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
  • A01K15/00—Devices for taming animals, e.g. nose-rings or hobbles; Devices for overturning animals in general; Training or exercising equipment; Covering boxes
  • A01K15/02—Training or exercising equipment, e.g. mazes or labyrinths for animals ; Electric shock devices; Toys specially adapted for animals
• • 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

Definitions

• the present invention relates to an automated method and system for altering or controlling the behaviour of livestock animals. Particularly, the present invention provides methods and systems for monitoring and preventing and/or correcting undesired livestock behaviour.
• pig husbandry for example, aggressive behaviour (e.g. fighting) or abnormal behaviour (e.g. tail-biting, ear-biting) are undesired behaviours since they cause high stress level: the pigs become more easily subjected to diseases and have lower growth levels.
• Other undesired behaviour is e.g. cribbing in species such as horses. Cribbing is a symptom of confining a range animal to a small stall, and most horses are confined to small stalls for convenience.
• pigs In intensive farming, pigs are kept in a confined environment and express aggressive behaviour on a much higher level than they do in a natural environment. Reasons for this aggressive behaviour in intensive farming conditions can be found in the limited space allowance, barren environment, low fibre feed diets and repeated changes in group composition.
• domesticated pigs are hierarchical animals just like wild pigs and in intensive farming, the group hierarchy does not always remain stable due to the commercial practice of mixing the animals. This mixing occurs usually after weaning, at the beginning of the fattening period or in sows after service due to management choice. This practice results in intense aggressive interactions that occur mainly in the period of the first two days from the moment of the new group formation until the new dominance hierarchy has been established. These encounters can lead to wounds that may cause infections and in extreme cases may even be lethal.
• US5566645 describes a method for animal training, particularly a method for rapidly and effectively training horses and other animals by facilitating the delivery of a primary reinforcement reward substance to the animal simultaneously with, or immediately following the exhibition of desired behaviour by the animal.
• GB2473540 describes an animal training apparatus comprising a trigger
• US4335682 provides a unit adapted to be worn by a dog or other animal, which acts under the control of a remote control unit to produce stimuli including an aversive electrical stimulus, a characteristic sound or other second stimulus to which the animal responds with a safety, relief and relaxation response, or a warning stimulus.
• US5351653 describes a method for training an animal based on positive and negative audio signals. The method enables a trainer to encourage good behaviour by the animal by applying the positive audio tone after the animal has been trained to associate the positive audio tone with pleasant feelings and to discourage bad behaviour by the animal by applying the negative audio tone.
• Livestock animals instead, are kept in very big groups (e.g. 60.000 broilers). The farmer has no time to train each individual animal. Also, many training methods based on punishment of undesired behaviour include harming the animal in some way. In particular, shocking devices of various kinds are well known in animal training. Such treatment is considered to be wasted.
• the present invention provides a fully automated method and system to monitor, correct or prevent the undesired behaviour, by a combination of different triggers or stimuli, which the animal associates with a reward, punishment or warning.
• said trigger or stimuli is initiated when the first or early signs of undesired behaviour have been detected or predicted, thus stopping or preventing the undesired behaviour in a fully automated way.
• Automated monitoring of on-farm animal welfare has a number of potential advantages, such as continuous measuring of indicators, real-time registrations, more accurate and precise information and increasing flexibility in the time management of the farmer.
• a first aspect of the present invention provides an automated method for controlling or preventing undesired behaviour of an animal or group of animals comprising the steps of automatically monitoring the behaviour of said animal or group of animals and generating a stimulus or trigger to prevent or stop the undesired behaviour.
• said automated monitoring detects the early signs of undesired behaviour of said animal or group of animals or generates data that is to predict future undesired behaviour of said animal or group of animals.
• said stimulus or trigger is generated automatically and prevents or stops the undesired behaviour, such as by capturing the attention of said animal or group of animals.
• said method of the present invention further comprises the step of conditioning or training the animal to associate said trigger or stimulus with a reward, a punishment or warning.
• Another aspect of the present invention relates to a system or control device for
• automatically controlling or preventing undesired behaviour of an animal or group of animals comprising means for monitoring the behaviour of said animal or groups of animals and means for generating a trigger or stimulus to prevent or stop undesired behaviour.
• Said means for monitoring the animal behaviour comprises at least one suitable sensor (e.g. a camera, a microphone, a motion sensor, a heat sensor, a heart rate sensor) which collects data on the animal behaviour.
• said system or control device of the present invention further comprises a processing system, capable of processing the data collected by the sensor(s) to detect or predict undesired behaviour, preferably the onset or early signs of said undesired behaviour.
• Said processing system also commands said means for generating a trigger or stimulus to generate a suitable trigger to prevent or stop undesired behaviour.
• the method and control device of the present invention modifies, reduces, prevents or eliminates undesired animal behavior in an automated way, i.e. without human intervention or in the absence of a human supervisor.
• the reduction, prevention or elimination of the undesired behaviour by the animal occurs without punishment of said animal.
• the method and control device of the present invention is used to modify or prevent the undesired behaviour of at least one animal (such as one or more animals showing early signs of undesired behaviour) within a group of animals.
• Figure 1 illustrates the general structure of an automated control scheme according to a method of the present invention.
• Figure 2 illustrates the general structure of an automated control scheme to grab the attention of the animal to control or prevent undesired behaviour
• Figure 3 illustrates the different non-contact pre-sign positions before the start of the aggressive behaviour. Codes as in Table 1 a.
• Figure 4 illustrates the different contact pre-sign positions before the start of the aggressive behaviour. Codes as in Table 1 b.
• Figure 5 shows a Motion History Image of a pig moving from left to right.
• Figure 6 shows segmented zones of pig movement from the Motion History Image.
• Figure 7 shows the scatter plot depicting the association between the two features extracted from the MHI for both the classes aggression and no aggression.
• the line is the calculated LDA boundary representing the separation between the two clusters of data.
• Figure 8 shows the scatter plot depicting the association between the two features extracted from the MHI for both the classes aggression and no aggression manually selected between episodes with low, medium and high group activity.
• the line is the calculated LDA boundary representing the separation between the two clusters of data.
• Figure 9 The predicted probability of behaviour continuation after sound release.
• Figure 10 shows the total number of all behaviours (black) and the number of interrupted behaviours (white) observed at day 1 and day 2 after mixing (five experimental trials).
• Figure 1 1 Reaction of the receiver in relation to the aggressive interactions, which were stopped after feeder activation (from 147 stopped interactions).
• Figure 12 Reaction of the aggressor in relation to the aggressive interactions, which were stopped after feeder activation (from 147 stopped interactions).
• Figure 13 Type of reaction in relation to the total percentage of encounters.
• Figure 14 Type of reaction (none, stopped and not stopped) in relation to the total percentage of encounters.
• livestock refers to any animal or group of animals which is intended to be monitored and/or managed, regardless of whether the animal(s) is
• domesticated, semi-domesticated or wild and regardless of the environment in which the animal may be found, such as, for example, in a commercial animal operation, or in a wild environment.
• Preferred livestock animals include animal species capable of learning to associate a trigger or stimulus with a reward, punishment and/or warning and include pigs, cattle, goats, sheep, horses, chickens, buffalo, deer or other wild animals.
• the method and control device of the present invention may also find use for zoo animals or laboratory animals.
• a "processing system” includes a system using one or more processors, microcontrollers and/or digital signal processors having the capability of running a "program” which is a set of executable machine code.
• Processing systems include computers, or “computing devices” of all forms (desktops, laptops, PDAs, servers, workstations, etc.), as well as other processor-based communication and electronic devices such as cell phones, tablets, personal data assistants, etc.
• Such processing systems may be discrete units, or may be formed of multiple components, which may be networked or otherwise capable of being placed in operative communication with one another, at least at needed intervals.
• a "program” as used herein, includes user-level applications as well as system -directed applications or daemons.
• the inventors developed a fully automated method and system for controlling or preventing undesired behaviour of an animal or group of animals, particularly livestock animals, making use of the intelligence of the animals and their ability to learn.
• a trigger or stimulus is automatically generated when automated monitoring shows or predicts undesired behaviour of an animal.
• Said trigger or stimulus then prevents or stops the undesired behaviour, particularly by capturing the attention of said animal.
• said method and system is also capable of training the animals, particularly livestock, to associate said trigger or stimulus with a reward, punishment or warning in a fully-automated way.
• the conditioned animal will then give its attention to the trigger or stimulus at the moment that early signs of undesired have been observed or predicted, thus its undesired behaviour is prevented in a fully automated way.
• a first aspect of the present invention provides an automated method for controlling or preventing undesired behaviour of an animal or group of animals comprising the steps of automatically monitoring the behaviour of said animal or group of animals and generating a stimulus or trigger to prevent or stop the undesired behaviour.
• said automated monitoring detects the early signs (before escalation) of undesired behaviour of said animal or group of animals or generates data that is processed to predict future undesired behaviour of said animal or group of animals.
• said stimulus or trigger is generated automatically and prevents the escalation towards or stops the undesired behaviour, such as by capturing or redirecting the attention of said animal or group of animals, particularly by the (conditioned) association of said stimulus or trigger to a reward, punishment or warning, preferably a reward.
• said method of the present invention further comprises the step of conditioning or training the animal to associate said trigger or stimulus with a reward, a punishment or warning.
• the present invention further relates to a system and control device for automatically controlling or preventing undesired behaviour of an animal or group of animals comprising means for monitoring the behaviour of said animal or groups of animals and means for generating a trigger or stimulus to prevent or stop undesired behaviour.
• Said means for monitoring the animal behaviour comprises at least one suitable sensor which collects data on the animal behaviour.
• said system or control device of the present invention further comprises a processing system, capable of processing the data collected by the sensor(s) to identify, detect or predict undesired behaviour, preferably the onset or early signs of said undesired behaviour.
• Said processing system also commands said means for generating a trigger or stimulus to generate a suitable trigger to prevent or stop undesired behaviour.
• Said processing system thus relates the identified or predicted undesired behaviour with the generation of a trigger to stop or prevent said undesired behaviour.
• Said processing means may also comprise means for storage of data.
• a suitable trigger or stimulus to capture or redirect the attention of the animal or to stop/prevent the undesired animal behaviour can be an audible, visual or sensory stimulus (e.g. sound, light, vibration, pressure).
• said method or device of the present invention can be used to condition the animal to associate said trigger, such as a particular sound or light, with a reward, a punishment or warning. Any sensory stimulus which is pleasant or unpleasant to the animal may be used as reward or
• a reward is used.
• examples include, without limitation, food and other edible material dispensed in the form of pellets, toys of the chewable type (chewtoys) made with rubber, plastic, rawhide and the like, puzzle toys and toys which may be filled with food, a brush, scents dispensed in the form of a spray, a breeze using a tunnel fan, vibrations using a vibrator and/or rocker pad, visual items and recordings.
• Suitable sensors include at least one or more of the following:
• a microphone which may be contained in the housing or mounted to a collar on the animal; - a motion sensor to detect e.g. a specific motion pattern (pacing);
• a heat sensor for sensing body heat of the animal, particularly livestock animal
• a heart rate sensor for sensing the heart rate of the animal, particularly livestock animal
• chewtoys may be equipped with pressure and/or tension sensors to monitor chewing behaviour.
• a pressure-sensitive pad may detect a position of the animal thereon, which may correspond to a desired behaviour such as resting, or absence of pacing, door scratching and the like spatially fixated undesired behaviours at other locations.
• a particular embodiment of the control device of the invention may include a processing system operatively executing one or more algorithms or programs for analyzing inputs received from the at least one sensor, said one or more algorithms being configured (A) (i) to identify the inputs the occurrence of undesired behaviour, particularly the onset or early signs of such undesired behaviour; or (ii) to predict the future occurrence of such undesired behaviour; and once such undesired behaviour has been detected or predicted (B) to command the generation of a trigger or stimulus to stop such undesired behaviour.
• animal behaviour is monitored in a continuous way with real-time registration and processing, with an accuracy that allows the continuous detection and/or prediction of specific behavioural aspects.
• the automated monitoring may include collecting data that will be useful in identifying individual livestock.
• This may be implemented with a tag, particularly a tag that is machine-readable only.
• a tag relates to any device allowing identification of an individual animal, regardless of how the device may be associated with an animal, such as by being externally affixed to the animal (for example, in the manner of conventionally-known ear tags, by a collar, or by some other mechanism), or by being implanted or otherwise internally carried by the animal.
• said tag may be a passive, machine-readable radio frequency identification (“RFID”) tag associated with an individual animal.
• RFID radio frequency identification
• FIG. 1 A method for controlling or preventing undesired behaviour in a fully automated way is schematically shown in Figure 1 .
• the animal or each individual animal in a group of animals is monitored continuously by using suitable sensors, such as cameras, microphones, etc. These sensors are used to extract variables from the animals. These variables are used to detect the early signs that will lead to undesired behaviours or are used to predict future undesired behaviour.
• their attention is captured by using one or more triggers or stimuli and the animal will not enter into a specific stage or specific mental status of undesired behaviour (e.g. fighting, biting, ...) ( Figure 2). If the attention of the livestock animal is obtained, then the animal can be preferentially rewarded (e.g. giving feed, ). Alternatively, it can be "punished".
• the undesired behaviour is detected or predicted as early as possible. Once the animal enters too far in a specific stage or specific mental status of undesired behaviour it is difficult, or even impossible, to grab their attention and control their behaviour.
• the reward/punishment more preferably reward, or warning is used as part of a reinforcement learning in which the livestock is conditioned to associate the trigger(s) or stimuli (e.g. light or sound) with the application of a reward/punishment.
• the distribution of punishment and reward is adjusted automatically in the time of the training. At the beginning of the training the rewards/punishments will be continuously given in response of the attention given, but it will decrease over time and at the end the reward/punishment will be only given in order to keep and reinforce the association between reward/punishment and the trigger(s).
• the schematic diagram presented in Figure 2 shows how in the present method and control device the attention of the animal can be captured.
• the animal is the central process of the system and it is affected by the environment and by the social interaction with the other animals.
• trigger(s) are used. Possible triggers are sound, light, smell, etc.
• the triggers are changed and adjusted depending on the response of the animal in a close loop control system.
• An automatic animal behaviour control device or method of the present invention may reduce or eliminate the frequency or occurrence of any undesired behaviour that increases the stress level in livestock animals, including but not limited to aggressive behaviour, self- mutilation, biting, etc, that is detectable and that can be detected and processed as an electronic signal. Because the attention of the animal is captured or diverted and/or the animal is likely to settle down calmly, the automated method and device of the present invention will also reduce the level of stress and, consequently, increase livestock welfare and productivity.
• the experiment was carried out at a commercial farm, located in Heusden (the Netherlands), with a capacity for approximately 6000 fattening pigs, weighing from 23 - 120 kg.
• the farm utilizes ad libitum dry feeding systems (Fancom B.V. - F71 ) and a central flow ventilation system (Fancom B.V. - F21 ).
• Behavioural observations were carried out on a newly composed group of 1 1 entire male pigs of 23 kg on average kept in a pen of 4m x 2,5m with partially slatted concrete floor and solid pen walls. Pigs were sprayed on their backs with standard colour stock marker to facilitate the identification of individuals in order to identify their behaviour before the start of aggressive interaction.
• Video recordings were performed using a camera (Allied Vision Technologies®, model F080C), placed above the pen in central position (in top view), at the height of 2.3m, that permitted an overview of the whole pen.
• the camera was connected to a computer installed inside the room. Strong paper wall was protecting pigs from disturbances associated with computer and human presence in the room. Images were captured with a frame rate of 1 1 frames per second, resolution of 1032 x 778 pixels, in colour. A total of 8 hours of video recordings were registered during 3 days after mixing. The video recordings were carefully observed to detect aggressive interactions between pigs.
• the aggressive interaction was defined as a close physical contact, during which at least one of interacting pigs performed head knocking, biting or pressing behaviours deemed to have ended after retreat of one or both pigs with separation lasting at least 3 sec. If after the separation one of the pigs attacked immediately the other animal it was counted as new interaction. Each interaction was observed (labelled) on the video images frame by frame (1 1 frames per second) to register its exact duration and to describe the behaviour and body positions of pigs on the early phase of aggression. The following initial behaviours of initiator pig at the first moment of aggressive interaction were labelled:
• - Body biting assigned when initiator started aggressive interaction with biting (opened its mouth and closed it on another pig) of any part of the body of another pig, excluding the front third of the body (head, ear, neck).
• - Head biting assigned when initiator started aggressive interaction with biting at the head region (except ears) of another pig.
• Ear biting assigned when initiator pigs started aggressive interaction with biting at the ear of another pig.
• a non-contact pre-sign position was defined as a body position of an initiator pig towards another pig before the start of aggressive interaction.
• the duration of non-contact pre-sign position was registered from the moment when the initiator pig was noticed to raise its head in the direction of the other pig prior to approaching till the start of aggressive interaction.
• the contact body positions were defined as the body positions of the pigs already approached to each other and resting in close contact by any part of their body (Table 1 , Figure 4). They were labelled as contact pre-sign positions when the animals were staying in these positions for at least 1 second before the start of aggressive interaction, while at the first moment of the start of aggressive interaction the same positions were registered as contact body positions at the start of aggressive interaction. Duration of contact position as pre-sign was recorded from the moment of the first contact of any part of the pigs' body till the start of aggressive interaction. The duration of aggressive interaction was registered from the moment of the detection of contact aggressive interaction start position till separation of the pigs.
• the non-contact pre-sign positions could be noticed before 55% of observed aggressive interactions (Table 3), while the contact pre-sign positions were observed only in 15 % of the cases.
• the contact pre-sign positions in some cases lasted more than 2 sec, when one of the pigs was following another one while not breaking body contact or in the case of mounting. Table 4. Duration of pre-sign positions (sec)
• Table 6 shows the initial behaviour of the initiator pig, with which it started an aggressive interaction and the contact body position from which it was performed.
• the aggressive interaction initial behaviours the most frequent was the head knocking (34.46 %).
• This initial behaviour was observed in relation to P12 and P7 positions.
• the bites were particularly directed to the neck and ears.
• From P12 position pigs started aggressive interaction with biting more frequently than from other positions (Table 6), mostly directed towards the neck.
• Mounting was a pre-sign only for 3 aggressive interactions, in most of the cases this behaviour of initiator pig did not escalate towards an aggressive response from the receiver.
• Table 6 Initial behaviour in relation to the positions of the pigs' body at the start of aggressive interaction
• neck/shoulders and head are the main target zone for bites during the fights (eg.
• the pens had a dimension of 2 m x 1 .8 m and were equipped with slatted floor and solid pen walls.
• the piglets had ad libitum access to dry feed and water and the animal feeding place ratio was 1 .5:1 .
• the experimental phase started after mixing and lasted 2 days until a new hierarchy among the animals was formed. VIDEO RECORDING. Video recordings of this mixing phase provided a dataset that was used to classify aggressive interactions among piglets.
• Video were captured for the first 3 hours after the groups were established and then for 3 hours at approximately 24 h post-grouping. The idea behind was that during the first 3 hours after mixing the pigs have the most severe fights. A relatively short time was needed because of the time consuming labelling procedure since the videos are observed image by image (25 images per sec) to detect all aggressive acts.
• 03514 3.5 mm lens (VS Technology, Tokyo, Japan). It recorded at a resolution of 1032 ⁇ 778 pixels.
• the second camera was a Guppy GC1350 camera (Allied Vision Technologies, Germany). The camera used a Pentax 4.8 mm lens (Pentax Corporation, Tokyo, Japan). It recorded at a resolution of 1360 ⁇ 1024 pixels.
• Both cameras were connected to a computer with LabVIEW (8.6, National Instrument, TX) that recorded synchronised videos in MJPEG.
• the computer's processor was Intel(R) Core(TM) 2 Quad CPU Q9300 @ 2.50GHz with 6 GB of physical memory.
• the operating system was Microsoft Windows 7 Ultimate.
• an aggressive interaction was defined as a close physical contact which lasted at least five seconds and in which at least one of the interacting pigs performed head knocking, biting, or pressing behaviour.
• an aggressive interaction stopped for example due to the retreat of one or both pigs, this sequence was interpreted as finished and any further interaction was considered a new episode.
• the starting and ending time of every interaction was therefore determined and used as a reference for the classifier.
• the labelling procedure is necessary in supervised learning in order to infer an unknown probabilistic function P(x, t) between inputs x e X and labels t e L.
• This function is called classifier when the output is discrete.
• DATASET In order to evaluate the algorithm, a dataset of 150 episodes with and 150 episodes without aggressive interactions was built (Table 7).
• the 150 episodes with aggressive interactions were randomly selected from the 228 episodes manually labelled by the expert.
• the 150 episodes without aggressive interactions were built in two steps: 100 episodes without aggressive interactions were randomly selected, while 50 episodes were manually selected by the expert from 1 1 episodes with low group activity (up to 50% of pig moving, up to 50% of pigs resting), 25 episodes with medium group activity (50-80% of pigs moving) and 14 episodes with high group activity (80-100% of pigs moving).
• This manually selected data was used as a validation of the algorithm in order to prevent that the randomly selected data without aggressive interaction were generated from instances without any activity (i.e. during sleeping).
• Table 7 Dataset used for classifying aggressive interactions. The dataset consisted of 150 episodes with aggressive interactions (randomly selected) and 150 episodes without aggressive interactions (100 randomly selected and 50 manually selected between episode with low, medium and high group activity). In the table is reported the minimum, maximum, mean and standard deviation of the duration of each category of episodes.
• the Motion History Image is a static image that represents how motion is moving by describing the pixel intensity as a function of the motion history at that point.
• the result is a scalar-valued image ( Figure 5) where brighter values correspond to more recent motion.
• the MHI was implemented in Matlab (R2010a, The MathWorks Inc., MA).
• the values of the MHI were rescaled between 0 and 255 pixels in order to obtain a grey scale image.
• This grey scale image was segmented in order to extract local regions of motion ( Figure 6). Since the aggressive interactions happened between at least two pigs and since the mean pixel size of one pig is 20000 pixels, the segmented zones of movement smaller than 24000 pixels were filtered out and excluded from further analysis.
• Featurel is a scalar specifying the mean of all the intensity values in the region. This feature represents how strong and intense the motion in the image is.
• Feature2 is a scalar representing the occupation of the movement inside the regions and is calculated by the ratio of pixels unequal to zero in the region and the total number of pixels in the region. This feature thus gives spatial information about the movement.
• LDA Linear Discriminant Analysis
• the discriminant coefficients w maximise the distance between the means of the dependent variables.
• LDA binary classification
• the LDA was used to classify if there were aggressive interactions in the video episodes, using featurel and feature2 extracted from image processing.
• SPSS (20, IBM, NY) was used for the LDA to classify aggressive and not aggressive interactions.
• the first step consisted in the calculation of the discriminant coefficient of the LDA function based on the features extracted from the MHI.
• the confusion matrix is a matrix in which the rows are the classes defined by the expert and the columns are the predicted classes. From this matrix, statistical measures of performance such as sensitivity, specificity and accuracy were retrieved.
• Sensitivity measures the proportion of actual positive values which are correctly classified.
• Specificity measures the proportion of negative values which are correctly classified.
• the data were also cross-validated by using the leave-one-out method.
• the leave-one-out method uses a single observation from the original sample as validation data and applies the remaining observations as training data. This method is repeated until each observation in the sample has been used once as validation data.
• Predictor variables were the two features extracted from the MHI, namely, the mean intensity ⁇ featurel) and the occupation index ⁇ feature2).
• LA Low Activity
• MA Medium Activity
• HA High Activity
• Table 9 illustrates the mean differences and standard deviation between the two features in the two different classes. Table 9. Descriptive statistical information of the two features used to classify aggressive interactions.
• Table 1 1 illustrates the results of the LDA classifier, using leave-one-out cross-validation. As can be seen from the confusion matrix, 133 episodes with aggressive interactions and 108 episodes without aggressive interactions were correctly classified. These results indicate an accuracy of 88.4%, a sensitivity of 89.9% and a specificity of 86.7%.
• Table 1 1 Confusion matrix of the Linear Discriminant Classifier for both the original and the leave-one-out cross-validation dataset, without the episodes that were filtered out.
• Image processing has been used to calculate information about the pigs' activity by means of the activity index in the study of Costa et al. (2009).
• the activity information was extracted from an entire pen or from fixed zones within a pen
• the use of the MHI provides both spatial and temporal information that is calculated automatically from the motion of the animals and is thus not bound to predefined zones.
• the most crucial disadvantage of using the activity and occupation index as in the Costa study (2009) is the fact that movements caused by different kinds of behaviour were summed up and could not be discriminated when occurring within the same zone. Stated differently, the activity and occupation index provides temporal information only, but no spatial information over time.
• the MHI no fixed zones needed to be defined. Instead, zones were calculated dynamically, by using the segmented regions of motion, and analysed separately. Therefore, the method exploited in this study provided more valuable information to detect aggressive interactions among pigs.
• the farmers should check the health and welfare status of their animals by assessing injuries in the pen that indicate occurrences of aggression.
• an automatic aggression monitoring system would be beneficial to both farmer and animal.
• the present monitoring tool that can continuously and automatically detect aggressive behaviour and consequently monitor the level of aggression in each pen is therefore a valuable tool and can be used by the farmer to increase the animals' health and welfare and to decrease the economic losses.
• the farmer can intervene more quickly by separating aggressive animals or by introducing environmental enrichment material in order to reduce the aggression level.
• aggression levels often return to the same level after a certain period of time due to habituation.
• the environmental enrichment could be changed in order to prevent the effect of habituation whenever the level of aggression exceeds a certain level.
• growth rates and uniformity of pigs as well as fertility of breeding sows could be improved.
• the present approach does not involve high costs and does not interfere with the animals.
• this monitoring tool able to identify aggression in an automated way before it starts or in a very initial stage before it escalates into an injuring fight can be coupled to a trigger/stimulus generating system (with the pig conditioned to associate the trigger/stimulus to a reward, punishment or warning) which allows to intervene automatically before the aggressive interaction starts.
• a method based on Motion History Image was used to calculate dynamic local temporal and spatial information about the mean activity and occupation index in order to detect aggressive interactions among pigs. The results revealed a classification accuracy of 89%, a sensitivity of 88.7% and a specificity of 89.3% and proved that the two motion features (occupation index & mean activity) can be successfully used in order to discriminate between aggressive and nonaggressive interaction.
• the aim of this study was to develop a method to automatically detect aggressive behaviour among pigs by means of image processing.
• 24 piglets were mixed in 2 pens after weaning and captured on video for a total of 60 hours in 5 repetitive experiments. From these video recordings, a dataset containing 150 episodes with and 150 episodes without aggressive interactions was built through manual labelling.
• the Motion History Image was used to gain information about the pigs' motion and to relate this information to aggressive interactions.
• Two features were extracted from the segmented region of the Motion History Image, namely, the mean intensity of motion and the occupation index. Based on these two features, the Linear Discriminant Analysis was used to classify the presence of aggressive interactions in every episode. Applying leave-one-out cross-validation, the accuracy of the system was 89% with a sensitivity of 88.7% and a specificity of 89.3%.
• the enrichment tool consisted of a commercially available electronic dog feeder (Manners Minder Treat and Train®, Sommerville Ct.,USA) filled with potentially attractive feed for piglets (chocolate candies).
• the feeder was activated by remote control releasing a sound signal just before feed distribution.
• Phase 1 Training phase
• Phase 2 (mixing phase), lasted two days and aimed to test the response of animals on the feeder sound during the performance of aggressive and abnormal behaviours.
• piglets were selected based on their weight (average 10kg ⁇ 1 ) and mixed in two pens, 12 piglets per pen. The dimension of the pens was 2m x 1 .8m (3.6m 3 ) with slatted floor and solid pen walls. The piglets had ad libitum access to dry food and water and the animal to feeding place ratio was 1 .5 to 1 . Direct observations were made for the first 3 h after the groups were established (day 1 ) and then for 3 h approximately 24 h post-grouping (day 2).
• the experimental phases were recorded by two video cameras, Guppy F-080C and. Guppy GC1350 (Allied Vision Technologies, Germany) placed at the height of 2.0 m above the pen floor. Both cameras were connected to a computer with LabVIEW (8.6, National Instrument, TX) that recorded synchronised videos in MJPEG format with variable image rates between 10 and 20 images per second, resolution of 1032 x 778 pixels for the F080C camera and 1360x1024 for the GC1350 and both in colour.
• the computer's processor was Intel(R) Core (TM) 2 Quad CPU Q9300 @ 2.50GHz with 6 GB of physical memory.
• the operating system was Microsoft Windows 7 Ultimate.
• the recorded videos were analyzed and manually labelled by one observer using a software tool developed in Matlab for that purpose (R2009a, The MathWorks Inc., MA, USA).
• the database of the Phase 1 (56 h) was analysed to estimate the learning performance of the piglets per each day.
• the number of piglets around the dog feeder 5 s after the sound exposure was counted.
• Feeder latency latency of response to the feeder sound and interruption of behaviour
• the labelling procedure permitted the identification of every selected behaviour happened during a certain period of time.
• Each recorded video was visually checked image by image (25 images per second) when an aggressive or abnormal behaviour was noticed on the video.
• a least squares analysis was carried out on duration of behaviours and feeder latency. Due to the small number of events, push rooting disk, lifting other and tail biting behaviours were excluded from analysis.
• the model included the fixed effects of the day (1 , 2), the response on the feeder sound (1 , 2), the behaviour (1 ,6) and the interaction between the response on feeder and behaviour , whereas for feeder latency the model included the fixed effects of trial (1 , 5) and pen (1 , 2).
• the comparison among the least square means was carried out by t-test. For these analyses the GLM procedure was used (SAS, 2008).
• Phase 1 of the experiment the animals were trained to approach the dog feeder after the release of the sound. On the first day 31 .6 ⁇ 4.1 % of the animals were around the feeder. On day 4 the piglets had reached a rate of 50 ⁇ 3.6%. Subsequently, they never fell below that value and reached 71 ⁇ 3.3 % at the end of this experimental phase (day 8). This shows that the piglets learned quickly to recognize the dog feeder as a food source.
• the logistic regression showed that the type of behaviour had a significant effect (P ⁇ 0.001 ) on the piglets response to the feeder sound (continuation or interruption of behaviour).
• the behaviours were included in the model as risk factors for the continuation of the behavioural event after the feeder sound exposure (Table 14).
• the logistic regression model predicted the continuation of behavioural event with a probability rate of 0.28 when the feeder sound was released within the first second after it's start (Figure 9). The later the feeder sound was released after the start of the behavioural event the higher is the predicted probability that the action or fight continues.
• Table 15 Mean duration (s) (LSM ⁇ SEM) of each type of behavioural event interrupted by the feeder sound versus continued.
• Table 16 The effect of the trial on the latency of response to the feeder sound (in seconds) of the piglets involved in aggressive interaction
• the presented method bears is able to reduce the frequency and duration of aggressive or undesired actions among young piglets.
• the motivation for an attractive food bait can be in most of the cases (up to 74%) higher than to continue with a just started fight.
• Aggressive behaviours related to the establishment of a dominance hierarchy within a group are less likely to be interrupted.
• Aggressive and violent actions among young piglets in unstructured common pens of livestock production systems, caused by reasons different from hierarchy establishment, can be successfully reduced by a sound - food reward application.
• Example 3 The same piglets that were trained in Example 3 (Phase 1 ) were used in a test called Resident-Intruder, where two piglets are confronted in a test arena for maximum 7 minutes, depending on their response towards each other.
• the test arena was formed by partitioning a portion of the home pen of a group of 12 trained piglets with a black board made of strong plastic. The resident piglet was first isolated in the arena built in its home pen. The intruder piglet was then collected from another pen and placed into the test arena already containing the resident piglet. If an attack occurred, the electronic feeder was activated in order to break the aggressive interaction. Pairs of piglets were randomly selected. 12 resident piglets were tested once a day with different partners for two days. In total 260 aggressive interactions were analysed in 3 rounds of experiment.
• the piglets played different roles by being aggressors or receivers during a certain confrontation. Regarding the aggressive interactions that could be effectively stopped, the receiver reacted in 65% and the aggressor reacted in 97% of the feeder activations ( Figures 1 1 and 12).
• the method and system according to the invention may instead for being used to monitor, modify or prevent undesired behavior of pigs, be used to monitor, modify or prevent undesired behaviour of other animals, such as poultry or cows.
• Undesired behavior amongst poulty may for instance comprise pecking order fights, feather picking, laying eggs in litter, water and feed spoilage and sexual dominance.
• Examples of undesired behaviour amongst cows may comprise aggression to other animals, humans, water and feed spoilage, sexual behaviour (mounting), lying on defecation areas, milking of other cows and navel licking.
• Other modifications, variations and alternatives are also possible.
• the specifications, drawings and examples are, accordingly, to be regarded in an illustrative rather than in a restrictive sense.
• any reference signs placed between parentheses shall not be construed as limiting the claim.
• the word 'comprising' does not exclude the presence of other features or steps then those listed in a claim.
• the words 'a' and 'an' shall not be construed as limited to 'only one', but instead are used to mean 'at least one', and do not exclude a plurality.
• the mere fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage.

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