NZ615942B2 - Apparatus and method for using infrared thermography and behaviour information for identification of biologically important states in animals - Google Patents
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Classifications
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; 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
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2503/00—Evaluating a particular growth phase or type of persons or animals
- A61B2503/40—Animals
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
- A61B5/002—Monitoring the patient using a local or closed circuit, e.g. in a room or building
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
- A61B5/015—By temperature mapping of body part
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
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- 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
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
Disclosed is an apparatus for identifying biological states in an animal. The apparatus comprises means for identifying the animal (6) to obtain animal identification information, at least one infrared thermography camera (8) for photographing the animal to obtain infrared thermography information and fidget information about the animal and a processor (9) for receiving and processing the animal identification information. The infrared thermography information and the fidget information can be used to determine a biological state of the animal. The biological state is a predictor of onset of disease, growth states and reproductive states in the animal. nd fidget information about the animal and a processor (9) for receiving and processing the animal identification information. The infrared thermography information and the fidget information can be used to determine a biological state of the animal. The biological state is a predictor of onset of disease, growth states and reproductive states in the animal.
Description
TITLE: Apparatus and Method for using ed thermography and behaviour
information for fication of bioiogically important states in s
TECHNICAL FIELD
A non—invasive apparatus and method of fying biologically important
states in animals is provided. More specifically, an tus and method of
combining real-time, non-invasive, ethological behavioral information with
infrared scanning is provided for identifying agriculturally important states, such
as disease, growth, or reproductive states, in animals such as livestock.
BACKGROUND
Livestock often undergo significant exposure to transport and handling,
co—mingling, n and some time off feed and water. tively, these events
can impede the immune system and can result in a significant incidence of
disease. Such events can have considerable economic impact, for example, on
the agricultural industry both with respect to health treatment costs and animal
performance. Recent research has resulted in an increased understanding of the
importance of animal management factors, such as transport and ante—mortem
handling, in influencing both animal welfare and the food quality arising from
such animals. It is known that disease and stress can have a dramatic, negative
impact on animal welfare parameters and performance as well as meat quality
and yield and hence the economics of the animal industries.
As would be known to a person of skill in the art, a number of diseases,
such as Bovine Viral Diarrhea (BVD) type 1 and 2, infectious Bovine
Rhinotracheitis (lBR), Corona Virus, Bovine Para—influenza (PIB) and Bovine
Respiratory Syncytial Virus (BRSV), can impact iivestock populations. One such
disease complex, known as bovine respiratory disease (BRD), refers to a host of
complex diseases, and is lly used to refer to an animal ying an
undifferentiated fever and/or other clinical signs (eg. respiratory distress,
lethargy, and loss of appetite).
The presence of BRD in intensively raised calves has caused a
dependence on antibiotic treatments (including mass treatments), which, in turn,
has led to a n for the promotion of antibiotic resistant microbes. indeed,
the ability to treat BRD in cattle is becoming more difficult due to the emergence
of resistant microbes (for e.g., pneumonia), or new zoontic diseases in multiple
sourced, co—mingled cattle. rmore, recent reports have shown substantiai
contamination of s and meat products with antibiotic resistant strains of
bacteria such as E. coli.
The effectiveness of treating livestock diseases, such as BRD, can
depend upon the ability to detect, diagnose and treat affected s early. The
ability to achieve early detection will depend upon the information available and
on the reliability of that information. For instance, when used alone, traditional
al signs of e provide poor diagnostic results because clinical
symptoms often occur late into the course of the illness. Further, many diagnostic
techniques, such as the use of acute phase proteins or hematology assessment,
require the capture and ve, in vivo collection of biological samples, which
result in the significant cost of analysis and time. The requirement of the capture
(and therefore restraint) of the animal in order to collect a ical sample
causes stress, and the process itself is therefore introducing inaccuracies into the
data collected.
Recent research has focused on alternative approaches to non—invasively
determine the early fication and onset of disease in cattle. One such
approach is infrared thermography, which can be used as a means of detecting
the dissipation of heat in s. Thermography operates on the principle that
infrared radiation can be utilized to observe radiated heat loss and to provide an
early indicator of fever because up to ~60°/o of the heat loss from an animal can
occur in ed ranges. The technology has been demonstrated to be effective
in non—invasive identification of transport and other environmental stressors that
can alter an animal’s heat loss.
r approach to non—invasive disease analysis is the commonly used
“pen-checking” approach, wherein the animal caregiver observes the animal on a
daily basis to detect any al behavioural patterns, or clinical signs of illness
(e.g. decrease in eating clue to loss of appetite; see Table i for further examples
of behavioural arks). Although non-invasive, pen-checking is highly
inaccurate particularly during the early stages of disease onset and leads to
false—positive and false—negative results because it depends upon the skill and
observations of the ver. Further, it is known that animals often do not
display overt signs of illness (that would be detectable to a ver) until later in
1O the progression of the disease, resulting in an increased risk of infection of
healthy animals in a population, particularly where the animals share a source of
food and water.
TABLE 1 — PRIOR ART
Commonly—used clinical scores used in the bovine respiratory disease (BRD)
early disease ion
, Clinical Score Assessment
0 4 5
Disposition, Moving Slightly Moderate Hanging Prostrate, Death
Lethargy around depressed lethargy back from ent
and well with appearance, and the rest of or abnormal
Behaviour normal Holds head depression, the herd, posture,
e, slightly Holds head Recumbent Not
Content, lower than low, Droopy or interested in
No normal, Mild ears, Slow abnormal surroundings,
signs of anorexia to rise, posture, Weakness
lethargy Stiff Largely
movements, depressed
Anorectic
Respiratory Normal Very fine Fine crackle Medium Course Marked
Insult breath crackle and/or crackle crackles respiratory
sounds and/or moderate and/or and/or distress
moderate nasal moderate severe and/or lung
cough discharge to severe discharge idation
and viscous with
moderate nasal atory
cough discharge distress and
with cough obtunded
lung sounds
Digestive E No Mild or Moderate Moderate Severe Severe
Insult E insult, slight diarrhea to severe diarrhea, and diarrhea and
1 Normal ea with 10% diarrhea E less than 1 not eating, i
l ‘
eating with slight dehydration with 10% or [ 10% of not drinking
; and dehydration and less of feed t normal feed and
l drinking and reduced reduced intake and ' intake 1 ated
1 eating feed intake more than
i 10% i
1 dehydration l
It is also known that the identification of non—disease states in animals is
important to the agricultural industry as well as to 200 and wildlife y
settings. There are many biological events in an animal’s life that influence a
plethora of biometric measurements and characteristics expressed. Some of
these events are normal biological functions an animal will display such as when
they adapt to a ng environmental ature, a changing growth period
or a changing endocrine event ing puberty or estrus. Other events are less
common and will include the onset of a disease state. In either disease or non~
1O disease states the animal will be ered to be in a biologically important,
non-steady state during these periods. These biologically important states may
have, for example, agriculturally important consequences and implications.
Growth ency in animals is often defined as the gain in a particular
tissue type such as muscle or milk compared to the input of resources such as
feed and water. ln addition to disease states, growth efficiency is an important
attribute in animal agriculture as competition for limited resources increases.
However, measuring growth efficiency has always been a challenge. One of the
more accurate methods to monitor growth ency is to use indirect metry
which measures exactly the amount of oxygen and energy used by an animal for
a given increase in gain of a specific tissue while noting that the metabolism will
also give off heat (Kleiber, M. 1961. The fire of life — an introduction to animal
energetics. John Wiley & Sons, inc). Alternatively, growth efficiency can be
monitored by measuring the actual feed consumed by an animal and the growth
that resulted or measuring the so called gain to feed ratio (Kleiber, M. 1961. The
fire of life - an introduction to animal energetics. John Wiley & Sons, Inc).
A more recent approach to monitoring growth efficiency has been to
monitor the so called residual feed intake (RF!) which fundamentally is a
comparison of the ed feed to gain against a known estimate for feed to
gain based on scientifically accepted formulas (Basarab et ai. 2003, 2007 see
below) while a
. However, this later method, reasonably accurate, requires
lengthy seventy days or more feed monitoring period which is both expensive
and impractical.
It is also known that the identification of uctive states in animals is
important to biology in general and to the agricultural industry specifically. For
example, reproductive states such as onset of puberty and estrus are important
to identify for the purposes of reproductive efficiency, and ore agricultural
efficiency. it is known in the art that the onset of puberty and estrus are
characterised by behavioural estrus which includes an increased ssness of
the animal.
There is therefore a need for vasive, early and te means of
identifying biologically important states in animals. Furthermore, there is a need
for a non-invasive detection means that are capable of identifying diseased
animals, even in populations where there is a low prevalence of the disease.
§QMMABX
The present apparatus and method provides for real-time automated, non—
invasive infrared thermography information of an animal to be used for both
thermal and oural measurement, thereby providing an r and more
accurate tor of onset of disease, growth states, or reproductive states in
that animal. More ically, the present system and method provide for the use
of thermal images (taken, for example, at a water station) to obtain both
temperature and behavioural information about one or more animals at a time,
and to utilize that information to determine the health, growth, or reproductive
state of the animal. The combination of thermal biometric data, such as radio
frequency identification ed thermography, and behavioural biometric
information, such as behavioural fidgets can be used to detect early-onset of these
biological steady and non-steady states in animals.
Broadly speaking, an apparatus for identifying important biological states in an
animal is provided, the apparatus comprising: an enclosure for receiving the animal
therein; means for animal identification mounted on the enclosure and connected to a
reader for identifying when an animal is received into the enclosure; at least one
infrared thermography camera mounted on the enclosure for photographing the animal
to obtain infrared thermography and behavioural information from the animal; and a
processor in communication with the reader and camera for receiving and processing
information from the camera and the reader; wherein the information processed by the
processor identifies important biological states in the animal.
Broadly speaking, a method of fying ant biological states in an
animal is provided, the method comprising: providing an enclosure receiving the
animal therein; receiving an animal within the enclosure; identifying the animal;
raphing the animal to obtain infrared thermography images and behavioural
information from the animal; processing the infrared thermography images and
behavioural information; and identifying ant ical states in the animal as a
result of processing the ed thermography images and behavioural ation.
Definitions of specific embodiments of the invention as claimed herein follow.
According to a first embodiment of the ion, there is provided an tus
for identifying biological states in an animal, the apparatus comprising:
means for identifying the animal to obtain animal identification information;
at least one infrared thermography camera for photographing the animal to
obtain infrared graphy information and fidget information about the animal; and
a processor for receiving and processing the animal identification information,
the infrared thermography information and the fidget information to ine a
biological state of the animal, wherein the biological state is a predictor of onset of
e, growth states and reproductive states in the animal.
According to a second embodiment of the invention, there is provided a method
of fying biological states in an animal, the method comprising:
identifying the animal;
photographing the animal to obtain infrared thermography images and fidget
information from the animal; and
processing the infrared thermography images and the fidget ation to
identify a biological state of the animal identified, wherein the ical state is a
predictor of onset of disease, growth states and reproductive states in the animal.
All documents and references referred to herein are incorporated by reference
in their entirety.
FIGURES
Figure 1 depicts an embodiment of an apparatus for combining real-time, non-invasive
gical behavioral information with infrared scanning for fying biologically
important states;
Figure 2 shows a schematic diagram of the embodiment of Figure 1, image as
published in Schaefer et al. 2011. Research in Veterinary Science. In Press;
[Text continued on page 7]
Figure 3 shows a graphical representation of our data for calves suffering
from BRD, wherein the ill (sick) calves “fidget” more per drinking bout ed
with healthy (not sick) calves;
Figure 4 shows a graphical representation of behaviour data for calves suffering
from BRD, wherein the ill (sick) caives “fidget” more overall than healthy (not
sick) calves;
Figure 5 shows a graphical representation of infrared thermography data vs time
of true positive (ill) and true negative (healthy) calves with BRD;
Figure 6 shows a graphical representation of infrared thermography data d
against time for true positive (ill) and true negative (healthy) calves with BRD;
Figure 7 shows a graphical representation of a ison of the infrared
thermal values in a True Negative (TN) healthy animal and a True Positive (TP)
ill animal for ison. Data shows the ed thermal value (y axis) vs the
day of study (x axis).
DESCRIPTiON OF MENTS
An apparatus and method of eariy detection of biologically important
states in animals, such as livestock, is described. In some embodiments, the
biologically important states can be agriculturally important states.
More specifically, an apparatus and method of combining thermal and
behavioural biometric information for the early identification of disease, growth
efficiency, puberty, or estrus is provided. infrared thermography (lRT) and
ethological benchmarks may be combined to provide an early and automated
identification system. While the present disclosure generally s to beef
cattle, it would be understood by one skilled in the art that the apparatus and
methods provided herein may be utilized to detect disease in any , such as
livestock species, including, but not limited to dairy cattle, pigs, and poultry.
In the present apparatus and method, automated thermal and ethological
data may be ted simultaneously using at feast one lRT camera and
software system. Thermal data can be used in conjunction with predictive or
diagnostic infrared values, alongside ethoiogical (behavioural) predictors termed
"fidgets" (Le. the "fidget factor”), wherein both the ed values and fidgets are
both determined from the lRT thermal camera image data.
Infrared Thermography ation
An embodiment of the present scanning apparatus is shown in Figure 1.
Figure 1 depicts a an embodiment of an automated, radio—frequency
identification—driven , multi-cait infrared scanning apparatus which can be
attached to a water trough, and can comprise a data storage unit (A), camera
housing (B) and a water system with an RFlD antenna (C).
Having regard to Figure 2, the automated scanning apparatus can
comprise:
0 An enclosure, for receiving the animal therein, n the enclosure can be,
for example, a water or food station. In some embodiments, the enclosure
can be fenced in, in other embodiments, the enclosure could simply be a
pasture area. As would be understood by one skilled in the art, an ure
can comprise any area or structure which accomplishes the functions
described herein.
In the present embodiment, a water station was designed having two side
panels (1), surrounding a commercially-available, two—water bowl float design
(2) from Ritchie water systems (Ritchie Cattle Fountains, Conrad lA, USA),
and optionally ted by a partition there between. The present ure
may allow for access to the water station from two or more directions. it
should be noted that a water station was utilized in the present embodiment
because during illness it is known that s cease , due to loss of
appetite, before they cease drinking. lt shouid also be noted that any
enclosure, or other means of reducing overall movement of an animal, in a
—free way, such that an image could be taken of the animal, could be
utilized and is contemplated;
Extension panels (3) can be piaced on each side of the water bowls to
“centre” or frame the position animal’s head, and to help keep the animal’s
head at the proper focal distance. A panel (1) on one side of the water bowls
may be modified to facilitate a window (4) in order to View the animal while at
the water station. The window may measure, for example, approximately 30
cm square;
At least two in-phase loop antennae (5), for receiving information from the
’s radio~frequency identification tags (RFID), or other such animal
identification means, as applicable, may be mounted in the panels (1)
adjacent to (or near) the water bowls (2), and ted to an Allfex PNL-
OEM-MODLE-3 RFID control module or “reader” (6) (Allflex ElD system,
Allflex Canada lnc. St—Hyacinthe, P.Q.).;
At least one infrared thermography camera (8). The camera (8) may, for
example, be capable of obtaining at least 1 — 60 images/second such as a
FLIR 860 broadband camera (FLlR Comp, Boston, MA), which may be
rotably mounted adjacent to or near the windows (4). Means for electronically
rotating the camera (8), such as a geared—head motor, may be connected to
the camera, for powering the rotation of the camera. The camera may be
used to obtain radiated temperatures around, for example, the l area
(eye plus one centimeter nding the eye) of animals. The orbital eye
area was chosen in the present system because it is known to provide an
accurate peripheral temperature reading, thereby providing a measurement
that is sensitive to both stress and disease onset. Although the thermal orbital
(eye) is described herein, it is understood that any area on the animal that
provides an accurate and adequate peripheral thermal g of the animal’s
temperature may be used; and
A l system or processor (9), for receiving and processing information
from the camera (8) and the RFID antenna/reader (5, 6). For ce, the
processor may be mmed to control the camera positioning, acquire the
infrared image, perform the anaiysis of the image data, and to store the
acquired information on a database. The information may be collected and
received tically upon the animal entering the enclosure, and the
processor may collect the ation via wireless transmission, such that
information may be monitored remotely. ment integration, and the
re and software used in such a thermal station was designed and
developed, in part, at the Lacombe Research Centre, Lacombe, a,
Canada.
In one ment, an optional electromagnetic shielding (7) may be
exposed to the g pen on the side of the panels (1) to prevent the improper
reading of RFiD tags on s that are not within the enclosure.
in operation, the present apparatus and method provide that when an
animal enters the enclosure, the RFiD antenna system (5, 6) can receive the
identification of the animal from the RFlD tags, and can signal control system (9)
to rotate the camera (8), if necessary, in the direction of the animal, and to initiate
capturing images of the animal’s head when it becomes visible through the
window (4) in the panel (1). in one embodiment, the camera/motor assembly (8),
can be enclosed under a protective cover, and can be located medially between
the two viewing windows (4) at a distance that provided a field of view to cover
most head positions of an animal.
it is understood that mounting the infrared camera on a motor capable of
rotating to at least two different scan windows (4) as signalled by the RFiD
reader can provide the capacity to obtain information from at least two animals at
one time. For instance, the system may be ed to accommodate a second
enclosure/thermography station ed parallel to the first station with the
camera located centrally between the two ns, thereby at least doubling the
animal handling capabilities of the system.
The present apparatus and design may enable known methods of correct
thermography ques, namely, a fixed focal length and angle with a near—still
image, thereby providing accurate thermal data collection. The system can
further provide non-invasive means of obtaining both thermal and behavioural
(discussed below) biometric information without the need to restrict or capture
the animals. it is understood that any similar system capable of ing t
thermography information, without necessitating capture and restraint of the
, is contemplated.
Ethologica/ (Behavioural) Information
The present apparatus and method can also provide for the use of the lRT
information obtained from animals as a measurement of behavioural prediction of
disease onset or other biologically important states in animals. For instance, the
’10 thermal images taken at the water station, as herein described, can be further
utilized to obtain behavioural information about the animal, thereby providing
means for combining the thermal (temperature) biometric information with
behavioural biometric information, to provide earlier and more accurate disease
ion and state identification in the animal.
Each tamped image taken by the automated lRT system can be
classified as a behavioural “event”. For example, movement of the animal at the
water station can result in the camera (8) having to reset the thermal contrasts
between and among the lRT pixeis, thereby automatically causing a new image
to be taken and time—stamped and the postural adjustment or “fidget” of the
animal to be recorded. As such, depending on how much fidgeting the animal
does at the water station, more or fewer images may be taken of one animal as
compared to r. The images can then be used to calculate behavioural
factors such as, for e, the total time the animal spent drinking, the number
of drinking bouts, the length of each drinking bout, the average number of
drinking bouts per day, and the number of “events”(i.e. fidgets) recorded (eg. the
number of thermal scans taken during a single drinking bout). The ation
can then be used, in conjunction with the thermal information to determine “true-
positive” (i.e. sick) and egative (i.e. non—sick) disease. ingly, an
ethological tor of disease, referred to herein as a “Fidget Factor” can
e an additional benchmark for non-invasive disease detection and state
identification.
It should be noted that different animals may fidget more or less than
others in the population, and that fidgeting can r be altered due to an
illness, growth state, or uctive state. it should be known that the processor
can be capable of utilizing all of the infrared thermography images of each animal
(eg. orbital (ocular), mouth, nose, ear, shoulder, and body images were all
included in the behaviour data set) in order to process the fidget our.
Thus, the t apparatus and method can provide for the use of
infrared thermography images to be used to detect the peripheral temperature of
the animal as well as the behavioural activity of the same animal, thereby
providing earlier and more accurate disease ion and state identification. it
is understood that the present apparatus and method can provide for two distinct
sets of data or information to be generated in parallel or series. it would also be
apparent that these two biometric data sets consisting of both infrared and fidget
information can be used in a number of statistical assessment procedures
including multiple regression and correlation, ranking and prediction indexes to
enable the more accurate identification of true—positive and true negative
Such detection and identification means are likely to be applicable in a
variety of settings, including, for example, in bio-security and bio—surveillance
circumstances.
The ing examples are provided to aid the understanding of the
present disclosure, the true scope of which is set forth in the claims. it is
understood that modifications can be made in the system and methods set forth
without departing from the spirit or scope of the same, as defined herein.
EXAMPLES
Example 1
Animals
In this example, forty (40) multiple sourced, co-mingied and transported
commercial, auctioned er calves, which had been exposed to viral and
bacterial infection for respiratory viruses including BVD, Pl3, lBR, Corona and
BRSV, and forty (40) retained possession calves were used. The calves were
weighed, monitored for core and orbital thermal properties, blood sampled and
placed onto conventional cereal grain silage with access to shelter and clean
water.
Twenty (20) of these calves were obtained from the BVD and lBR
antibody free herd at the Animal Diseases Research lnstitute at Lethbridge,
Alberta, Canada. These calves were Angus x Hereford crosses and had been
weaned approximately one week prior to transport to e, Alberta, Canada.
The calves were an average of 550 tbs, were raised on native grass pasture and
had been given a de-worming medication two weeks prior to weaning.
The calves were transported on a tional ected horse trailer.
On arrival at Lacombe Research Station, a ort time of approximately 5 h,
the calves were co—mingled with 20 muitiple sourced and commingled auctioned
calves. All calves were monitored continuously for 3 weeks. All calves were
observed to have continuous contact with each other by touching noses as well
as sharing the same water trough, salt lick, feed bunks and g.
Infrared Thermography
Automatic ed thermography images (lRT) were collected using a
portable lnframetrics broadband S60 ed scanner (FLIR® lnframetrics S60,
Boston, MA, USA). All images were taken of animals as they entered the
automated infrared ng station at the common water station located in the
pen (see Figures 1A, B and C). in the present experiment, images specific to the
orbital area of each calf were used in collecting thermal data.
All calves were thus monitored for average daily maximum temperatures
(including change in temperature) and for the mean ratio values (MR). The mean
ratio which was calculated as the average of daily radiated maximum
temperature for a given animal divided by the average daily maximum value for
the group of calves. The thermal data was verified by ison to serology
and virology blood parameters.
Ethology — “Fidgets”
Using the time stamp (which provides the hour, minute, second and date
of the image obtained via a chronometer or clock), for each image taken by the
automated lRT system, each image was defined as a behavioural “event” which
triggered the image to be recorded. During the present example, a 4—minute
interval between ng events (known as the bout criterion interval; BCl) was
used to determine the termination or conclusion of one ng bout and the start
of another. Accordingly, the same infrared images were used in the analysis of
both thermal and ethological data sets, albeit, the ethological data set ed
“all images”, while the thermography data set included “orbital images” only.
Results
in the forty (40) multiple—sourced and co—mingled commercial calves, there
were 10 s out of the 40 fied as “ill” (true—positive animals). This
identification was made by virtue of displaying clinical scores of 3 or higher (see
Table 2), and by orbital infrared values of 351°C compared to healthy animals
(or true negative animals) having a ature of 348°C. Clinical illness was
verified by statistically significant haematology values. in addition, the ill calves
demonstrated an approximately 40% increase in the blood cortisol values
(deviating from an average of 52 nmol/L in healthy calves to over 70 nmol/L in ill
calves). Results demonstrate that haematology data for the forty (40) l
retained possession calves displayed normal haematology values.
It is known that four to six days prior to the display of clinical signs and lab
verification of illness, infrared orbitai scans can be 71% efficient (combined true
ve and true negative ) at early fying ill s compared to
either clinical scores alone (55% efficiency) or rectal temperatures alone (59%
efficiency). This is supported in the present example where the vasive
collection of orbital infrared atures, alone, was 73% efficient at identifying
ill animals 27 days before clinical symptoms able by “pen-checking”.
Further analysis of all thermal images recorded through the automated
lRT system, and based on a 4 minute drinking bout interval, results show that
true—positive or “sick” animals have a tendency to “fidget” more than “non-sick”,
true-negative animals (Figures 4 and 5). Sick (true—positive) animals were found
to have a greater number of lRT images taken during each ng bout. This is
1O despite overall ng behaviour, which includes the drinking duration and the
number of drinking bouts, being the same in sick and healthy (true—negative)
animals. Based on the number of average fidgets (events) per bout in sick and
healthy animals, a behavioural predictor or “Fidget Factor” of 4 fidgets per
drinking bout was determined to be a possible indicator of disease.
False-negative and false—positive animals were not included in this data
set, as analysis focused on true-sick and true-healthy animals only.
TABLE 2
Means iSD of haematology values for the forty multiple d co—mingled
calves. White blood cells (WBC) and all other ential cells = cells X 10 Red
blood cells (RBC) = cells X 10 12, hgb = g/L
WBC Neut Lymph Mono 1 Eosyn Baso RBC HgB Hct% N/L
j L
Healthy 9.12 1.22 5.56 0.98 I 1.28 0.07 _8.83 11.98 35.4 0.24
SD 1.65 0.83 1.15 0.51 ‘ 0.87 0.07 0.94 1.08 3.8 0.22
(n=30)
HI 1239 3.33 5.24 2.11 2.14 I 0.13 7.94 11.24 34.4 082*
tBRDl .
SD 3.78 3.23 2.26 1.62 1.87 0.01 1.04 0.91 3.6 0.91
(n=10)
P value 0.01 0.01 0.55 0.01 0.01 0.02 0.01 0.04 0.41 0.01
Statistical separation based on least squares analysis (two tailed t—test). * N/L
ratio for ill animals was either very high or very low.
Haematology, Endocrine and Serology Data
With respect to laboratory analysis, salivary and serum cortisol was
ed using a known enzymatic assay from ted samples. Hematology
analysis and differential counts were conducted on a Ceil—Dyne model 3700
hematology analyser t LabsTM, Mississauga, Ontario). Serology
assessment was conducted by Prairie Diagnostic ServicesTM (Saskatoon
Saskatchewan) and assessment was carried out for the BRD s, Bovine
Viral Diarrhea (BVD) type 1 and 2, as well as infectious Bovine Rhinotracheitis
(iBR) via serum neutralization tests.
Additional assessment for Corona virus, Bovine Para-influenza (PIS) and
Bovine Respiratory Syncytial Virus (BRSV) were conducted by ELlSA using
methods known to one skilled in the art. Antibody trations (units) for BVD,
iBR, BRSV, Pi3 and Corona were obtained as follows:
((mean net optical density of sample — mean net optical density of
fetal bovine serum) / (mean net optical density of positive standard
—- mean net optical density of fetal bovine serum)) X 100
The g of antibody titre scores was as follows: for BVD and iBR 0—2 =
negative, 3-1321 = suspicious, 14-40:1 = low, 41~80:1 2 moderate, >80:1 = high.
For BRSV, PB and Corona <10 = negative, 11-13 = suspicious, 14-50 3 low,
51:100 = moderate, > 100 = high.
Example 2
Animals
in this example, r trials were conducted on 100 le sourced, co—
mingied, transported and weaned commercial calves. These calves were
procured from two primary sources with 17 from the ADRi herd at idge,
Alberta, Canada and 83 procured from commercial auction facilities. These
calves were themselves purchased through auction from two separate locations.
The calves were brought to the Lacombe Research Centre (LRC) Beef Research
unit, weighed, blood sampled, core temperatures recorded and then placed into
clean receiver pens with wood shavings bedding and free access to water and
cereal grain silage.
Methods
Continuous, tic ed and oural data was captured on all
animals for a three—week period (except when power supply or solar loading
glitches caused a failure in the system), as described in Example 1.
Results
In the present example, thirty seven animals were identified as “at risk” of
BRD by the “pen checking" technique. Of these animals, 24 were subsequently
verified by objective lab data as being true positive (TP) and 13 were identified as
false positive. Hence, the incidence of false negatives and positives is again
comparatively high when pen checking ciinical scores alone are used to identify
BRD.
in the calves identified as true positive for BRD, the RT temperatures
were on average 367°C compared to the true negative animals at the same time
at 356°C (P<0.05). Figures 6 and 7 demonstrate the relationship between true-
positive and true—negative animals for calves with the most complete clinical
score and infrared data, and show that the infrared scores detected animals that
were verified to display BRD several days before the ciinicai “pen checking”
scores.
Using the “Fidget Factor” tor generated and d in e 1 to
detect sick animals (an average of 4 fidgets per drinking bout over a 24 hour
period), no significant differences were found in the present Example 2. Some
possible expianations may e either the need to alter the drinking bout
interval or due to the high rate of false negatives in caif group. The present
example also experienced higher than usual electrical glitches, which may have
made the gical data set less robust. t research on adjusting the BC!
and the fidget predictor value is required and continues.
Example 3
Animals
in the present Example 3, investigations were d out on a total of
sixty—five (65) receiver calves. These calves consisted of 54 retained possession,
low disease incidence animals from the Lacombe Research Centre (LRC) Beef
Research fall calving herd and a further eleven (11) calves from the high-health,
closed herd located at the Animal Disease Research Institute (ADRl) at
Lethbridge, Alberta, Canada. The ADRI calves were unique in that they displayed
no antibodies to either BVD or IBR virus and therefore would be susceptible to
BRD-causing viruses. Calves from both herds were commercial crossbred cattle
of British X Continental breed. All calves had been weaned and transported to
the LRC beef unit prior to the study. The animals averaged 220 kg at the start of
the study.
Methods
To simulate l marketing conditions all calves were co—mingled and
transported to a local auction facility within one hour of the LRC beef unit. The
calves were then offloaded and kept in pens overnight without feed or water. The
cattle were loaded onto a cial r and returned to Lacombe, Alberta,
Canada the following day for processing.
On arrival at the LRC beef unit the calves were weighed, blood sampled,
core temperature recorded, clinicaily scored and then placed into receiver pens
containing straw bedding and with free access to water and a cereal grain silage.
The calves were subsequently “pen checked” daily for signs of s and lRT
values were recorded continuously using the present system and method as
defined herein. Behavioural “events" and/or s were monitored and an
alternative ”Fidget Factor” for disease was determined based on various intervals
between drinking bouts (eg. 3 or 5 minutes, rather than 4 minutes utilized in
Example 1). in addition, preliminary live observations were conducted to
determine which specific faciai nts ed IRT images to be ed
as events (i.e. to define the specific mechanics of a “fidget”). it is contemplated
that such ations could be further enhanced by classifying and defining the
actual fidget our by, for exampie, video analysis.
logy values for all animals were assessed on a CellDynTM
hematology analyser. Clinical scores were assessed using a point system (see
Figure 2) and core or rectai temperatures were recorded using a chute side
digital temperature probe.
Results
Based on the pen checking information, two of the sixty-five calves were
considered to be at risk of BRD. Taken together with data available at the time of
1O processing (core temperatures of 40°C or higher) four of the sixty—five animals
would have been diagnosed as true positive for BRD. However, two of these
animals were subsequently determined to be false positive by hematology
is (white blood cell numbers and neutrophile/lymphocyte ratios), core
temperature and clinical score. The use of the same analysis would also have
classed eleven (11) of the calves as true positive (TP) and 20 as true negative
(TN) with the remaining 34 as intermediate health. Three of the true ve (TP)
animals were from the ADRl BVD and IBR antibody free herd and 8 from the
LRC herd. The average values for these animals both at the start and end of the
assessment period are shown in Table 3.
TABLE 3
Average Health Values :t SD for the e 3 calves
Core Temp C Clinical Score WBC X N/L ratios
1000/pl
March 13 ave 102.8 3.18 12 0.273
so 0.65 1.15 12 0.19
April 1 ave 102.6 2.95 9 0.129
SD 0.98 1.75 2 0.101
Infrared Values
In excess of 20,000 thermal data points were collected on the 65 calves
over the two week ment period using the automated thermal station
located at the cattle water system. The average radiated temperature value for all
calves during this period was lly between 33—35°C. The l radiated
thermal value for the true-negative caives for the entire observation period was
34.7“(3 :t 057°C and the value for true—positive calves 35.4OC C (see
Figure 9).
Live behavioural observations were also med in this Example 3 to
increase the understanding of why true—positive calves generated greater
numbers of IRT images (when all images were included) compared with true-
negative calves. These obsen/ations suggest that shifts in posture or stance, eye
, leg nts, tongue , and ear twitches may cause the lRT pixels
to recalculate contrasts, thereby determining it is time to take a new image.
With respect to the data per se, the calves used in the present study were
comparatively low stress and expressed a low incidence of BRD of approximately
17%. Of interest however, was the observation that conventional industry
standard practice of using pen checking as the primary tool for fying BRD
would have identified only two animals and even with the addition of core
temperature data at the time of processing only four animals were identified as at
risk of BRD and of those, two were subsequently identified as being false positive
identifications. in other words, once again, one of the primary challenges with
conventional pen checking or clinical score methods for detecting BRD is with the
incidence of false negatives.
Example 4
ol Data
Salivary and serum cortisol analysis were performed for all animals in the
foregoing Examples 1 — 3 (see Table 4). An ELISA assay system, developed at
Lacombe Research Centre, was utilized.
In all three Example data sets the cattle were identified as true positive
(TP) for bovine respiratory disease (BRD) or true negative (TN) using the
hematology, core temperature and clinical score criteria fied in each of the
foregoing method sections. Least squares analysis (t—tests) for the cortisol data
has also been performed.
Cortisol assays were conducted on blood and saliva samples collected
when the cattle arrived at the Lacombe Beef Research Centre and when an
animal was identified as t for BRD. Cortisol data displayed erable
ion both within and between the studies performed in Examples 1 — 3. Some
of this variation is likely due to variation in animal populations, procurement
ures and animal history among the groups. There is also likely to be some
variation in stress tibility across cattle groups from experiment to
experiment.
Table 4 represents overall averages for the animals, without correction for
the present data set for animals displaying health aberrations for non—BRD
reasons such as transport stress, mechanical insults such as ss or other
metabolic reasons such as ation. There were a few of these animals
identified and they could arguably cause some bias in the data set. eless,
cattle identified as TP for BRD also show a trend towards or an actual statistical
increase in cortisol values. Some animals observed also tended to fall into an
intermediate group for BRD identification. Again, as described in the methods
section, a true negative animal would have displayed a score value of O or 1 for
temperature values > 40°C, WBC counts of > 10 or <7 X 103/pL, an N/L ratio of
< 0.1 or > 0.8 and a clinical score of < 3. A true positive animal would display a
value of 3 or 4 of these criteria and the intermediate animals would display a
value of 2. As with the other laboratory criteria, these intermediate s also
tended to display an intermediate cortisol value (data not shown).
TABLE 4
Salivary and serum cortisol values in weaned and receiver calves identified as
true positive (TP) or true ve (TN) for BRD
Data Salivary P Value Serum P value
Year Cortisol Salivary Cortisol Serum
nmol/L Cortisol nmoi/L Cortisol
' 1
Mean Std. Mean Std. Dev
l Dev.
2007 TP 3.16 3.0 NSD 70.96 19.7 P: 0.1
1T—test
2tail, 0.05
2007 TN ‘ 2.95 3.2 52.4 34.9
2008 TP 5.82 2.86 P=0.01 139.7 87.8 P=0.1 1T,
0.5 2T
2008 TN 3.92 1.27 113.3 31.1
2009 TP 2.97 2.47 P=0.06 123.6 61.2 P=0.1 1T,
05. 2T
2009 TN 2.05 1.26 103.3 36.1
These results demonstrate that animals displaying BRD demonstrated a
higher infrared radiated temperature and a higher degree of variation associated
with that temperature. The cortisol data is also consistent with this g
g that BRD animals lly display a higher cortisol vaiue with greater
vanafion.
Example 5
Fidget Value and growth efficiency
As an alternative to the methods discussed in the Background section
above, ce has been ed that demonstrates the use of infrared
thermography to classify animals into more efficient and less efficient growth
categories (Schaefer, A. L., Basarab, J., Scott, S., Colyn, J., McCartney, D.,
McKinnon, J., Okine, E. and Tong, A. K. W. 2005. The relationship between
infrared thermography and residuai feed intake in cows. J Anim Sci 83(Suppl.
1):263). it is known that animais that are more efficient at growth can display a
lower heat loss to the environment. However, the components that make up or
account for this difference in efficiency and energy loss are less apparent. To this
end, the present apparatus and methods can be used to show that animal
behaviour or so called “fidgeting” can be partially responsible for this differential
energy use. As such, measuring these “fidgets” would have utility in
differentiating animals with different growth efficiency. This Example 5 is
provided as a non-limiting example of enting this ple.
Eight crossbred mature cows were used in the present example to test
whether a fidget ement also ranked with both a measure of growth
efficiency (Residual Feed intake, RFI) and a measure of energy loss (infrared
graphy). The cattle were fed a balanced alfalfa cube based diet which
met 1.25 times the maintenance nutritional requirement for these animals. The
cows were housed in outdoor pens with free access to fresh water and a straw
bedded area.
The relative growth efficiency for these animals, referred to as the residual
feed intake, had been previousiy determined using a feed bunk monitoring
system to record exact feed consumption and weight gain as described by
b et al (Basarab, J. A., McCartney, D., Okine, E. K. and Baron, V. S.
2007. Relationships between progeny al feed intake and dam productivity
traits. Canadian Journal of Animai Science 87(4):489—502: Basarab, J. A., Price,
M. A., Aalhus, J. L., Okine, E. K., Sneiling, W. M. and Lyle, K. L. 2003. al
feed intake and body composition in young growing cattle. Canadian Journal of
Animal Science 83(2):189—204).
For infrared and fidget measurements the cows were monitored
postprandial between a 24h feed . In other words the animals were off feed
during the time of monitoring. However, all animals had free access to a water
station and when the cows attended the water n they triggered an infrared
scanning system which inciuded a radio frequency identification tag (RFlD)
thereby recording both their station attendance frequency and their facial ed
3O characteristics.
The daily average values for the infrared scans for the half of the animals
with the lowest efficiency and the half of the animals with the highest value are
summarized and shown in Table 5 along with the known RFI feed efficiency
values for the cow group of 8 animals. The RFI values basically report what an
dual animal’s actual feed intake was (as measured by the bunk monitoring
systems) compared to what would be a predicted feed intake for that animal
based on her body weight and growth. For example, as explained by Basarab et
al ( 2003, 2007) an animal with an RFI value of 4 represents a cow that
ed 1 kg per day less feed than what would be expected and an RFI value
of +1 ents an animal that consumed 1 kg of feed more than what would be
predicted. Lower RF! values can represent more efficient cows. in addition as
shown in Table 5, the fidget values or number of thermal station triggering events
were also collected. This data is expressed as the total number of fidgets per
animal per day within four minute drinking bouts.
Using conventional ranking statistics (Spearman Ranking: Tuckman.
1978. Conducting Educational Research, Second Ed. Harcourt Brace Jovanovich
Inc. New York) the s in the present study displayed a significant (P<0.05)
rank order of thermal values against known RFl values. The animals with the
lowest thermal values also yed the lowest RFI values and the lowest
number of fidget events. This e demonstrates that a fidget value can have
y in identifying animals displaying different production (growth) efficiency.
TABLE 5
Comparative values for RH and IRT in two groups of mature cows
Category Mean RFI Mean lRT Mean Fidget Spearman
Value Value Value rank value of
0 C Total/day/4 fidget with RFl
min drinking
bout
Higher 4 .3 10.6 4 F5<O.O5
efficiency (low
RFI) four cows
Lower 0.41 13.2 9
ency
(high RFl) four
Cows
Example 6
Fidget value and estrus
it is known in the art that there is a link between restless behaviour and
estrus in animals.
Behavioural estrus indicators are the primary means in which producers
determine whether dairy cows are in estrus (ie have ovulated or are ready to
ovulate). Behavioural indicators of estrus include increased activity such as
ng events, pacing/walking, as well as l restless behaviour (eg. lying
1O down and standing up, walking, stepping, and shifting, but also includes many
other more subtle behaviours; Poilock, W. E. and Hurnick, L. F. 1979. Effect of
two confinement systems on estrus 436 detection and diestrus behaviour in dairy
cows. Can. J. Anim. Sci. 59: 799—803.; Walton, J. S. and King, G. J. 1986.
Indicators of estrus in Holstein cows housed in 468 tie stalls. J. Dairy Sci. 69:
973).
Cows housed in tree—stalls exhibit 4 times more activity and restless
behaviour during estrus (Kiddy, C. A. 1977. ion in al activity as an
indication of estrus in dairy 414 cows. J. Dairy Sci. 60: 235-243). Cows housed in
tie~stalls exhibit 2.75 times more activity and restiess behaviour during estrus
(when compared with cows not in behavioural estrus). Similar findings have been
reported when pedometers were used to e activity and ss behaviour
during estrus in free-stalls (Roelofs, J. B., van Eerdenburg, F. J. C. M, Soede, N.
M. and Kemp, B. 2005. 451 Pedometer readings for estrous detection and as
predictor for time of ovulation in dairy 452 cattle. Theriogenoiogy. 64: 16904703;
Roelofs, J., LOpez-Gatius, F., Hunter, R. H. F., van Eerdenburg, F. J. C. M. and
447 Hanzen, C. 2010. When is a cow in estrus? Clinical and practical s.
448 Theriogenology. 74: 327-344). While pedometers on cows continuously tie
stalled have been unable to detect behavioural estrus based upon walking
activity measures alone (Feiton, CA, Colazo, M.G., Ponce-Barajas, P., Bench,
C.J., and Ambrose, D.J. 2012. Dairy cows continuously—housed in tie—stalls failed
to manifest activity changes during . Can. J. Anim. Sci. (in press», the use
of a more subtle behavioural biometric of restless behaviour has the ability to
capture behavioural estrus even in confined cows.
e the fidget biometric, as described within the t
specification, can be an accurate and reliable measure of restless our
when an animal is standing in a confined space, the use of this type of fldget
measure also has the means of capturing restless behaviour exhibited during
estrus. As such, the apparatus and methods described herein can identify
reproductive states such as estrus.
Example 7
Further fidget data (EDOB/OQ sample)
included are calculations on a calf bovine respiratory disease (BRD) data
set referred to as ED08/09 (calves analysed in 2008 and 2009).
Briefly, a true positive (TP) animal was one that displayed 3 or 4 out of 4
for a high white blood cell count, a high neutrophile/Iymphocyte ratio, an elevated
clinical score and an ed core (rectal) temperature. These criteria are
defined in ations known in the art. By contrast, a true negative (TN) animal
was one that yed a score of either 0 or 1 out of 4.
Of the ED08/09 data set for 21 animals, 11 of the calves met a TP criteria
and 10 met a TN criteria. in other words the prevalence of BBB in this data set
was 52%. This result is very similar to the multiple sourced, commingled
orted and weaned calves studied eleswhere and is typical for calves of this
type in general.
The biometric data collected from the ED08/09 animals for predicting earty
disease onset included the absolute infrared value for the eye maximum
determination, the mean ratio of the individual calf eye m value compared
to the group mean maximum value, the so called MR value, and thirdly, the fidget
value for those animals calculated from the same infrared image data set. The
five minute s/bout/calf/day information was used. The data used was for the
day the animals were verified as TP or TN (so—cailed pull day) and the four days
prior to that time.
One approach to determine the relative contribution a given data set has
to an overall prediction or ranking of variables is to use a le regression
approach and also a discriminant analysis imes called stepwise
sion) or logistic regression analysis. Different statistical programs will use
different names, for example SAS uses discriminant analysis and MedCalcTM
uses the term logistic regression.
Using the ED081059 data, the value for correct TP vs, TN identification
using a single biometric measurement was between 57—68%. However,
ing all three biometric measurements raised the overall correct
identification of animals into disease class ( both TP and TN) to 83.5% . This
improvement in correct classification is significant and also offers the ability to
identify BRD before the pull day unlike prior art methods.
One method for ranking the relative importance of each biometric
measurement in a multi-regression model is to obtain the r value (correlation
value), square this value and ly by 100 to obtain the ve percentage
importance of a biometric measure or the proportion of the variance that a
particular ric value can account for. For example, with a situation like
calving difficulty in s (dystocia) it has been determined that the relative
ranking of the importance of factors would be as foilows; birth weight and pelvic
width of the heifer = 30%, heifer age 10%, calf birth weight = 7% and so on. With
the BRD model above the highest ranking (value for predicting BRD onset) for all
days was for the orbital absolute infrared vaiue at 33%, next was the fidget value
at 16% and the MR value at 10%.
The scope of the Claims should not be limited by the preferred
embodiments set forth in the examples, but should be given the broadest
retation consistent with the description as a whole.
Claims (17)
1. An apparatus for identifying biological states in an animal, the tus comprising: means for identifying the animal to obtain animal identification information; 5 at least one infrared thermography camera for photographing the animal to obtain infrared thermography information and fidget information about the animal; and a processor for receiving and processing the animal identification information, the infrared graphy information and the fidget information to determine a biological state of the animal, wherein the biological state is a predictor of onset of 10 disease, growth states and reproductive states in the animal.
2. The apparatus of claim 1, wherein the biological state is selected from the group consisting of a e state, a non-steady state growth period, the onset of puberty and the onset of estrus.
3. The apparatus of claim 1 or claim 2, r comprising an ure accessed 15 by the animal.
4. The apparatus of any one of claims 1 to 3, wherein the infrared graphy information and the fidget ation are obtained postprandial.
5. The apparatus of claim 4, wherein the postprandial period is between a 24 hour feed period. 20
6. The apparatus of any one of claims 1 to 5, wherein the means for identifying the animal comprises antennae for receiving radio-frequency identification (RFID) ation from the animal.
7. The apparatus of any one of claims 1 to 6, wherein the camera is capable of obtaining at least 1 – 60 images per . 25
8. The apparatus of any one of claims 1 to 7, wherein the processor receives the animal identification information, the infrared thermography information and the fidget information wirelessly.
9. The apparatus of any one of claims 1 to 8, wherein the apparatus is automated.
10. A method of identifying biological states in an animal, the method comprising: identifying the animal; photographing the animal to obtain infrared graphy images and fidget information from the animal; and 5 processing the infrared thermography images and the fidget information to identify a biological state of the animal identified, wherein the biological state is a predictor of onset of disease, growth states and reproductive states in the animal.
11. The method of claim 10, wherein the method can be performed during a postprandial period. 10
12. The method of claim 11, wherein the postprandial period is between a 24h feed
13. The method of claim 10, n the method is automated.
14. The method of any one of claims 10 to 13, wherein the images are obtained from or around the l area of the animal.
15 15. The method of any one of claims 10 to 14, wherein the biological states is selected from the group consisting of a disease state, a non-steady state growth period, the onset of puberty and the onset of estrus.
16. An apparatus as defined in claim 1 and as ntially described herein with reference to one or more of the accompanying figures. 20
17. A method as d in claim 10 and as substantially described herein with reference to one or more of the accompanying examples.
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US201161468492P | 2011-03-28 | 2011-03-28 | |
US61/468,492 | 2011-03-28 | ||
PCT/CA2012/000279 WO2012129657A1 (en) | 2011-03-28 | 2012-03-28 | Apparatus and method for using infrared thermography and behaviour information for identification of biologically important states in animals |
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