NZ615942A - Apparatus and method for using infrared thermography and behaviour information for identification of biologically important states in animals - Google Patents

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.

Description

TITLE: Apparatus and Method for using infrared thermography and behaviour ation for identification of bioiogically important states in animals TECHNICAL FIELD A non—invasive apparatus and method of identifying biologically important states in animals is provided. More specifically, an apparatus and method of combining real-time, vasive, 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 re to transport and handling, gling, auction and some time off feed and water. Collectively, 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 ltural industry both with respect to health ent costs and animal performance. Recent research has resulted in an increased understanding of the importance of animal management s, 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 atory Syncytial Virus (BRSV), can impact iivestock populations. One such disease complex, known as bovine respiratory disease (BRD), refers to a host of complex es, and is generally used to refer to an animal displaying an erentiated 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 concern for the promotion of antibiotic resistant microbes. indeed, the ability to treat BRD in cattle is becoming more difficult due to the nce of resistant microbes (for e.g., pneumonia), or new zoontic diseases in multiple sourced, co—mingled cattle. Furthermore, recent reports have shown substantiai contamination of carcass and meat products with antibiotic ant 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 animals early. The y to achieve early detection will depend upon the information available and on the reliability of that information. For ce, when used alone, traditional clinical signs of disease 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 ment, e the capture and invasive, in vivo collection of biological s, 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 biological sample causes stress, and the process itself is therefore ucing inaccuracies into the data ted.

Recent research has focused on alternative approaches to non—invasively determine the early identification and onset of disease in . One such approach is infrared thermography, which can be used as a means of detecting the ation of heat in animals. Thermography operates on the principle that infrared radiation can be utilized to observe ed 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 infrared 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.

Another approach to non—invasive disease analysis is the ly used “pen-checking” approach, n the animal caregiver observes the animal on a daily basis to detect any abnormal behavioural patterns, or clinical signs of illness (e.g. decrease in eating clue to loss of appetite; see Table i for r 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 caregiver. Further, it is known that animals often do not display overt signs of illness (that would be able to a caregiver) 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 detection , Clinical Score Assessment 0 4 5 Disposition, Moving Slightly Moderate Hanging Prostrate, Death Lethargy around depressed lethargy back from Recumbent and well with appearance, and the rest of or abnormal Behaviour normal Holds head depression, the herd, posture, posture, slightly Holds head Recumbent Not Content, lower than low, Droopy or interested in No normal, Mild ears, Slow al ndings, signs of ia to rise, posture, Weakness lethargy Stiff Largely movements, depressed Anorectic Respiratory Normal Very fine Fine crackle Medium Course Marked Insult breath e and/or crackle crackles atory sounds and/or moderate and/or and/or distress moderate nasal moderate severe and/or lung cough discharge to severe discharge consolidation and viscous with moderate nasal respiratory 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 diarrhea with 10% ea 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 ng and reduced reduced intake and ' intake 1 dehydrated 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 ry as well as to 200 and wildlife biology 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 y such as when they adapt to a changing environmental temperature, a changing growth period or a ng endocrine event including 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 considered to be in a biologically important, non-steady state during these periods. These biologically important states may have, for example, lturally important consequences and implications.

Growth efficiency in animals is often defined as the gain in a particular tissue type such as muscle or milk ed 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 ency has always been a challenge. One of the more accurate methods to monitor growth efficiency is to use indirect calorimetry 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 er, M. 1961. The fire of life — an introduction to animal energetics. John Wiley & Sons, inc). atively, 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 measured 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 te, requires lengthy seventy days or more feed ring period which is both expensive and impractical.

It is also known that the identification of reproductive 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 therefore agricultural efficiency. it is known in the art that the onset of puberty and estrus are characterised by oural estrus which includes an increased restlessness of the animal.

There is therefore a need for non—invasive, early and accurate 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 e.

§QMMABX The t apparatus and method provides for real-time automated, non— invasive infrared thermography information of an animal to be used for both thermal and behavioural measurement, thereby ing an earlier and more accurate tor of onset of disease, growth states, or uctive states in that animal. More specifically, 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 ine the health, growth, or uctive state of the animal. The combination of thermal biometric data, such as radio frequency identification infrared thermography, and behavioural biometric information, such as behavioural fidgets can be used to detect onset of these biological steady and non-steady states in animals. y speaking, an apparatus for fying important biological states in an animal is provided, the apparatus comprising: an enclosure for receiving the animal n; means for animal identification mounted on the enclosure and connected to a reader for identifying when an animal is received into the ure; at least one infrared thermography camera mounted on the enclosure for photographing the animal to obtain infrared graphy and behavioural information from the animal; and a processor in communication with the reader and camera for receiving and sing 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 identifying important biological states in an animal is provided, the method comprising: providing an enclosure receiving the animal n; receiving an animal within the enclosure; identifying the animal; photographing the animal to obtain infrared thermography images and behavioural information from the animal; processing the infrared thermography images and behavioural information; and identifying ant biological states in the animal as a result of processing the infrared thermography images and behavioural information. tions of ic embodiments of the invention as claimed herein follow.

According to a first embodiment of the invention, there is provided an apparatus 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 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 disease, growth states and reproductive states in the .

According to a second embodiment of the invention, there is provided a method of identifying biological states in an animal, the method comprising: identifying the animal; raphing the animal to obtain infrared thermography images and fidget information from the animal; and 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 e, growth states and reproductive states in the .

All documents and nces referred to herein are incorporated by reference in their entirety.

FIGURES Figure 1 depicts an embodiment of an apparatus for ing real-time, non-invasive ethological behavioral information with infrared scanning for identifying 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 behaviour data for calves suffering from BRD, wherein the ill (sick) calves “fidget” more per drinking bout compared with healthy (not sick) calves; Figure 4 shows a graphical representation of our data for calves suffering from BRD, n 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 plotted against time for true positive (ill) and true negative (healthy) calves with BRD; Figure 7 shows a graphical representation of a comparison of the infrared thermal values in a True ve (TN) healthy animal and a True Positive (TP) ill animal for ison. Data shows the radiated thermal value (y axis) vs the day of study (x axis).

DESCRIPTiON OF EMBODlMENTS An apparatus and method of eariy ion of biologically important states in animals, such as livestock, is described. In some embodiments, the biologically important states can be lturally important .

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 relates to beef cattle, it would be understood by one skilled in the art that the tus and methods provided herein may be utilized to detect disease in any animal, such as livestock species, including, but not limited to dairy cattle, pigs, and y.

In the present apparatus and method, automated thermal and ethological data may be collected simultaneously using at feast one lRT camera and software system. Thermal data can be used in ction with predictive or diagnostic ed 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 Information 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 (RFlD), multi-cait infrared scanning apparatus which can be attached to a water trough, and can comprise a data e 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, wherein 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 enclosure can comprise any area or structure which accomplishes the ons 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 enclosure 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 e during illness it is known that animals 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 animal’s 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 connected to an Allfex PNL- OEM-MODLE-3 RFID control module or r” (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 and camera (FLlR Comp, Boston, MA), which may be rotably mounted adjacent to or near the windows (4). Means for onically 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 orbital area (eye plus one centimeter surrounding the eye) of animals. The orbital eye area was chosen in the present system because it is known to provide an accurate peripheral ature reading, thereby providing a measurement that is sensitive to both stress and disease onset. Although the thermal l (eye) is described herein, it is understood that any area on the animal that es an accurate and te peripheral thermal reading of the animal’s temperature may be used; and A control system or processor (9), for receiving and processing ation from the camera (8) and the RFID antenna/reader (5, 6). For instance, the processor may be programmed 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 ation may be collected and received automatically upon the animal entering the enclosure, and the sor may collect the information via wireless transmission, such that information may be monitored remotely. instrument integration, and the hardware and software used in such a thermal station was designed and ped, in part, at the Lacombe Research Centre, Lacombe, Alberta, Canada.

In one embodiment, an optional omagnetic shielding (7) may be exposed to the holding pen on the side of the panels (1) to prevent the improper reading of RFiD tags on animals that are not within the ure. 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 /motor assembly (8), can be ed 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 ons 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 ty to obtain information from at least two animals at one time. For instance, the system may be designed to odate a second enclosure/thermography station situated parallel to the first n with the camera located centrally between the two stations, thereby at least doubling the animal handling capabilities of the system.

The present apparatus and design may enable known methods of correct thermography techniques, 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 providing correct graphy information, without necessitating capture and restraint of the animal, is contemplated.

Ethologica/ (Behavioural) Information The present apparatus and method can also provide for the use of the lRT ation obtained from animals as a measurement of behavioural prediction of e onset or other biologically important states in animals. For instance, the ’10 thermal images taken at the water station, as herein bed, can be further utilized to obtain behavioural information about the animal, thereby providing means for combining the thermal (temperature) biometric information with oural biometric information, to provide earlier and more accurate disease detection and state identification in the animal.

Each time—stamped image taken by the automated lRT system can be fied as a behavioural “event”. For example, nt of the animal at the water n 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 ing the animal does at the water station, more or fewer images may be taken of one animal as compared to another. The images can then be used to calculate oural factors such as, for example, the total time the animal spent drinking, the number of ng 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 information can then be used, in conjunction with the thermal information to determine “true- positive” (i.e. sick) and true—negative (i.e. non—sick) disease. Accordingly, an ethological predictor of disease, referred to herein as a “Fidget Factor” can provide 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 ing can further be altered due to an illness, growth state, or reproductive 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, er, and body images were all included in the behaviour data set) in order to process the fidget behaviour.

Thus, the present 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 detection 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 el 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 ures including multiple regression and ation, ranking and prediction indexes to enable the more accurate identification of true—positive and true negative animals.

Such detection and identification means are likely to be applicable in a y of settings, including, for example, in bio-security and bio—surveillance circumstances.

The following es 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 d herein.

EXAMPLES Example 1 Animals In this example, forty (40) multiple sourced, co-mingied and transported cial, ned receiver 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 d, 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 Lacombe, 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 disinfected horse trailer.

On arrival at Lacombe Research Station, a transport time of approximately 5 h, the calves were co—mingled with 20 le 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 bedding.

Infrared Thermography Automatic infrared thermography images (lRT) were collected using a portable etrics broadband S60 infrared r (FLIR® lnframetrics S60, Boston, MA, USA). All images were taken of s as they d the automated ed scanning 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 ated as the average of daily radiated maximum temperature for a given animal d by the average daily maximum value for the group of calves. The thermal data was verified by comparison 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 ted lRT system, each image was defined as a behavioural “event” which triggered the image to be ed. During the t example, a 4—minute interval between drinking events (known as the bout criterion interval; BCl) was used to determine the termination or conclusion of one drinking bout and the start of another. Accordingly, the same ed images were used in the analysis of both thermal and ethological data sets, albeit, the ethological data set included “all images”, while the thermography data set ed “orbital images” only.

Results in the forty (40) multiple—sourced and co—mingled commercial calves, there were 10 animals out of the 40 identified 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 y animals (or true negative s) having a temperature 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 trate that haematology data for the forty (40) control retained possession calves displayed normal haematology values.

It is known that four to six days prior to the display of al signs and lab verification of illness, infrared orbitai scans can be 71% efficient (combined true positive and true negative values) at early identifying ill animals compared to either clinical scores alone (55% efficiency) or rectal atures alone (59% efficiency). This is supported in the present example where the non-invasive collection of orbital infrared temperatures, alone, was 73% efficient at identifying ill animals 27 days before clinical symptoms detectable by “pen-checking”. r 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 es 4 and 5). Sick (true—positive) s were found to have a greater number of lRT images taken during each drinking bout. This is 1O e overall drinking 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 tor 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 s only.

TABLE 2 Means iSD of haematology values for the forty multiple sourced 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 analysed using a known enzymatic assay from collected samples. Hematology analysis and differential counts were conducted on a Ceil—Dyne model 3700 hematology analyser (Abbott LabsTM, Mississauga, Ontario). Serology assessment was conducted by Prairie Diagnostic ServicesTM (Saskatoon Saskatchewan) and assessment was carried out for the BRD viruses, Bovine Viral ea (BVD) type 1 and 2, as well as infectious Bovine Rhinotracheitis (iBR) via serum neutralization tests. onal assessment for Corona virus, Bovine nfluenza (PIS) and Bovine Respiratory Syncytial Virus (BRSV) were conducted by ELlSA using methods known to one skilled in the art. Antibody concentrations (units) for BVD, iBR, BRSV, Pi3 and Corona were obtained as follows: ((mean net optical y 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 ranking of antibody titre scores was as follows: for BVD and iBR 0—2 = negative, 3-1321 = suspicious, 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 s in this example, further trials were conducted on 100 multiple d, co— mingied, transported and weaned commercial calves. These calves were ed from two primary sources with 17 from the ADRi herd at Lethbridge, Alberta, Canada and 83 procured from commercial n 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 d, 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, automatic infrared and behavioural 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 s were identified as “at risk” of BRD by the “pen checking" technique. Of these animals, 24 were subsequently ed by objective lab data as being true positive (TP) and 13 were identified as false positive. Hence, the incidence of false negatives and ves 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). s 6 and 7 demonstrate the onship 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 ” tor generated and defined 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 include 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 es, which may have made the ethological data set less robust. Current research on adjusting the BC! and the fidget predictor value is required and continues.

Example 3 Animals in the present Example 3, igations were carried 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 ute (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 ental 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 te typical 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 commercial carrier 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 er 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 fidgets were monitored and an ative ”Fidget Factor” for disease was determined based on various intervals n drinking bouts (eg. 3 or 5 minutes, rather than 4 minutes utilized in Example 1). in addition, inary live observations were ted to determine which specific faciai movements prompted IRT images to be recorded 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 behaviour by, for exampie, video analysis.

Hematology 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 analysis (white blood cell s and neutrophile/lymphocyte ratios), core ature and clinical score. The use of the same analysis would also have classed eleven (11) of the calves as true ve (TP) and 20 as true negative (TN) with the remaining 34 as intermediate health. Three of the true positive (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 ment period are shown in Table 3.

TABLE 3 Average Health Values :t SD for the Example 3 calves Core Temp C al 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 assessment period using the ted thermal station located at the cattle water system. The average radiated temperature value for all calves during this period was typically between 33—35°C. The overall 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 i0.58°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 s of IRT images (when all images were included) ed with true- negative calves. These obsen/ations suggest that shifts in e or stance, eye blinks, leg movements, 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 nce 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 identifying BRD would have fied 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, ped 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 identified 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 suspect for BRD. Cortisol data displayed considerable variation 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 susceptibility across cattle groups from experiment to ment.

Table 4 represents overall averages for the animals, without correction for the t data set for animals displaying health aberrations for non—BRD reasons such as transport stress, mechanical insults such as lameness or other lic reasons such as dehydration. There were a few of these animals identified and they could arguably cause some bias in the data set. Nonetheless, cattle identified as TP for BRD also show a trend towards or an actual statistical se 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 ve animal would display a value of 3 or 4 of these ia and the ediate animals would y a value of 2. As with the other laboratory criteria, these intermediate animals also tended to display an intermediate ol 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 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 finding showing that BRD animals generally display a higher cortisol vaiue with greater vanafion.

Example 5 Fidget Value and growth efficiency As an alternative to the methods sed in the Background n above, evidence has been reported that demonstrates the use of infrared thermography to classify animals into more efficient and less efficient growth ries (Schaefer, A. L., Basarab, J., Scott, S., Colyn, J., McCartney, D., McKinnon, J., Okine, E. and Tong, A. K. W. 2005. The onship 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 ed as a non-limiting example of implementing this principle.

Eight red mature cows were used in the present example to test whether a fidget measurement also ranked with both a measure of growth efficiency (Residual Feed intake, RFI) and a e of energy loss (infrared thermography). The cattle were fed a balanced a 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 ve 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 Basarab et al (Basarab, J. A., McCartney, D., Okine, E. K. and Baron, V. S. 2007. Relationships between progeny residual feed intake and dam productivity traits. an 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. Residual feed intake and body composition in young growing cattle. Canadian l of Animal Science 83(2):189—204).

For infrared and fidget measurements the cows were monitored postprandial between a 24h feed period. 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 station they triggered an infrared scanning system which ed a radio frequency identification tag (RFlD) y recording both their n attendance frequency and their facial infrared 3O characteristics.

The daily average values for the ed scans for the half of the animals with the lowest efficiency and the half of the s 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 individual animal’s actual feed intake was (as measured by the bunk monitoring s) compared to what would be a predicted feed intake for that animal based on her body weight and . For example, as explained by Basarab et al ( 2003, 2007) an animal with an RFI value of 4 represents a cow that consumed 1 kg per day less feed than what would be expected and an RFI value of +1 represents 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 ring 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 . The animals with the lowest thermal values also displayed the lowest RFI values and the lowest number of fidget events. This example demonstrates that a fidget value can have utility 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 efficiency (high RFl) four Cows 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 ers determine whether dairy cows are in estrus (ie have ovulated or are ready to ovulate). oural tors of estrus include increased activity such as mounting events, pacing/walking, as well as general ss 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 ement 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: 2966-2973).

Cows housed in tree—stalls t 4 times more activity and restless behaviour during estrus (Kiddy, C. A. 1977. Variation 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 measure activity and restless 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: 03; 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 aspects. 448 Theriogenology. 74: 327-344). While ters on cows continuously tie stalled have been unable to detect behavioural estrus based upon walking activity measures alone (Feiton, CA, Colazo, M.G., Barajas, P., Bench, C.J., and Ambrose, D.J. 2012. Dairy cows continuously—housed in tie—stalls failed to manifest activity changes during estrus. Can. J. Anim. Sci. (in press», the use of a more subtle behavioural ric of restless behaviour has the ability to capture behavioural estrus even in confined cows.

Because the fidget biometric, as described within the present specification, can be an accurate and reliable measure of restless behaviour 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 ted during estrus. As such, the tus and methods described herein can identify reproductive states such as estrus.

Example 7 Further fidget data (EDOB/OQ ) 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 elevated core (rectal) temperature. These criteria are defined in publications known in the art. By st, a true negative (TN) animal was one that displayed a score of either 0 or 1 out of 4.

Of the ED08/09 data set for 21 s, 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 d, commingled transported 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 te infrared value for the eye maximum determination, the mean ratio of the individual calf eye maximum 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 fidgets/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 multiple regression approach and also a discriminant analysis imes called se 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 fication using a single biometric measurement was between 57—68%. However, combining all three ric measurements raised the overall correct identification of animals into disease class ( both TP and TN) to 83.5% . This improvement in correct classification is icant and also offers the ability to identify BRD before the pull day unlike prior art methods.

One method for ranking the ve importance of each biometric ement in a multi-regression model is to obtain the r value (correlation value), square this value and multiply by 100 to obtain the relative percentage ance of a biometric measure or the proportion of the variance that a particular biometric value can account for. For example, with a situation like calving difficulty in heifers (dystocia) it has been ined 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)

What we claim is:

1. An tus for identifying biological states in an animal, the apparatus sing: means for fying 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 thermography information and the fidget information to ine 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 disease 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, further comprising an ure accessed 15 by the animal.

4. The apparatus of any one of claims 1 to 3, wherein the infrared thermography information and the fidget information are obtained postprandial.

5. The apparatus of claim 4, wherein the andial period is between a 24 hour feed period. 20

6. The apparatus of any one of claims 1 to 5, n the means for identifying the animal comprises antennae for receiving radio-frequency identification (RFID) information from the animal.

7. The tus of any one of claims 1 to 6, wherein the camera is capable of obtaining at least 1 – 60 images per second. 25

8. The apparatus of any one of claims 1 to 7, wherein the processor receives the animal identification ation, 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 thermography images and fidget information from the animal; and 5 processing the ed thermography images and the fidget information to identify a ical state of the animal identified, wherein the biological state is a predictor of onset of disease, growth states and reproductive states in the .

11. The method of claim 10, wherein the method can be med during a postprandial period. 10

12. The method of claim 11, wherein the postprandial period is between a 24h feed period.

13. The method of claim 10, wherein the method is automated.

14. The method of any one of claims 10 to 13, wherein the images are obtained from or around the orbital 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 defined in claim 10 and as ntially described herein with reference to one or more of the accompanying examples.