SE1650682A1 - Systems and methods for determining likelihood of states in cattle animal - Google Patents

Systems and methods for determining likelihood of states in cattle animal Download PDF

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Publication number
SE1650682A1
SE1650682A1 SE1650682A SE1650682A SE1650682A1 SE 1650682 A1 SE1650682 A1 SE 1650682A1 SE 1650682 A SE1650682 A SE 1650682A SE 1650682 A SE1650682 A SE 1650682A SE 1650682 A1 SE1650682 A1 SE 1650682A1
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SE
Sweden
Prior art keywords
movement
determining
images
movement pattern
cattle
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SE1650682A
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Swedish (sv)
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SE541891C2 (en
Inventor
Mccarthy Graham
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Bmp Innovation Ab
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Publication date
Application filed by Bmp Innovation Ab filed Critical Bmp Innovation Ab
Priority to SE1650682A priority Critical patent/SE541891C2/en
Priority to PCT/SE2017/050535 priority patent/WO2017200480A1/en
Publication of SE1650682A1 publication Critical patent/SE1650682A1/en
Publication of SE541891C2 publication Critical patent/SE541891C2/en

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Classifications

    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01K—ANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K29/00—Other apparatus for animal husbandry
    • A01K29/005—Monitoring or measuring activity, e.g. detecting heat or mating
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61D—VETERINARY INSTRUMENTS, IMPLEMENTS, TOOLS, OR METHODS
    • A61D17/00—Devices for indicating trouble during labour of animals ; Methods or instruments for detecting pregnancy-related states of animals
    • A61D17/002—Devices for indicating trouble during labour of animals ; Methods or instruments for detecting pregnancy-related states of animals for detecting period of heat of animals, i.e. for detecting oestrus
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61D—VETERINARY INSTRUMENTS, IMPLEMENTS, TOOLS, OR METHODS
    • A61D17/00—Devices for indicating trouble during labour of animals ; Methods or instruments for detecting pregnancy-related states of animals
    • A61D17/006—Devices for indicating trouble during labour of animals ; Methods or instruments for detecting pregnancy-related states of animals for detecting pregnancy of animals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61D—VETERINARY INSTRUMENTS, IMPLEMENTS, TOOLS, OR METHODS
    • A61D17/00—Devices for indicating trouble during labour of animals ; Methods or instruments for detecting pregnancy-related states of animals
    • A61D17/008—Devices for indicating trouble during labour of animals ; Methods or instruments for detecting pregnancy-related states of animals for detecting birth of animals, e.g. parturition alarm
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion

Abstract

Disclosed is a method for determining a likelihood for a state of a cattle animal. The method comprises capturing (S100), by a motion detector, a plurality of images of a cattle animal and determining (S110), based on the plurality of images, a movement pattern of the cattle animal. The method further comprises comparing (S120) the determined movement pattern with a reference movement pattern and then determining (S130), based on the comparison, if the detected movement pattern correlates with a known state out of a plurality of known states, the plurality of known states including at least oestrus, pregnancy and parturition. The method further comprises outputting (S140), based on the determining, a likelihood value for at least one of the plurality of known states.(Fig. 1)

Description

SYSTEMS AND METHODS FOR DETERMINING LIKELIHOOD OF STATES INCATTLE ANIMAL Technical field
[0001] The present invention relates to a method and device for determiningIikelihoods of states in cattle animal.
Background art
[0002] Traditional methods for identifying and determining that a cow is in thestate of oestrus involve spending a lot of time physically present in the stables, atleast 20-30 minutes per day, four to five times a day. Naturally, this method is verytime consuming while still being approximate, and cattle animal only go intooestrus for short periods of time. Furthermore, the likelihood of succeeding withartificial insemination of cattle animals are at best 70%. ln combination with theuncertainties regarding if the cattle animal really is in oestrus, the accumulatedsuccess rate is generally quite low. On top of that, a feedback inspection toinvestigate if the insemination was successful is also critical in these kinds ofsystems, since it is needed to determine if the process needs to be repeated.Furthermore, insufficient monitoring around the time of parturition might prolong the birth process unnecessarily, thereby increasing the risk of stillbirth.
[0003] Thus, there is a need for better ways of determining how likely a cattleanimal is to be in any significant state of the states including at least oestrus,pregnancy and parturition.
Summary of invention
[0004] An object of the present invention is to solve at least some of theproblems outlined above. An object is to provide systems and method fordetermining Iikelihoods of states in cattle animal, such as oestrus, pregnancy andparturition. By having such a determination of likelihoods, resource efficiency canbe greatly increased, e.g. by decreasing the amount of failed inseminations.
[0005] According to a first aspect, there is provided a method for determining aIikelihood for a state of an animal. The method comprises capturing a plurality ofimages of a cattle animal, in some embodiments this is done with a motiondetector. The method further comprises determining, based on the plurality ofimages, a movement pattern of the cattle animal, and comparing the determinedmovement pattern with a reference movement pattern. The method furthercomprises determining, based on the comparison, if the detected movementcorrelates with a known state out of a plurality of known states, the plurality ofknown states typically including at least oestrus, pregnancy and parturition; andbased on the determining, outputting a Iikelihood for at least one of the plurality ofknown states. ln some embodiments, the method may only to determine theIikelihood of one state, such as oestrus.
[0006] According to optional embodiments, the step of determining a movementof the cattle animal comprises comparing the captured images with images in adatabase and choosing a movement that correlates best with the captured images,from a database with a plurality of movements. ln some embodiments, the step ofdetermining a movement may comprise using predetermined variables to specify what kind of movement is being searched for.
[0007] According to optional embodiments, the reference movement pattern isanother movement pattern related to the same cattle animal. ln someembodiments, the reference movement pattern may be a previously recordedmovement pattern of another cattle animal, or it may be a combination of amovement pattern from other cattle animals and previous movement patterns fromthe same cattle animal. ln some embodiments, the reference movement patternmay be a statistically derived movement pattern obtained by analyzing a number of movement patterns in a database.
[0008] ln some embodiments, the reference movement pattern may be achange in movement measured over time. The reference movement pattern may be a single movement, or it may be a sequence of multiple movements.
[0009] According to optional embodiments, the reference movement is amovement obtained from a database comprising historical records with movements of cattle animals.
[0010] According to optional embodiments, the method further comprisesoutputting an alert if the detected movement does not correlate with any knownstate.
[0011] According to optional embodiments, the method comprises the usage of abductive reasoning in order to determine likelihoods.
[0012] According to a second aspect, there is provided a system for determininga likelihood of a state in a cattle animal. The system comprises a motion detectoradapted to capture a plurality of images of a cattle animal and a processing unitwith a memory, operatively connected to the motion detector. The system furthercomprises a database, operatively connected to the processing unit and to themotion detector. The processing unit is adapted to determine, based on theplurality of images, a movement pattern of the cattle animal and further todetermine, based on the comparison, if the detected movement pattern correlateswith a known state of a plurality of known states, the plurality of known statesincluding at least oestrus, pregnancy and parturition. The system is furtheradapted to, based on the determining, outputting a likelihood value for at least one of the plurality of known states.
[0013] The aspects and embodiments described above are freely combinablewith each other. There are optional embodiments of the second aspect that correspond to the optional embodiments of the first aspect.
Brief description of drawinqs
[0014] The solution will now be described more in detail, by way of example, with reference to the accompanying drawings, in which:[0015] Fig. 1 is a flow chart ofa method according to the present disclosure.
[0016] Fig. 2 shows a system according to the present disclosure.
Description of embodiments
[0017] ln the following, a detailed description of a system and a methodaccording to the invention will be given.
[0018] Visual observation is generally considered the most effective method fordetermining the time of oestrus, however it is very time consuming. By measuring,tracking and classifying an animal's movement pattern using video motiondetection, it is possible to determine their point in the reproductive cycle.The mainindication is so called standing oestrus and is simply the changes in animalbehaviour that are associated with an animal standing to be mounted by a bull oranother female. Other indications include chin resting, sniffing and licking of theurogenital region. All these movements and behaviour can be detected,distinguished and characterised using video imaging and image processing. Whendetecting and analyzing movement patterns, the above are some examples ofmovement patterns that may be detected which are indicative of oestrus.
[0019] By monitoring these movements and correlating them to knownmovement patterns and/or movement pattern fluctuations, it is possible todetermine the point in the reproductive cycle, with a certain likelihood value. As willbe understood, the likelihood value can be 100%, indicating that the animal woulddefinitely be in a specific state and it can be 0%, indicating that the animal woulddefinitely not be in a specific state.
[0020] Shortly described, the methods and systems of the present disclosurerelate to determining likelihoods for different states in animals. The disclosurerelates to monitoring and distinguishing movements of an individual animal, evenwithin a herd. The movement information is tracked, logged, analysed andcharacterised. lf the movement data corresponds to a known state that can beattributed to a distinctive characteristic, including at least one of oestrus,pregnancy or parturition, i.e. correlated to a database, the result is characterisedand flagged. lf the movement data is noteworthy but cannot be attributed to adistinctive characteristic the result is flagged and characterised as an unknown event so further investigations can be instigated.
[0021] Looking now at Fig. 1, the steps of a method according to the present disclosure will now be described in more detail.
[0022] The method comprises a step S100 of capturing a plurality of images of acattle animal. The capturing is performed by way of a motion detector with thecapability of storing images, such as a camera. The reason for capturing a pluralityof images, over a period of time, is to be able to identify a movement of a cattleanimal. Typically, the step S100 of capturing images lasts for approximately 30seconds.
[0023] The method may optionally comprise a step of storing the detectedmovement pattern in a database, immediately after the detection step and beforethe step S110. Generally speaking, systems that implement the method accordingto the present disclosure will have functionalities that allow for results to be storedin a database, and in cases where the result is not stored during the process ofanalyzing a specific cattle animal, it will in most cases be stored at some point,even though it may be after all the measurements and analyses have beenperformed.
[0024] Thus, after capturing S100 a plurality of images, the method comprises astep S110 of determining a movement pattern of the cattle animal. The phrase“movement pattern” can mean both a single movement or multiple movements, orit may entail a change in movement. ln some embodiments, movement patternmay also mean a result from measuring and analyzing multiple movements over aperiods oftime, possibly with certain time intervals in between measurementperiods. lt is also possible for the movement pattern to be a combination of thesethings, e.g. a change over time in combination with a specific movement currently being determined.
[0025] The data necessary to determine a movement is usually related tovectors and matrixes needed to transform an image into a movement pattern.Even though a detector such as a camera may be used, it is not data regularlycaptured by a regular camera, such as one present in a smartphone, that is the most relevant variable for making the determination. As such, the quality of themotion detector used for capturing is an important factor.
[0026] ln a typical embodiment of the method, determining a movement patterncomprises determining a movement. This may be done by comparing the capturedimages with previously stored images of cattle animal in movement, andcomparing the captured images with the stored results in order to determine if theymatch. lt is also possible to determine specific variables to look for before theimages are captured, such as being on the lookout for a specific movement of the leg.
[0027] When a movement of the cattle animal has been determined, the methodfurther comprises a step S120 of comparing the determined movement patternwith a reference movement pattern. The reference movement pattern is typicallyobtained from a database comprising large amounts of previously recordedmovement data related to cattle animal. ln some embodiments, the referencemovement pattern may be multiple movement patterns. Each reference movementpattern may correlate with at least one specific known state, including at leastoestrus, pregnancy and parturition. lt is also possible for a reference movementpattern to correlate with multiple different states at once, possibly to a varying degree.
[0028] ln some embodiments, the known states may include only one ofoestrus, pregnancy and parturition. ln some embodiments, the known states mayinclude all three of oestrus, pregnancy and parturition. ln other embodiments, theknown states may include two of oestrus, pregnancy and parturition.Correspondingly, the step of outputting a likelihood value for at least one of theplurality of states, may entail outputting a likelihood value for only one state, or fortwo states, or outputting a likelihood value for each of the three states.
[0029] ln some embodiments, the reference movement pattern comprisesanother movement pattern related to the same cattle animal. Depending on theembodiment, as will be evident from the description above, the reference movement pattern may include at least one reference movement pattern from the same cattle animal that is being examined, and at least one reference movementpattern from at least one other cattle animal. By doing this, it may be possible todetect changes in behavior in the cattle animal both relative to themselves, andrelative to other cattle animal, and also relative to a statistical changes derived from a vast number of measurements.
[0030] Following the comparison step, the method comprises determining S130whether the detected movement pattern correlated with a known state or not,wherein the known states includes at least oestrus, pregnancy and parturition. lnsome embodiments, the steps of comparing S120 and determining S130 may beperformed more or less as one step, wherein the detected movement pattern iscompared to a number of reference movement patterns, each being indicative ofat least one state, and then the determination follows directly from which referencemovement pattern(s) the detected movement pattern correlates best with. Thus,determining if the detected movement pattern correlates with a known state mayentail determining if the detected movement pattern correlates with previouslydetected and recorder movement patterns that, in turn, are indicative of certainstates.
[0031] After it has been determined if the detected movement pattern correlateswith a known state, and preferably which state it correlates with, the methodcomprises a step S130 of outputting a likelihood value for at least one state,meaning the likelihood of the cattle animal being in that particular state at the timeof performing the measurements. ln some embodiments, the method comprisesoutputting the likelihood for at least each of the states including oestrus,pregnancy and parturition. As will be understood, it is possible for the likelihood ofcertain states to be 0% or 100%, and anything in between.
[0032] ln some embodiments, the method comprises the usage of abductivereasoning in order to infer correlations from likelihoods, in such a way that when atleast two different values are available, the certainty of an indicator which isdependent on two variables will be a great deal higher than the certainty of anindicator is dependent on only one variable. The usage of abduction, as opposed to deduction, does not guarantee the conclusion, but may instead be seen as aninference to the best possible explanation, i.e. the most likely one.
[0033] An example of how abductive reasoning may be used in the context ofthe present disclosure will now be described. lf we assume that event B followsfrom event A, we can measure B in order to determine the likelihood of the eventA. An example being representative of the present disclosure: lf oestrus follows aspecific pattern of movement, we can measure body movement patterns to determine likelihoods of oestrus.
[0034] Looking now at Fig. 2, a system according to the present disclosure willnow be described.
[0035] The system comprises a motion detector 200, typically a camera withhigh quality and the specific functionality of providing a lot of metadata other thanthe apparent visual data of an image, such as a camera having revolution andframe rate no less than what is considered TV standard, such as PAL. The camerais adapted for capturing a plurality of images of a cattle animal, and the cameramay be both portable and stationary depending on the specific implementation.
[0036] The motion detector 200 is operatively connected to a database 210,comprising historical movement data of cattle, wherein the database may furtherbe used to store the images captured by the motion detector 200 and all datarelated to them, as well as results and other data that is generated when applyinge.g. a method according to the present disclosure.
[0037] The system further comprises an entity capable of processing data, suchas a computer 220. The computer 220 comprises a memory and a processor andis operable to execute instructions and is also operatively connected to the motiondetector 200. ln some embodiments, the database 210 and the computer 220 arealso operatively connected to each other.
[0038] The entities of the system may be connected to each other by any means suitable for the implementation, such as for instance by means of cable or wirelessly. lt is also possible to have a mix between wired and wirelessconnections in the system.
[0039] ln some embodiments, the system is stationary, and the cattle animalsthat are to be inspected are moved to the location of the system. ln otherembodiments, the system may be portable.
[0040] A typical usage of the system will now be describe as an example. Acattle animal is chosen as a subject for determining the likelihood of a certain statein that cattle animal. The states typically include at least oestrus, pregnancy andparturition, but in some embodiments, the system may be adapted to look only fora specific state, such as oestrus. Then a plurality of images of the cattle animal iscaptured with the motion detector 200. This may be performed by moving thecattle animal to the motion detector 200, or it may entail moving the motiondetector 200 close to the cattle.
[0041] Then a plurality of images are captured by the motion detector 200, andis sent to the computer 220 in order to determine which movement pattern is beingcaptured. The computer does this processing by retrieving data from the database210 and comparing it to the captured images.
[0042] When the computer 220 has decided which movement pattern is beingcaptured, the next step is to compare this movement pattern with a referencemovement pattern. However, there is a possibility that the captured images wouldnot correlate with any images stored in the database, in this case the system maybe adapted to trigger an alarm to signal this.
[0043] The reference movement pattern is typically retrieved from the database210, and used to determine if the movement pattern detected from the capturedimages correlates with any known state, including at least the states of oestrus,pregnancy and parturition. ln some embodiments, this entails comparing thecaptured movement pattern with a vast number of stored movement patterns in order to determine which movement pattern is the best match.
[0044] Then, based on the determining, the system will output a Iikelihood valuefor at least one of the states, such as for example a 67% change of the cattleanimal being in oestrus. The outputting may be done on the computer 220 or onthe motion detector 200, depending on the specific implementation. Typically, theresults are shown on a display which is operatively connected to at least one ofthe computer 220 and the motion detector 200.
[0045] The system may further comprise various input and output devices, thatmay be used to interact with the other parts of the system.
[0046] lt should be understood that the system may also be implemented as amotion detector 200 having a processing device and memory thereon, adapted toperform the same functions as the computer 220, in which implementation themotion detector 200 is able to perform the processing as well.
[0047] Although the description above contains a plurality of specificities, theseshould not be construed as limiting the scope of the concept described herein butas merely providing illustrations of some exemplifying embodiments of thedescribed concept. lt will be appreciated that the scope of the presently describedconcept fully encompasses other embodiments which may become obvious tothose skilled in the art, and that the scope of the presently described concept isaccordingly not to be limited. Reference to an element in the singular is notintended to mean "one and only one" unless explicitly so stated, but rather "one ormore". Moreover, it is not necessary for an apparatus or method to address eachand every problem sought to be solved by the presently described concept, for it to be encompassed hereby.

Claims (10)

1. Method for determining a likelihood for a state of a cattle animal, comprising thesteps of: capturing (S100), a plurality of images of a cattle animal; determining (S110), based on the plurality of images, a movement pattern of the cattle animal; comparing (S120) the determined movement pattern with a reference movementpattern; determining (S130), based on the comparison, if the detected movement patterncorrelates with a known state out of a plurality of known states, the plurality of known states including at least oestrus, pregnancy and parturition; and outputting (S140), based on the determining (S130) a likelihood value for at leastone of the plurality of known states.
2. The method according to any one of the previous claims, wherein the step ofdetermining a movement of the cattle animal comprises comparing the capturedimages with images in a database and choosing a movement that correlates bestwith the captured images, from a database with a plurality of movements.
3. The method according to any one of the previous claims, wherein the reference movement pattern is a movement pattern related to the same cattle animal.
4. The method according to any one of the previous claims, wherein the referencemovement is a movement obtained from a database comprising historical records with movements of cattle animals.
5. The method according to claim 1, wherein determining if the detectedmovement correlates with a known state step comprises abduction
6. A system for determining a likelihood for a state of an animal, comprising: 12 a motion detector (200), adapted to capture a plurality of images of a cattle animal; a processing unit and a memory (220), operatively connected to the motiondetector; a database (210), operatively connected to the processing unit and to the motiondetector, wherein the processing unit is adapted to: determine, based on the plurality of images, a movement pattern of the cattle animal; determine, based on the comparison, if the detected movement pattern corre|ateswith a known state of a plurality of known states, the plurality of known states including at least oestrus, pregnancy and parturition; and based on the determining, outputting a likelihood value for at least one of theplurality of known states.
7. The system according to claim 6, wherein the processing unit is further adaptedto determine a movement of the cattle animal by comparing the captured imageswith images in a database and choosing a movement that corre|ates best with thecaptured images, from a database with a plurality of movements.
8. The system according to any one of claims 6 or 7, wherein the reference movement pattern is a movement pattern related to the same cattle animal.
9. The system according to any one of claims 6-8, wherein the referencemovement is obtained from a database comprising historical records with movements of cattle animals.
10. The system according to any one of claims 6-9, wherein the processing unit isadapted to use abductive reasoning when determining if the detected movementcorre|ates with a known state.
SE1650682A 2016-05-19 2016-05-19 Systems and methods for determining likelihood of states in cattle animal SE541891C2 (en)

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PCT/SE2017/050535 WO2017200480A1 (en) 2016-05-19 2017-05-19 Systems and methods for determining likelihood of states in cattle animal

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SE544419C2 (en) * 2020-11-19 2022-05-17 Videquus Ab A computer-implemented method for monitoring a horse to predict foaling

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FR2759541B1 (en) * 1997-02-18 1999-04-23 Visio Systemes METHOD AND INSTALLATION FOR AUTOMATICALLY REMOTE DETECTION OF THE ACTIVITY OF FREE STABLE ANIMALS
US8634635B2 (en) * 2008-10-30 2014-01-21 Clever Sys, Inc. System and method for stereo-view multiple animal behavior characterization
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JP6199791B2 (en) * 2013-04-23 2017-09-20 パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカＰａｎａｓｏｎｉｃ Ｉｎｔｅｌｌｅｃｔｕａｌ Ｐｒｏｐｅｒｔｙ Ｃｏｒｐｏｒａｔｉｏｎ ｏｆ Ａｍｅｒｉｃａ Pet health examination apparatus, pet health examination method and program

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