field-Programmable Gate Arrays
  • an element or feature may exist is intended to include (a) embodiments in which the element or feature exists; (b) embodiments in which the element or feature does not exist; and (c) embodiments in which the element or feature exist selectably, e.g., a user may configure or select whether the element or feature does or does not exist.
  • Any suitable input device such as but not limited to a sensor, may be used to generate or otherwise provide information received by the apparatus and methods shown and described herein.
  • Any suitable output device or display may be used to display or output information generated by the apparatus and methods shown and described herein.
  • Any suitable processor/s may be employed to compute or generate or route, or otherwise manipulate or process information as described herein and/or to perform functionalities described herein and/or to implement any engine, interface, or other system illustrated or described herein.
  • Any suitable computerized data storage e.g., computer memory, may be used to store information received by or generated by the systems shown and described herein.
  • Functionalities shown and described herein may be divided between a server computer and a plurality of client computers. These or any other computerized components shown and described herein may communicate between themselves via a suitable computer network.
  • UI user interface
  • the term user interface, or “UI” as used herein includes also the underlying logic which controls the data presented to the user e.g., by the system display, and receives and processes and/or provides to other modules herein, data entered by a user e.g. using her or his workstation/device.
  • Figs. 1 - 3, and 8 are pictorial illustrations of systems for detection of burrowing insects according to embodiments of the invention.
  • Figs. 4 and 5 are schematic and cross-sectional views of sensors in accordance with embodiments of the invention.
  • arrows between modules may be implemented as APIs and any suitable technology may be used for interconnecting functional components or modules illustrated herein in a suitable sequence or order e.g., via a suitable API/Interface.
  • state of the art tools may be employed, such as but not limited to Apache Thrift and Avro which provide remote call support.
  • a standard communication protocol may be employed, such as but not limited to HTTP or MQTT, and may be combined with a standard data format, such as but not limited to JSON or XML.
  • one of the modules may share a secure API with another. Communication between modules may comply with any customized protocol or customized query language, or may comply with any conventional query language or protocol.
  • Methods and systems included in the scope of the present invention may include any subset or all of the functional blocks shown in the specifically illustrated implementations by way of example, in any suitable order e.g., as shown.
  • Flows may include all or any subset of the illustrated operations, suitably ordered e.g., as shown.
  • Tables herein may include all or any subset of the fields and/or records and/or cells and/or rows and/or columns described.
  • Computational, functional, or logical components described and illustrated herein can be implemented in various forms, for example, as hardware circuits, such as but not limited to custom VLSI circuits or gate arrays or programmable hardware devices, such as but not limited to FPGAs, or as software program code stored on at least one tangible or intangible computer readable medium and executable by at least one processor, or any suitable combination thereof.
  • a specific functional component may be formed by one particular sequence of software code, or by a plurality of such, which collectively act or behave or act as described herein with reference to the functional component in question.
  • the component may be distributed over several code sequences such as but not limited to objects, procedures, functions, routines, and programs and may originate from several computer files which typically operate synergistically.
  • Each functionality or method herein may be implemented in software (e.g., for execution on suitable processing hardware such as a microprocessor or digital signal processor), firmware, hardware (using any conventional hardware technology such as Integrated Circuit technology), or any combination thereof.
  • modules or functionality described herein may comprise a suitably configured hardware component or circuitry.
  • modules or functionality described herein may be performed by a general purpose computer or more generally by a suitable microprocessor, configured in accordance with methods shown and described herein, or any suitable subset, in any suitable order, of the operations included in such methods, or in accordance with methods known in the art.
  • Any logical functionality described herein may be implemented as a real time application, if and as appropriate, and which may employ any suitable architectural option, such as but not limited to FPGA, ASIC or DSP or any suitable combination thereof.
  • Any hardware component mentioned herein may in fact include either one or more hardware devices e.g., chips, which may be co-located or remote from one another.
  • Any method described herein is intended to include within the scope of the embodiments of the present invention also any software or computer program performing all or any subset of the method’s operations, including a mobile application, platform or operating system e.g., as stored in a medium, as well as combining the computer program with a hardware device to perform all or any subset of the operations of the method.
  • Data can be stored on one or more tangible or intangible computer readable media stored at one or more different locations, different network nodes or different storage devices at a single node or location.
  • Suitable computer data storage or information retention apparatus may include apparatus which is primary, secondary, tertiary, or off-line, which is of any type or level or amount or category of volatility, differentiation, mutability, accessibility, addressability, capacity, performance, and energy use, and which is based on any suitable technologies such as semiconductor, magnetic, optical, paper, and others.
  • Termite bait stations which use much smaller amounts of poison, are typically buried in the ground, a few meters apart, around a property’s perimeter, with the very top of the trap sitting flush with the ground surface, so as not to cause any unsightly or inconvenient obstructions within the yard or property, and also since termites travel underground.
  • Termite baits may include a delayed action insecticide, as opposed to immediate action poison which causes sick or dead termites to accumulate near stations, which may cause other termites to avoid the trap. This eliminates entire colonies, which may not be necessary, since structural protection may be achievable without colony elimination.
  • the bait station housing may be a hollow receptacle e.g., plastic cylinder with apertures e.g., slits along the sides, via which termites may enter.
  • termites do not always discover the baiting stations such that termites may happen upon, and set upon, the structure to be protected before they happen upon the baiting stations, causing damage, often costly and difficult to fix, which is detected only once some of the termites happen upon the station (and even then, an additional interval of time elapses until periodic inspection occurs).
  • bait stations require expert discretion e.g., if termites fail to discover (or consume) the bait, additional stations may need to be added and/or many gallons of liquid barrier termiticide, which is objectionable from a health and environmental standpoint, may be required to be injected into the ground around the structure and also underneath. Moreover, ongoing surveillance of the stations is often not enough; monitoring of the building itself is also required.
  • termites cannot see or smell baits deployed the soil; they instead simply wander into them (or not). Whether the termites eventually find or discover the bait stations is currently a matter of luck. Also, probability of getting termites to find the bait is low when ground is frozen, snow-covered, or saturated, which impedes feeding hence bait discovery, whereas during this same period, feeding within the warmer structure may continue apace. During periods of drought, when termites forage more deeply for moisture, bait discovery may also be low, whereas feeding on the indoor walls may continue apace. Finally, since, at best, bait discovery takes time, the bait station approach requires homeowners to accept the possibility of a lengthy process.
  • the system leverages the existing deployment of human-inspected termite baiting stations, worldwide, to jump-start an effective Al-based termite control system, typically including using human inspection, to tag certain time -points in a stream of acoustic data collected from an IOT which unites the existing worldwide population of human- inspected stations, thereby to provide training data for the Al-based termite control system’s server.
  • the algorithm may utilize any suitable known knowledge about termite detection e.g., as described herein, and/or in: https://www.eurasip.org/Proceedings/Eusipco/Eusipco2008/papers/1569095383.pdf https://link.springer.com/ article/ 10.1007/s00107 -023 -01948-1 https://www.sciencedirect.com/science/article/abs/pii/S02632241060Q1977 https://www.pestboard.ca.gov/howdoi/research/ucbfinal.pdf https://www.sciencedirect.com/science/article/abs/pii/S02632241060Q1977
  • the system also typically trains cost effectively, since the reality is that large populations of baiting systems are deployed, to detect infestation and/or re -infestation early and/or to treat same, and, currently, are manned by humans.
  • the server typically tags the acoustic output data using yes/no infestation decisions made by human inspectors which are retained to inspect the baiting stations in any event, thereby to cost effectively provide training data. Once the system is trained, human inspection frequency may be reduced or even eliminated.
  • Certain embodiments include a termite detector which detects termites in baiting stations; the detector may include a structural element which is driven into bait and which conducts seismic waves; the waves are detected and communicated to a processor or server which may be remote.
  • detection algorithm is verified vis a vis human inspection which occurs anyway, and/or is trained, e.g., based on machine learning which can use the human inspection reports as training data, termite detection becomes far more effective, both functionally and in terms of cost.
  • termite infestation is detected essentially immediately or within seconds, minutes, hours, or, at most, within days, and not weeks or months after it occurs when an inspector finally arrives at the premises.
  • detectors may be deployed in the structure to be protected, rather than in the structure’s vicinity, given that detection is essentially immediate. This also improves detection e.g. if detectors are deployed in the vulnerable (e.g.
  • bait stations do not form a continuous ring around the residence to be protected, and are instead deployed at intervals from one another.
  • Yet another advantage is in streamlining the real estate business; today, transactions are held up by termite inspections which, once the system herein is verified and/or trained, become superfluous, in buildings which are protected by the system herein.
  • training data provided by human inspectors can include an indication of the termite species to enable separate algorithms to be operated for each of plural types of termites, thereby to enhance detection rates by precision-configuring a detection algorithm to each species, then running algorithms configured for all relevant species, on data collected by each individual sensor at each individual location in each individual residence to be protected.
  • Figs. 1 - 3 depict example systems, not necessarily to scale, for detection of burrowing insects in timbers, bait stations (of which, for simplicity, only one is shown), and homes, in accordance with embodiments of the invention.
  • Fig. 8 depicts systems, not necessarily to scale, for detection of burrowing insects in trees.
  • each sensor includes at least a MEMS sensing unit (e.g., accelerometer or microphone), antenna, antenna communication, and signal processing (e.g., noise filtering) e.g. on a suitable PCB (printed circuit board).
  • MEMS sensing unit e.g., accelerometer or microphone
  • antenna communication e.g., antenna communication
  • signal processing e.g., noise filtering
  • PCB printed circuit board
  • the conduction component typically formed of metal, is typically configured to be driven or screwed into a substrate in which insect-generated vibrations are to be sensed e.g., wood.
  • the sensor typically connects to the (typically wooden) object in which insect vibrations are being measured or sensed via a screw or stud which firmly engages the sensor with the wood.
  • sensing surface may be employed herein such as but not limited to a metal surface connected to the sensor (or sensing unit) and the conduction component, and serves to increase the contact area with the object (wall, furniture, etc.) being sampled, so that the vibration signal will be received, by the sensor, not only through the conduction component, but also through the sensing surface.
  • the sensing surface is a mechanical, and not an electronical component.
  • the Comm unit aka communication unit, may include circuitry which provides any suitable typically wireless communication such as BLE communication, for transmitting information accumulated and/or processed and stored in the sensor to the gateway.
  • the energy unit which distributes energy/power to the components of the sensor typically comprises a battery e.g., with associated cables, resistors, and voltage stabilizers.
  • the sensing unit includes all electrical components responsible for converting vibration into an electrical signal, typically including an accelerometer and associated amplifiers.
  • Data storage may store output (typically signal-processed) of sensing unit/s for a given window of time. This output may be batched; each batch may be sent up to the server and memory is then cleared for the next batch of sensing unit outputs.
  • the sensor embodiments of Figs. 4 and 5 may be used in-house, or, if desired, for trees e.g., as shown in Fig. 8 and/or for bait stations and/or for timbers, e.g., as shown in Fig. 1. Alternatively, any known sensor e.g., as described in the co-owned patent document mentioned herein, may be employed e.g., for trees.
  • Termite detection processing may occur on a central server which may be deployed e.g. on a cloud and may receive data from sites e.g. homes, directly from individual sensors or via gateways (e.g. controllers) which may be deployed on-site and which may communicate with all sensors deployed at a given site, on the one hand, and with the central server, on the other hand.
  • Each gateway may be deployed at a suitable (typically single) location per residence or protected area.
  • the gateway if battery operated, may be deployed anywhere in the grounds (e.g., garden) or even house/building if the garden is close to the house, to support communication between the stations and the gateway.
  • Communication may be wireless e.g., communication between sensors and the gateway local to them may be wireless, such as but not limited to Bluetooth Eow Energy aka BEE communication.
  • a human interface or web platform or application e.g., cell app may be provided to present termite detection results to end-users and/or to system administrators; communication with the server (e.g. on the cloud) may for example be cellular or WiFi. Communication between the cloud server and gateway may also be cellular or WiFi, and includes all or any subset of: a threaded front prong which may be driven e.g. screwed into wood to be monitored for presence of termites, a base which may be aluminum cast, an electronic board e.g., PCB, which may be screwed to the base, a sealing rubber O-ring intermediate the base and the PCB, one or more batteries (or other power source e.g.
  • the solar panel or mains interface may include a cover for the batteries which may be screwed to the base, and an O-ring under the cover (typically Nitrile rubber (NBR) or any other sealing elastomer.
  • NBR Nitrile rubber
  • the cross section need not be circular as shown, and may instead be, for example an oblong e.g., square.
  • the PCB may include a WiFi interface to the remote server.
  • Front end processing provided by the sensor may include all or any subset of the functionalities shown or described in co-owned patent document https://patents.google.com/patent/USl 1324210B2 e.g., sampling, pulse detection, dynamic termite detection threshold choice typically ML-powered, activity marking, and auxiliary sensors, such as but not limited to any of a temperature sensor, humidity sensor, accelerometer, which may be tri-axis, oxygen sensor, methane sensor, or microphone. Communication between a server application on the cloud, and the sensors in the field may be wireless, wired, or optical fiber.
  • the system shown herein will have an associated application e.g., cell app and WiFi to report from home to the cloud, and perform processing and analysis of the information remotely and centrally, and report to end-users from the cloud.
  • an associated application e.g., cell app and WiFi to report from home to the cloud, and perform processing and analysis of the information remotely and centrally, and report to end-users from the cloud.
  • the length of the screw may for example be between 3-20 cm; an inventory may include plural e.g., 4 screw lengths, such 2 cm, 7 cm, 10 cm, and 20 cm lengths.
  • An end-user can then use, for detection of termites in walls and furniture , a screw of length 2 cm, for detection of termites in basement wood, 8 cm, for detection of termites in bait stations 10 cm (since the depth of commercial bait stations is about 15 cm) and for detection of termites in trees either a 10 cm screw or a 20 cm screw, depending on the thickness or cross-sectional size of the trunk.
  • the term “basement wood” is intended to include beams which support the house and are embedded in the ground on which the house is built.
  • the length of the screw depends on the type and the thickness of the detection object; typically, longer screws are used for thicker objects, whereas shorter screws are used for thinner objects, e.g., the length of the screw is typically at least 30% of the thickness of the monitored surface.
  • sensors in the building (say) to be protected may be deployed.
  • sensors may be deployed along the roof and along the walls of the basement , bathrooms and kitchen, all say every 3 meters.
  • the wood substrate where the termites are found has acoustic features which differ from a wood substrate in which no termites are found. This is one possible basis for development of a sensor for early detection of termites e.g., in trees and bait stations.
  • Developing a sensor for termite detection in trees and bait stations while ignoring environmental noises may be similar to the process for early detection of a Red Palm Weevil e.g. as described in the following co-owned patent document: https://patents.google.com/patent/USl 1324210B2 typically with focus on behaviour and characteristics of termites, specifically, either in general, or separately, per each species of termites.
  • Developing a sensor for termite detection e.g., in trees and/or bait stations while mitigating environmental noises may include all or any subset of the following:
  • Sensor Design Conduct research on termite behaviour, characteristics, and their typical habitats in trees and bait stations. Design a (typically seismic) sensor sensitive sufficient for detecting termite activity, while minimizing impact of or filtering out environmental noises.
  • Data collection Build a prototype that records the seismic signals typically while using noise filtering techniques e.g., Kalman noise filtering, seismic noise filtering, or neural-network based noise reduction (e.g. based on a feedforward denoising neural network (e.g. DnCNN)).
  • noise filtering techniques e.g., Kalman noise filtering, seismic noise filtering, or neural-network based noise reduction (e.g. based on a feedforward denoising neural network (e.g. DnCNN)).
  • DnCNN feedforward denoising neural network
  • Data Analysis and Algorithm Development Analyze the data collected and recorded to develop algorithms and models for termite detection while mitigating environmental noises. This involves signal processing techniques (e.g., for noise filtering) e.g. on a suitable PCB and machine learning algorithms (such as, by way of non-limiting example, Hidden Markov models or recurrent neural networks) to separate termite signals from environmental noises and other false positives.
  • signal processing techniques e.g., for noise filtering
  • machine learning algorithms such as, by way of non-limiting example, Hidden Markov models or recurrent neural networks
  • Testing and Validation Validate the prototype sensor in real-world field conditions, specifically focusing on its ability to ignore environmental noises and accurately detect termite activity in trees and bait stations. Evaluate its performance in different environmental conditions that may introduce noises, such as wind, rain, or other ambient sounds. Adjust noise filtering parameters or sensor settings responsively.
  • Cloud decision-making algorithm Based on the data analysis and algorithm development in the sensor, a cloud decision-making algorithm may be provided that optimizes and refines the data from the sensor, to improve the accuracy and reliability in detecting termites in trees and bait stations and different environments, while ignoring environmental noises. This may involve noise filtering techniques and Al algorithm parameters to enhance its performance in real-world conditions.
  • the Artificial Intelligence algorithm will continue to improve the performance of the system as more information from the sensors is added to improve the accuracy and speed of early detection.
  • Field Testing and fine-tuning the algorithms Test the optimized sensor in real-world field conditions, including trees and/or bait stations where termites are known to be active, to validate its performance in detecting termites, while effectively mitigating environmental noises. Gather data and feedback from field testing to further refine the sensor and algorithms, as needed.
  • the algorithm is configured or trained to detect termites biting a (typically wooden) substrate or bait and not, or not only, head-banging of soldier termites who may call out when a nest e.g., in a tree, is in danger.
  • seismic data may be tagged or labelled, during R & D, e.g. by classifying the termite activity (e.g. as burrowing, biting, or head-banging) and/or termite characteristics (e.g. termite species, number of termites, termite role (e.g. soldier/worker/king/queen), relying on human experts examining imagery of the termites captured (in field and/or lab conditions) while the seismic data was collected and/or relying on image processing of this imagery.
  • termite activity e.g. as burrowing, biting, or head-banging
  • termite characteristics e.g. termite species, number of termites, termite role (e.g. soldier/worker/king/
  • sensor readings from N1 trees infected with termites of type (e.g., species) 1, and from N2 trees infected with termites of type (e.g. , species) 2, and from N3 trees infected with termites of type 3, and from M trees which are NOT infected are collected.
  • Y may be a binary variable (infestation yes/no) or may be a probability of infestation, or may be a multi-level variable (e.g., infestation very likely, somewhat likely, unlikely, almost definitely clean).
  • X variables may be identified which characterize the sensor readings e.g., various features of the sensor readings (e.g., presence of peaks including at least one of peak height, width, prevalence) which may be characteristic of infested wood and are rare in non-infested wood.
  • Regression analysis or ANOVA or multivariate analysis
  • Clustering may be employed to computationally differentiate n species of termites, and only then each species may be separately determined to be present/absent.
  • Termite detection may also be based on known principles such as sound amplification or microwave detection as by TERM_A_TRACTM.
  • SVc support vector classification
  • ANN artificial neural network
  • Machine learning may be used to leverage knowledge which naturally accumulates about termite detection outcomes, as sites (e.g., bait stations deployed at the sites) are manually inspected by humans worldwide.
  • human involvement characteristics are defined and systematically tested to enable the system to use its learning ability in order to determine optimal human involvement (e.g., desired frequency of human inspection, or desired rapidity of dispatchment of exterminators once termites have been discovered).
  • the system records the human involvement characteristics for each residence and also records variable/s indicative of successful treatment e.g., % infested buildings per year, and/or variable/s indicative of how early infestations were detected.
  • the termite detection algorithm may be developed for and/or trained on each type of pest separately. Sensors may record the raw data of a pest of a given type e.g., species, and the data is then analyzed (e.g., by noise filtering) to find the signal and develop a detection algorithm accordingly, which is typically burned into the sensor. Subsequently, these sensors may begin to sample infected and clean trees and, based on this, Al in the cloud may direct decision-making regarding the condition of the tree.
  • a detection algorithm may be developed for and/or trained on each type of pest separately. Sensors may record the raw data of a pest of a given type e.g., species, and the data is then analyzed (e.g., by noise filtering) to find the signal and develop a detection algorithm accordingly, which is typically burned into the sensor. Subsequently, these sensors may begin to sample infected and clean trees and, based on this, Al in the cloud may direct decision-making regarding the condition of the tree.
  • a particular advantage of certain embodiments is that most of the billions spent on termite (e.g.) control are spent on human technicians visiting premises every (say) 3 months, where most visits are, in retrospect, superfluous, since no termites are found, thus no pest control is required.
  • the human inspection occurs only in that fraction of cases in which termites are automatically found by a system active continuously (24/7).
  • the sensors may detect termites living inside furniture or firewood, which are both sources of infestation.
  • the system herein may include a compact sensor to stick into furniture or firewood, enabling furniture or firewood to be sold as “termite-safe”.
  • deployment of bait stations is reduced or eliminated e.g., once the system is demonstrated to be well trained, in comparison to human evaluations of existing deployments of bait stations.
  • a target to be protected such as a structure e.g., home, or household objects, or industrial or agricultural wood
  • bait stations in the ground are ecologically damaging, since burrowing insects which have not yet attacked any target are also destroyed, even though they are biologically beneficial to the ecosphere. Bait stations in the ground are also costly to deploy, inspect, and maintain.
  • references to termites herein may more generally relate to burrowing insects.
  • References herein to protection of “buildings” from termites are intended to refer as well to any other target substrates or substrates to be protected e.g. wood repositories, and repositories of fabrics e.g. (e.g. for the textile industry).
  • “Gardens” are intended to refer to any type of grounds in which a substrate to be protected may be located. Vibrations may optionally be sensed in a substrate e.g., in the ground or in wood, by using an optical fiber for conveying the vibrations.
  • the sensor need not necessarily detect vibration; instead, visual detection, say, may be employed.
  • leaf caterpillars are not borers, and may be detected visually, which may include capturing video or still sensor output and using image processing, perhaps remotely, to detect certain insects.
  • sensors are deployed in bait stations (or furniture, pre-fabricated walls, etc.) a priori e.g. the conductive components are screwed into the wood bait in the factory.
  • sensors herein may be deployed in the field, in already- deployed bait stations, or in homes, trees, timber etc. e.g., by screwing or otherwise driving the sensor’s conductive component into bait, furniture, walls, trees, etc., in the field.
  • poison may be deployed initially e.g., in the factory or when deploying bait stations, or only upon discovery of infestation.
  • a human inspector deploys poison into a bait station
  • s/he may also deploy the system of the present invention e.g., by putting into the bait station a new piece of wood bait into which a seismic sensor, e.g., as described herein, may be driven.
  • bait stations are deployed initially without poison, and only upon discovery of infestation, is poison added to the stations.
  • electromagnetic signals in accordance with the description herein. These may carry computer-readable instructions for performing any or all of the operations of any of the methods shown and described herein, in any suitable order, including simultaneous performance of suitable groups of operations, as appropriate. Included in the scope of the present disclosure, inter alia, are machine -readable instructions for performing any or all of the operations of any of the methods shown and described herein, in any suitable order; program storage devices readable by machine, tangibly embodying a program of instructions executable by the machine to perform any or all of the operations of any of the methods shown and described herein, in any suitable order i.e.
  • a computer program product comprising a computer useable medium having computer readable program code, such as executable code, having embodied therein, and/or including computer readable program code for performing, any or all of the operations of any of the methods shown and described herein, in any suitable order; any technical effects brought about by any or all of the operations of any of the methods shown and described herein, when performed in any suitable order; any suitable apparatus or device or combination of such, programmed to perform, alone or in combination, any or all of the operations of any of the methods shown and described herein, in any suitable order; electronic devices each including at least one processor and/or cooperating input device and/or output device and operative to perform, e.g., in software, any operations shown and described herein; information storage devices or physical records, such as disks or hard drives, causing at least one computer or other device to be configured so as to carry out any or all of the operations of any of the methods shown and described herein, in
  • Any computations or other forms of analysis described herein may be performed by a suitable computerized method. Any operation or functionality described herein may be wholly or partially computer-implemented e.g., by one or more processors.
  • the invention shown and described herein may include (a) using a computerized method to identify a solution to any of the problems or for any of the objectives described herein, the solution optionally including at least one of a decision, an action, a product, a service, or any other information described herein, that impacts, in a positive manner, a problem or objectives described herein; and (b) outputting the solution.
  • the system may, if desired, be implemented as a network, e.g., web-based system, employing software, computers, routers, and telecommunications equipment, as appropriate.
  • a network e.g., web-based system, employing software, computers, routers, and telecommunications equipment, as appropriate.
  • a server may store certain applications, for download to clients, which are executed at the client side, the server side serving only as a storehouse.
  • Any or all functionalities e.g., software functionalities shown and described herein, may be deployed in a cloud environment.
  • Clients e.g., mobile communication devices such as smartphones, may be operatively associated with, but external to the cloud.
  • the scope of the present invention is not limited to structures and functions specifically described herein, and is also intended to include devices which have the capacity to yield a structure, or perform a function, described herein, such that even though users of the device may not use the capacity, they are, if they so desire, able to modify the device to obtain the structure or function.
  • any “if -then” logic described herein is intended to include embodiments in which a processor is programmed to repeatedly determine whether condition x, which is sometimes true and sometimes false, is currently true or false, and to perform y each time x is determined to be true, thereby to yield a processor which performs y at least once, typically on an “if and only if’ basis e.g., triggered only by determinations that x is true, and never by determinations that x is false.
  • Any determination of a state or condition described herein, and/or other data generated herein, may be harnessed for any suitable technical effect.
  • the determination may be transmitted or fed to any suitable hardware, firmware or software module, which is known or which is described herein, to have capabilities to perform a technical operation responsive to the state or condition.
  • the technical operation may, for example, comprise changing the state or condition, or may more generally cause any outcome which is technically advantageous given the state or condition or data, and/or may prevent at least one outcome which is disadvantageous given the state or condition or data.
  • an alert may be provided to an appropriate human operator or to an appropriate external system.
  • a system embodiment is intended to include a corresponding process embodiment, and vice versa.
  • each system embodiment is intended to include a server-centered “view” or client centered “view”, or “view” from any other node of the system, of the entire functionality of the system, computer-readable medium, apparatus, including only those functionalities performed at that server or client or node.
  • Features may also be combined with features known in the art, and particularly although not limited to, those described in the Background section or in publications mentioned therein.
  • features of the invention including operations, which are described for brevity in the context of a single embodiment or in a certain order, may be provided separately, or in any suitable sub-combination, including with features known in the art (particularly although not limited to those described in the Background section or in publications mentioned therein) or in a different order, "e.g.” is used herein in the sense of a specific example which is not intended to be limiting.
  • Each method may comprise all or any subset of the operations illustrated or described, suitably ordered e.g., as illustrated or described herein.
  • Devices, apparatus or systems shown coupled in any of the drawings may, in fact, be integrated into a single platform in certain embodiments, or may be coupled via any appropriate wired or wireless coupling, such as but not limited to optical fiber, Ethernet, Wireless LAN, HomePNA, power line communication, cell phone, Smart Phone (e.g. iPhone), Tablet, Laptop, PDA, Blackberry GPRS, Satellite including GPS, or other mobile delivery.
  • any appropriate wired or wireless coupling such as but not limited to optical fiber, Ethernet, Wireless LAN, HomePNA, power line communication, cell phone, Smart Phone (e.g. iPhone), Tablet, Laptop, PDA, Blackberry GPRS, Satellite including GPS, or other mobile delivery.
  • functionalities described or illustrated as systems and sub-units thereof can also be provided as methods and operations therewithin
  • functionalities described or illustrated as methods and operations therewithin can also be provided as systems and sub-units thereof.
  • the scale used to illustrate various elements in the drawings is merely exemplary and/or appropriate for clarity of presentation, and is not intended to be
  • Any processing functionality illustrated (or described herein) may be executed by any device having a processor, such as but not limited to a mobile telephone, set-top-box, TV, remote desktop computer, game console, tablet, mobile e.g. laptop or other computer terminal, embedded remote unit, which may either be networked itself (may itself be a node in a conventional communication network e.g.) or may be conventionally tethered to a networked device (to a device which is a node in a conventional communication network, or is tethered directly or indirectly/ultimately to such a node).
  • a processor such as but not limited to a mobile telephone, set-top-box, TV, remote desktop computer, game console, tablet, mobile e.g. laptop or other computer terminal, embedded remote unit, which may either be networked itself (may itself be a node in a conventional communication network e.g.) or may be conventionally tethered to a networked device (to a device which is a no
  • processor or controller or module or logic are intended to include hardware such as computer microprocessors or hardware processors, which, typically, have digital memory and processing capacity, such as those available from, say Intel and Advanced Micro Devices (AMD). Any operation or functionality or computation or logic described herein may be implemented entirely or in any part on any suitable circuitry, including any such computer microprocessor/s, as well as in firmware or in hardware, or any combination thereof.
  • any modules, blocks, operations or functionalities described herein which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination, including with features known in the art.
  • Each element e.g., operation described herein may have all characteristics and attributes described or illustrated herein, or, according to other embodiments, may have any subset of the characteristics or attributes described herein.
  • apps or applications referred to herein may include a cell app, mobile app, computer app, or any other application software. Any application may be bundled with a computer and its system software or published separately.
  • phone and similar used herein is not intended to be limiting and may be replaced or augmented by any device having a processor, such as but not limited to a mobile telephone, or also set-top-box, TV, remote desktop computer, game console, tablet, mobile e.g.
  • laptop or other computer terminal, embedded remote unit which may either be networked itself (may itself be a node in a conventional communication network e.g.) or may be conventionally tethered to a networked device (to a device which is a node in a conventional communication network or is tethered directly or indirectly/ultimately to such a node).
  • the computing device may even be disconnected from e.g., WiFi, Bluetooth, etc. but may be tethered directly or ultimately to a networked device.
  • references herein to “said (or the) element x” having certain (e.g. , functional or relational) limitations/characteristics are not intended to imply that a single instance of element x is necessarily characterized by all the limitations/characteristics. Instead, “said (or the) element x” having certain (e.g. functional or relational) limitations/characteristics is intended to include both (a) an embodiment in which a single instance of element x is characterized by all of the limitations/characteristics and (b) embodiments in which plural instances of element x are provided, and each of the limitations/characteristics is satisfied by at least one instance of element x, but no single instance of element x satisfies all limitations/characteristics.

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Pest Control & Pesticides (AREA)
  • Engineering & Computer Science (AREA)
  • Insects & Arthropods (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • Environmental Sciences (AREA)
  • Catching Or Destruction (AREA)

Abstract

L'invention concerne un système de lutte contre des insectes fouisseurs, comprenant un serveur pour communiquer avec des capteurs sismiques à un ou plusieurs emplacements respectifs à l'intérieur des locaux. Chaque capteur permet de détecter des vibrations d'une substance consommable par des insectes, dans laquelle le capteur est entraîné, produisant des données de sortie de capteur. Une unité de communication communique, du capteur au serveur distant, i. des données de sortie de capteur et/ou ii. un identifiant unique au capteur individuel; et/ou iii. un horodatage identifiant un instant auquel les données ont été détectées par le ou les capteurs. Le serveur différencie, pour un ou plusieurs instants, une substance consommable par des insectes dans laquelle des insectes sont ou ne sont pas présents, et stocke des informations associant des identifiants de certains capteurs, à des données indiquant si des insectes fouisseurs sont présents ou non dans la substance consommable par des insectes dans laquelle chaque capteur est respectivement entraîné, à des instants stockés donnés correspondant à un ou plusieurs horodatages.
PCT/IL2024/050439 2023-05-18 2024-05-08 Système, procédé et produit-programme d'ordinateur pour une détection de termites améliorée Ceased WO2024236559A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202363503055P 2023-05-18 2023-05-18
US63/503,055 2023-05-18

Publications (1)

Publication Number Publication Date
WO2024236559A1 true WO2024236559A1 (fr) 2024-11-21

Family

ID=93518823

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IL2024/050439 Ceased WO2024236559A1 (fr) 2023-05-18 2024-05-08 Système, procédé et produit-programme d'ordinateur pour une détection de termites améliorée

Country Status (1)

Country Link
WO (1) WO2024236559A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200005626A1 (en) * 2018-06-29 2020-01-02 Smart Wave Technologies, Inc. Pest Control System Having Event Monitoring
WO2022232515A2 (fr) * 2020-05-08 2022-11-03 Royal Guemar Group, LLC Système de dispositifs d'amélioration des maisons en communication sur un réseau longue portée de faible puissance

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200005626A1 (en) * 2018-06-29 2020-01-02 Smart Wave Technologies, Inc. Pest Control System Having Event Monitoring
WO2022232515A2 (fr) * 2020-05-08 2022-11-03 Royal Guemar Group, LLC Système de dispositifs d'amélioration des maisons en communication sur un réseau longue portée de faible puissance

Similar Documents

Publication Publication Date Title
Zwerts et al. Methods for wildlife monitoring in tropical forests: Comparing human observations, camera traps, and passive acoustic sensors
Preti et al. Insect pest monitoring with camera-equipped traps: strengths and limitations
Furnas et al. Using automated recorders and occupancy models to monitor common forest birds across a large geographic region
Suraci et al. Behavior‐specific habitat selection by African lions may promote their persistence in a human‐dominated landscape
US20240420560A1 (en) Pest Control System Having Event Monitoring
Shonfield et al. Autonomous recording units in avian ecological research: current use and future applications.
CN111726986B (zh) 系统与方法
Shaked et al. Electronic traps for detection and population monitoring of adult fruit flies (Diptera: Tephritidae)
Borker et al. Vocal activity as a low cost and scalable index of seabird colony size
MacSwiney G et al. What you see is not what you get: the role of ultrasonic detectors in increasing inventory completeness in Neotropical bat assemblages
Digby et al. A practical comparison of manual and autonomous methods for acoustic monitoring
Fogarty et al. Influence of olfactory and visual cover on nest site selection and nest success for grassland‐nesting birds
Suraci et al. A new Automated Behavioural Response system to integrate playback experiments into camera trap studies
WO2016168347A1 (fr) Dispositif de piégeage intelligent de moustique et d'insecte sans fil, réseau et procédé de comptage de population de moustiques ou d'insectes
AU2017203296A1 (en) Active probe, system and method for pest detection
Santos et al. Automated electronic approaches for detecting disease vectors mosquitoes through the wing-beat frequency
US20220192171A1 (en) Pest control system and method for use thereof
Leseberg et al. Accounting for both automated recording unit detection space and signal recognition performance in acoustic surveys: a protocol applied to the cryptic and critically endangered Night Parrot (Pezoporus occidentalis)
Clement et al. Summer tree roost selection by Rafinesque's big‐eared bat
Balingbing et al. Application of a multi-layer convolutional neural network model to classify major insect pests in stored rice detected by an acoustic device
Orben et al. Comparing imaging, acoustics, and radar to monitor Leach’s storm-petrel colonies
Oliver-Villanueva et al. Advanced wireless sensors for termite detection in wood constructions
Lovera et al. Small rodent species on pig and dairy farms: habitat selection and distribution
Gottwald et al. Classifying the activity states of small vertebrates using automated VHF telemetry
Vélez et al. Implications of the scale of detection for inferring co‐occurrence patterns from paired camera traps and acoustic recorders

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24806776

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE