AU2021102427A4 - Ai-based micro-ecosystem monitoring device and method thereof - Google Patents

Abstract

"Al-based Micro-Ecosystem monitoring device and method thereof." Exemplary aspects of the present disclosure are directed towards the Al-based Micro Ecosystem monitoring device and method thereof, consisting of a plurality of ANIMAL IDENTIFICATION DEVICE (AID) 101 capable of identifying the type of animals based on Seismic and Video Images, ANIMAL MONITORING DEVICE (AMD) 102 capable of detecting Animal contingency and insurgency based on Seismic Signals. Microcontrollerl0la integrated with Camera 101b and a Geophone sensor 101c, hooterlOld and Vibrator 101f to formulate AID-101. AMD 102, runs appropriate MLA to identify the proximity between pay and the predator based on Animals detected and their proximity to each other. Once proximity establishes, AMD 102 activates the hooter 101d, Seismic vibrator and alerts the rangers/user nearby over WiFi mesh network 103 and GPRS 105 to avoid causalities and conflicts. Page 1 of 4 104 102 101 10WE ECS AMAZON WEB SERVICES (AWS) 106 2 USER AM D . interface TTGO-ESP32-700GSM PC/Mobile 105 Wi-Fi/RF433 Mesh Connected FIG I 100 Al based Micro-Ecosystem monitoring device

Description

Page 1 of 4

104

102 101 10WE ECS AMAZON WEB SERVICES

(AWS) 106

2

USER AM D . interface PC/Mobile TTGO-ESP32-700GSM

105

Wi-Fi/RF433 Mesh Connected

FIG I 100 Al based Micro-Ecosystem monitoring device

I TITLE

Al-Based Micro-Ecosystem monitoring device and method thereof

PREAMBLE TO THE DESCRIPTION

The following specification particularly describes the invention and the manner in which it is to be performed.

DESCRIPTION TECHNICAL FIELD

[0001] The present disclosure generally relates to animal kingdom protection. Most importantly, the micro-ecosystem and its constituents which are needed to be carefully monitored and protected from invaders, including humans.

BACKGROUND

[0002] The learned fact that the ecosystem can be considered as pieces of micro ecosystems wherein the endangered species are breaded or contained in a protected zone such as reserve forests extends up to 100 Square kilometres. Several endangered and rare animals/species are captivated in these reserves, which may be prey or predator. Poachers and other higher-order predators very much target these micro-ecosystems.

[0003] Though several alarm systems and mechanism are in place, averting predators and poachers is a big challenge. The alarming system might give some sounds to alert the animal/ human rangers or restrain the animals/poachers. But this system on long usage makes the poacher or animals adapt to the sounds of alarm and show no further results.

[0004] Numerous prior arts have made attempts to automate monitoring systems with multiple prototyping but haven't achieved more desirable feature in a single unit for the user.

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[0005] Similarly, several prior art disclosures have ascertained best devices and practices for animal/poacher detection using several means such as electric fence, laser fence, vibration sensor fence, IR fence, Combinational light fence, camera-based fence and so on.

[0006] Articles in the prior art by Bridget A. Matikainen-Ankney et al in their article Rodent Activity Detector (RAD), an Open Source Device for Measuring Activity in Rodent Home Cages discussed PIR sensors and how it detects when warm moving objects (such as an animal) cross its sensing zone. By mapping the area of activation using an infrared LED, they determined that in Allentown NextGen wire rack home cages with RAD placed above the wire rack, RAD tracks activity in -30% of the cage surface area.

[0007] In the prior art CN108399696A, with title Intrusion behavior recognition methods and device, described about the invention which alleviates conventional machines study identification intrusion behavior technical problem of high cost and poor for applicability. Invention includes : Obtain time series signal, wherein time series signal is sequence signal made of the tested amplitude for enclosing boundary's vibration arranges at any time ; Characteristic quantity is extracted from time series signal, wherein characteristic quantity includes : The maximum value of the tested peak swing for enclosing boundary's vibration, energy, spectrum energy breadth coefficient, energy gradient and energy gradient gradient ; Intrusion behavior identification is carried out using characteristic quantity as the input quantity of machine learning algorithm, to determine the tested intrusion behavior type for enclosing boundary by the output quantity of machine learning algorithm.

[0008] Another Prior art RU2013125902/08A- explained invention relates to means of detecting an intruder of extended security boundaries. The technical result is faster and more accurate determination of the area of intrusion. The system consists of a central security post and a plurality of electronic units, each connected to a group of signal processors. Each signal processor is connected to a segmented vibration-sensitive element mounted on a physical enclosure. The electronic units are connected to the central security post by a first RS-485 interface line. The signal processors are connected to corresponding electronic units by other CAN interface lines.

[0009] Similar prior art US5969608A - invention discloses an intrusion detector which has buried sensor modules arranged along a perimetero sense seismic vibrations caused by intrusions within the area defined by the perimeter. The sensor modules transmit data representative of the intrusions via magneto-inductive signals in the ELF to VLF range through ground, air, and/or water to at least one buried relay module within the area. The relay modules transmit RF signals representative of the intrusion data via a camouflaged RF antenna to mobil or fixed stations for appropriate action. Transmission of magneto-inductive signals in the ELF to VLF range is clandestine and reliable, and locations of buried sensor modules and relay modules are not revealed to intruders to reduce the possibility of evasion or tampering. The sensor modules may have sensor elements sensitive to humans, vehicles, and low flying aircraft to give enforcement officers the opportunity to better utilize their resources where the intrusions are occurring.

[0010] In Prior art W02018085949A1 with title Vibration-analysis system and method therefor emphasized on vibration/seismic survey, vibration monitoring, and the like. Invention contains vibration-detection unit may have a vibration-detection sensor and a positioning module for automatically determining the position thereof. The vibration-detection units may be geophones and the system may have a signal process module for compensating for the distortion introduced by the geophones.

[0011] US8705017B2 discloses a system for tracking airborne organisms includes an imager, a backlight source (such as a retroreflective surface) in view of the imager, and a processor configured to analyze one or more images captured by the processor to identify a biological property of an organism.

[0012] An prior art document EP2318804B1 discloses system for detecting intrusion, said system comprising: an illumination source projecting an array of illuminating beams distinguished by beam identifying features, along different optical paths; a detector array comprising elements detecting reflected illumination received in an array of fields of view, said reflected illumination originating from said array of illuminating beams, and said elements using said beam identifying features to determine from which of said illuminating beams said reflected illumination originates; and a signal processing system adapted to detect changes in the reflected illumination levels detected by said elements of said detector array, wherein an increase greater than a first predefined level in said reflected illumination from the field of view associated with an element, provides an indication of an intrusion at the crossing point of that field of view associated with said element, with that optical path whose illuminating beam generates said increase in reflected illumination detected by said element, said optical path being defined by said beam identifying feature of said reflected illumination detected

[0013] Another prior art document WO 2004/008403 describes a laser based range finder for use with cameras of an intrusion detecting system.

[0014] Another prior art document US 4065778 shows a focussing apparatus for a camera where the distance to an object is measured via the intersection of a light beam with the camera's field of view.

[0015] Referring to another document, US 20080222942A1 discloses A method of detecting and exterminating rodents can include collecting geographical data of underground rodent tunnels and nests within a defined geographical region. Additionally, the method can include processing the geographical data, and presenting the data in a form Sufficient to allow an operator to identify a location of the underground rodent tunnels and nests. A method of detecting and exterminating rodents also can include exterminating rodents dwelling in the under round tunnels and nests. A system of detecting and exterminating rodents includes a Substantially above-ground Surveyor which can generate data of underground rodent tunnels and nests, and a data processor which can receive, store, interpret and present the data generated by the Surveyor.

[0016] US20150369591A1 document titled Optical detection systems and methods of using the same where the invention invention relates generally to the field of optical detection systems and, more particularly, to improved systems and methods for accurately detecting presence in, and/or interference with, an area to be monitored using fiber optics.

[0017] JP5263692B2, presented an invention relates to a laser scan sensor that detects, for example, an intruder into a building site, and in particular, after a warning area is set, a new harmless obstacle is installed in the warning area or a car or the like enters. The present invention relates to a laser scan sensor capable of accurately detecting an intruder that should be detected regardless of the presence of the intruder even when the vehicle is parked.

[0018] In an eraly document by Caitlin E O'Connell-Rodwell Et al in thieir publication titled Vibrational Communication in Elephants: A Case for Bone Conduction presented physiological data on bone conduction hearing from cadaveric temporal bone ears of

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an elephant. They discuss the results in the context of the elephant's ability to detect and interpret ground-borne vibrations as signals and compare with similar measurements in a human cadaveric temporal bone ear. Since elephant ossicles are at least seven times the mass of human ossicles, they compared the sensitivity of both species to vibrations in the frequency range of 8 ,000 Hz and report that elephants have up to an order of magnitude greater sensitivity below 200 Hz, indicating a heightened sensitivity to bone conduction hearing in comparison to humans.

[0019] In a prior document by Peggy S. M. Hill discussed about How do animals use substrate-borne vibrations as an information source?. Stated that, alongside visual signals, songs, or pheromones exists another major communication channel that has been rather neglected until recent decades: substrate-borne vibration. Vibrations carried in the substrate are considered to provide a very old and apparently ubiquitous communication channel that is used alone or in combination with other information channels in multimodal signaling. The substrate could be 'the ground', or a plant leaf or stem, or the surface of water, or a spider's web, or a honeybee's honeycomb. Animals moving on these substrates typically create incidental vibrations that can alert others to their presence. They also may use behaviors to create vibrational waves that are employed in the contexts of mate location and identification, courtship and mating, maternal care and sibling interactions, predation, predator avoidance, foraging, and general recruitment of family members to work. In fact, animals use substrate-borne vibrations to signal in the same contexts that they use vision, hearing, touch, taste, or smell.

[0020] In an invention stated in document CN107527009A, the invention discloses a kind of remnant object detection method based on YOLO target detections, is related to intelligent monitoring, computer vision, deep learning field. The present invention is detected in real-time by YOLO targets, obtains the target classification in every frame image data, and specific coordinate corresponding to it. The non-object target, such as row humans and animals, has accurately been filtered by target classification, has greatly reduced the interference judged follow-up legacy.

[0021] The present invention provides an effective Poacher and animal detection and restaining system based on a mixture of Image processing Vibration, and bioacoustics.

[0022] The present invention addresses the shortcomings mentioned above of the prior art.

[0023] All publications herein are incorporated by reference to the same extent as if each publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies, and the definition of that term in the reference does not apply.

SUMMARY

[0024] The following presents a simplified summary of the disclosure in order to provide a basic understanding of the reader. This summary is not an extensive overview of the disclosure, and it does not identify key/critical elements of the invention or delineate the scope of the invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later.

[0025] Exemplary embodiments of the present disclosure are directed towards the Al based Micro-Ecosystem monitoring device.

[0026] An exemplary object of the present disclosure is directed towards a system that monitors and restrain the intruding poacher and animals.

[0027] Another exemplary object of the present disclosure is directed towards the integration of microcontroller 101a with camera 101b, geophone 101c, Audio Speaker and Microphone 101d and 101e, and Vibrator 101f to make ANIMAL IDENTIFYING DEVICE (AID) 101. Whose primary function is to monitor intruding rodents and animals by identifying their seismic vibrations.

[0028] Another exemplary object of the present disclosure is integrating microcontroller 101a with camera 101b to detect an intruding object by using an image detection algorithm.

[0029] An exemplary aspect of the present subject matter is directed towards the integration of microcontroller 101a with camera 101b for transmitting video between Microcontroller 101a and AMD 103 for detecting intruding animal or poacher.

[0030] An exemplary aspect of the present subject matter is directed towards the use of Microcontroller 101a for detecting intruding animals or Poachers using a Machine Learning based Image recognition algorithm and execute an appropriate averting command.

[0031] An exemplary aspect of the present subject matter is directed towards the implementation of mechanical vibrator 101f, which exerts seismic vibrations of specific predator determined by AMD 103.

[0032] Another exemplary aspect of the present disclosure is directed towards playback the bio-acoustic sounds of the corresponding predator through Audio-speaker 101d by AID101.

[0033] Another exemplary aspect of the present disclosure is directed towards the use of relevant Machine Learning Algorithm MLA to predict the proximity of threat to the prey from a nearby predator.

[0034] Another exemplary aspect of the present disclosure directed towards integrating microcontroller 101a with Camera 101b, Geophone 101c and Microphone 10ld to determine the exact animal/poacher by executive relevant Image processing Algorithms.

[0035] Another exemplary aspect of the present disclosure directed towards intimating the intruder by AMD 103 to user 106 about the avert through GPRS 105.

BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In the following, numerous specific details are set forth to provide a thorough description of various embodiments. Certain embodiments may be practised without these specific details or with some variations in detail. In some instances, certain features are described in less detail so as not to obscure other aspects. The level of detail associated with each of the elements or features should not be construed to qualify the novelty or importance of one feature over the others.

[0037] FIG.1 is a diagram depicting 100 A-based Micro-Ecosystem monitoring device, according to an exemplary embodiment of the present disclosure.

[0038] FIG. 2 is a representation of Component Architecture of Animal Identification Device, according to an exemplary embodiment of the present disclosure.

[0039] FIG. 3 is a representation 102 Component Architecture Of Animal Monitoring Device (AMD), according to an exemplary embodiment of the present disclosure.

[0040] FIG. 4 is a diagram 400 Process Executed In Al-based Micro-Ecosystem monitoring device, according to an exemplary embodiment of the present disclosure.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0041] It is to be understood that the present disclosure is not limited in its application to the details of construction and the arrangement of components outlined in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practised or of being carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.

[0042] The use of "including," "comprising," or "having" and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. Further, the use of terms "first," "second," and "third," and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another.

[0043] Referring to FIG. 1 is a diagram depicting the 100 Al-based Micro-Ecosystem monitoring device comprising of a plurality of ANIMAL IDENTIFICATION DEVICE (AID) 101 capable of identifying the type of animals based on Seismic, Bioacoustic and Video Images. Microcontroller 101a integrated with Camera 101b and a Geophone sensor 101c, Audio Speaker 101d, Microphone 101e and Vibrator 101f to formulate ANIMAL IDENTIFICATION DEVICE (AID)-101.

[0044] Further to it, all these devices communicate with each other in WiFi Mesh network. Wherein WiFi mesh network once established between all the devices then the necessity of router or special signalling devices is eliminated. AID 101 traces the surface anomaly and alerts the concerned Animal Monitoring Device (AMD) 103 through a WiFi mesh network 102. Which in turn senses the executes the relevant MLA on seismic vibrations, bioacoustics and video feed of all the relevant AID 101 wherein animals were traced to find the proximity. If a proximity alert is activated, respective AID 101 rises alarm both vibrational and bioacoustic. This information is sent back to AMD102 so that it would communicate the intrusion information to the user106 over GRPS 105.

[0045] In accordance with a non-limiting exemplary embodiment of the present subject matter, FIG. 2 depicts the component layout of ANIMAL IDENTIFICATION DEVICE (AID) 101 capable of identifying the type of animals based on Seismic, bioacoustic and Video Images. Microcontroller 101a integrated with Camera 101b and a Geophone sensor 101c, Audio Speaker 101d, Microphone 101e and Vibrator 101f to formulate ANIMAL IDENTIFICATION DEVICE (AID)-101. All AID-101 are connected in WiFi Mesh, and this eliminates the necessity of routing device. Microcontroller 101a acquires seismic activities and bioacoustics to identify the incoming animal. If the presence is observed, then a video feed is acquired to pinpoint the intruder position, direction and type. All this data is transmitted to AMD 103 over 102.

[0046] Referring to FIG 3 is a diagram depicting Component Architecture Of Animal Monitoring Device (AMD) 103. AMD 103 bridges the connection between AIDs and Cloud platforms. AIDs connect to AMD with no dependency on internet and WiFi routers and AMD with Cloud Platforms. The AMD 103 has an onboard LoRa based ESP32, an OLED display, 2.4GHz antenna, onboard SD Card Holder, ceramic antenna for GSM signal trans receiver, LiPo battery and GSM700 module. Initially, ESP32 receives data through an antenna transmitted by AIDs. The power data then send to OLED display along with time and ID of AIDs and as well as cloud Platforms. The transmission of data from AMD takes place in the form of GPRS data packets forming internet connectivity. The Power Data is then stored to SD memory card.

[0047] Following is a non-limiting exemplary embodiment of the present subject matter, as shown in FIG. 4, which is a 400 Process Executed in An Al-based Micro-Ecosystem monitoring device. The process starts at step 401; microcontroller 10la Acquire Seismic Vibration through 101c and bioacoustics through 101f to confirm the animal presence. In step 402, microcontroller 102a acquire video feed through camera 101b and Execute Machine Learning Algorithm-i (MLA-1) to Compare the Images, bioacoustics and Seismic values with stored values and determine the exact animal and send data to Animal Monitoring Device

1u

(AMD) 103. Step 403, AMD 103 collect the data from the plurality of AID 101, time and ID stamps the data and executes relevant MLA-2 to predict proximity of prey and predator.

[0048] Further in step 404, If AMD 103 predicts the proximity of prey and predator and there is high chance of conflict then, AMD 103 signals AID 101 over 103 to trigger bioacoustics sound 10ld and Seismic Vibrators 101f. In step 405, If AID 101 ascertains human and AMD 103 predicts the proximity of prey and predator, and if there is a high chance that the predator is a Human and the animal is having eminent danger, AMD 103 sends a notification to the Rangers/user 106 over 105.

[0049] Subsequently, in step 409, microcontroller 101a check Check for seismic activity, bioacoustics and Video feed data and confirm the presence of a predator and report the event to user. In step 410, microcontroller 10la repeat the process till the animal is restrained from entering the proximity and inform the user through GPRS 101c

[0050] In an embodiment, the machine learning algorithm uses the Random forest method which is trained at 80-20 and set to predict the proximity and conflict of prey and predators. The Amazon Web Services (AWS) is the cloud-based aggregators which receive the data from AMD 103 over 105 communication protocol. Cloud architecture for Amazon Web Services (AWS) where data will be received, stored, analyzed, and exudes commands. The data received will be done by AWS IoT Core which is an MQQT broker, which is a server that receives all messages from the clients and then routes the messages to the appropriate destination clients; here the client is AWS. The AWS IoT rules engine receives this data and writes it to a DynamoDB table, and the data will be present here for easy retrieval by user interface. Once the user requests specific data, the query will be fetched, and the results will be displayed on the user interface. For this to happen, Amazon API gateway will act accordingly by triggering the Lambda function based on user selections, then collects the data from DynamoDB and uses AWS Graphic QL lambda function to display relevant graphics with data. Further to this, the AWS Machine learning module is deployed for every trigger alert by the AID. AWS ML module search for proximity between triggered AIDs. If only one AID is triggered, it only executes relevant MLA to determine the type of animal; if a hunter/poacher is identified by AID, the relevant coordinates are transmitted to the user/ranger. If two AIDs are triggered, and a proximity algorithm identifies the presence of an imminent threat to the prey/animal, a mitigative step is initiated accordingly, and information is passed to the user.

Claims (3)

CLAIMS STATEMENT We claim

1. An Al-based Micro-Ecosystem monitoring device and method thereof consisting of, a plurality of ANIMAL IDENTIFICATION DEVICE (AID) 101 capable of identifying the type of animals based on Seismic, Bioacustics and Video Images; and ANIMAL MONITORING DEVICE (AMD) 102 capable of detecting Animal contingency and insurgency based on Seismic Signals.

2. The device as claimed in claim 1, the Microcontroller 10la integrated with Camera 10lb and a Geophone sensor 101c, Speaker101d , microphone 10le and Vibrator 101f to formulate AID-101.

3. The device as claimed in claim 1, AMD 102, runs appropriate MLA to identify the proximity between pay and the predator based on Animals detected and their proximity to each other. Once proximity establishes, AMD 102 activates the hooter 101d, Seismic vibrator and alerts the rangers/user nearby over WiFi mesh network 103 and GPRS 105 to avoid causalities and conflicts.

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103 102 101 AMAZON WEB SEVICES

(AWS) 106 AID 2021102427

AID 1 2

USER AMD Interface PC/Mobile TTGO-ESP32-700GSM AID 3

AID 105 n

Wi-Fi/RF433 Mesh Connected

FIG 1 100 AI based Micro-Ecosystem monitoring device

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101a 101b 2021102427

101d & 101e

Audio 101f 101c Speaker Microph one

102 COMPONENT ARCHITECTURE OF ANIMAL IDENTIFYING DEVICE (AID) FIG 2

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103c 2021102427

103b

103d 103a

103e 103f

103 ANIMAL MONITORING DEVICE FIG 3

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Acquire Seismic Vibration through 101c and bioacoustics through 401 101f to confirm the animal presence.

402 Acquire video feed through camera 101b and Execute Machine 4 Learning Algorithm-1 (MLA-1) to Compare the Images and Seismic values with stored values and determine the exact animal 2021102427

and send data to Animal Monitoring Device (AMD) 103

AMD 103 collect the data from plurality of AID 101 and executes 403 relevant MLA-2 to predict proximity of prey and predator.

If AMD 103 predicts the proximity of prey and predator and there is high chance of conflict then, AMD 103 signals AID 101 over 103 404 to trigger bioacoustics sound 101d and Seismic Vibrators 101f

If AID 101 ascertains human and AMD 103 predicts the proximity 405 of prey and predator and if there is high chance that the predator is a Human and the animal is having eminent danger, AMD 103 sends notification to the Rangers/user 106 over 105

Check for seismic activity, bioacoustics and Video feed data and 406 confirm the presence of predator and report the event to user

410 Repeat the process till the animal is restrained from entering the proximity and inform the user though GPRS 101c

FIG 4 400 Process Executed AI based Micro-Ecosystem monitoring device