EP2509408A1 - Procédé et système d'estimation de la consommation d'herbe d'un animal - Google Patents

Procédé et système d'estimation de la consommation d'herbe d'un animal

Info

Publication number
EP2509408A1
EP2509408A1 EP10796267A EP10796267A EP2509408A1 EP 2509408 A1 EP2509408 A1 EP 2509408A1 EP 10796267 A EP10796267 A EP 10796267A EP 10796267 A EP10796267 A EP 10796267A EP 2509408 A1 EP2509408 A1 EP 2509408A1
Authority
EP
European Patent Office
Prior art keywords
herbage
animal
uptake
data
head
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP10796267A
Other languages
German (de)
English (en)
Inventor
Frank Willem Oudshoorn
Esmaeil Shahrak Nadimi
Rasmus Nyholm JØRGENSEN
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
WEBSTECH APS
Original Assignee
WEBSTECH APS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by WEBSTECH APS filed Critical WEBSTECH APS
Publication of EP2509408A1 publication Critical patent/EP2509408A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01K—ANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K29/00—Other apparatus for animal husbandry
    • A01K29/005—Monitoring or measuring activity, e.g. detecting heat or mating
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01K—ANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K11/00—Marking of animals
    • A01K11/006—Automatic identification systems for animals, e.g. electronic devices, transponders for animals
    • A01K11/008—Automatic identification systems for animals, e.g. electronic devices, transponders for animals incorporating GPS

Definitions

  • the invention relates to feeding management of animal and in particular to determination of herbage uptake by animals in the field.
  • the feeding of for example cows for milk or meat production may be based on grazing in the field and supplements such as concentrates and silage. It is desirable to control the total amount of feed from grazing and supplements to ensure the cows neither eat too little nor too much since too little feed reduces the agricultural production, e.g. milk production, whereas too much feed may result in increased feeding costs without corresponding production increase.
  • WO 2007/119070 discloses a method and system for monitoring the condition of livestock and comprises a plurality of sensors for sensing a plurality of different behavioral parameters of an animal.
  • the sensed data is transmitted by a unit, wirelessly, to a central processor and a plurality of status conditions of the animal is determined on the basis of the transmitted sensed data, such as the onset of parturition, fertility status and other health status conditions.
  • the unit may be permanently worn by the animal and may keep an electronic record of the status conditions of the animal.
  • WO 2007/119070 discloses monitoring of parameters of an animal, but does not determine detailed herbage uptake. Accordingly, WO 2007/119070 does not provide a solution to the problem and, therefore, there is a need for solving the above mentioned problems.
  • the invention contributes to alleviate or eliminate one or more of the above mentioned problems of managing feeding practice to grazing animals.
  • a method for determining herbage uptake of a grazing animal in the field comprises,
  • the strong correlation between the head movement frequency and the herbage uptake may be recognized and from this the relationship between herbage uptake and the head accelerations and/or head movement frequencies can be modeled using e.g. a black box model such as a neural network.
  • the improved accuracy of estimated herbage uptake may have a number of associated advantages for the farmer and the society in general.
  • the farmer knows how much herbage the cow has consumed e.g. in terms of the feeding value, e.g. calories
  • the farmer might save labor since the amount of supplementary feed stuff could be automated as sensor-based determination of supplementary feed stuff may replace qualified guessing of the amounts.
  • the estimation of grazing could stimulate farmers to re-introduce grazing practice, as one of the motives for modern high producing farmers for not using grazing is the lack of control with grass uptake.
  • Reduction of climate gas emissions may be seen as another advantage of the invention.
  • discharge of methane from the animal itself may be reduced since the total amount of consumed feed stuff and herbage can be controlled to avoid over-feeding, and discharge of climate gasses (e.g . nitrous oxides and carbon dioxide) from the production of feed stuff can be reduced as well, since only the necessary amount of feed stuff is given to the animal.
  • Discharge of carbon dioxide from energy use for cut and carry of grass may also decrease.
  • the invention may solve one or more of these problems. It should be noted that the estimation of herbage uptake may be provided as real time data, i.e. new herbage uptake values may be continuously provided, e.g. each time the cow rips off grass. Alternatively, the herbage uptake values may be provided as accumulated herbage values, e.g . per minute, per hour and/or per day.
  • the dry matter yield of the fields used for grazing may increase with controlled grazing, which stimulates grass shoots to appear and grow.
  • the method may further comprise determining a herbage parameter and using the herbage parameter in addition to the head acceleration and/or the head movement frequency for estimating the amount of herbage uptake.
  • a herbage parameter such as a quality of the grass, an amount of available grass per square meter, grass length, a feeding value or dry matter content of the grass may advantageously be combined with other input data for estimating the amount of grass uptake. That is, since the herbage parameter contains information relating to the actual available herbage, the estimation may be improved by inclusion of such herbage data.
  • the herbage parameter might be a herbage length, a herbage quality or a combination thereof.
  • the herbage parameter may be estimated from head
  • the herbage parameter may advantageously be estimated from other measured data which correlates with the herbage
  • Position data of the animal may be determined by a satellite receiver of a global position system (GPS), by attaching the satellite receiver on the animal.
  • GPS global position system
  • the relative position of the animal in the field may be determined by using localization techniques in wireless sensor networks.
  • the herbage parameter is measured in the field.
  • the grass length may be measured by manual inspection or by automated stationary sensors distributed in the field or sensors arranged to move in the field on autonomous vehicle. Such sensors may be based on cameras or optical methods, e.g . laser scanning .
  • the method further comprises determining position data of the animal and using the position data in addition to the head acceleration for estimating the amount of herbage uptake.
  • Position data provides information about the spatial distribution of the animals and consequently the velocity obtained from post processing of positions.
  • positional data may be combined advantageously with other input data to improve estimation of grass uptake since positional data may contain information relating for example to the grass quality, available grass per area.
  • a grazing status may be estimated from the head acceleration, where the grazing status indicates whether the animal is grazing or not, and using the grazing status in addition to the head acceleration and/or the head movement frequency for estimating the amount of herbage uptake.
  • Estimation of herbage uptake may be improved by use of the grazing status, for example because an indication of inactive grazing simply set the herbage uptake to zero irrespective of other input data which might otherwise have indicated a certain herbage uptake.
  • one or more data may be selected from the list comprising : head acceleration, the head movement, the herbage parameter, position data of the animal, a velocity of the animal in the field, animal race, lactation stage, animal age and climate data and may be supplied as input data to a mathematical model for estimating the herbage uptake. Accordingly, a selection of different input data may be used for estimating the herbage uptake.
  • the invention in a second aspect relates to a data analysis system for determining herbage uptake in the field of an animal, the system comprising,
  • a sensor module configured to be carried by an animal for detecting a head acceleration of the animal
  • the sensor module may comprise a two-axis acceleration sensor. Whereas measurement of the acceleration in one direction may be sufficient, two-axis acceleration data may result in more accurate estimations. Alternatively, acceleration measurement in more than two directions, e.g . three directions may be used.
  • the sensor module may further comprise a transmitter for transmitting the head acceleration and/or the estimated herbage uptake to a receiver via a wireless transmission network, and the wireless network may comprise one or more relay transmitters.
  • a third aspect of the invention relates to a feeding management system
  • a feeding decision system configured to determine a supplementary amount of feed stuff to be given to the animal from the estimated amount of herbage uptake, so that the sum of the feeding value of the supplementary amount of feed stuff and the estimated herbage uptake approximates a desired total feeding value of consumed supplementary feed stuff and herbage required for efficient production.
  • the invention relates to a method and a system for estimating the feeding value or the amount of consumed herbage of grazing animals.
  • the estimated herbage uptake is based on measured and possibly estimated data which is supplied as input data to a mathematical model.
  • Measured input data may be acceleration data of the head of the animal, the length of herbage and the quality, i.e. feeding value, of herbage.
  • Estimated input data may be the frequency of the reciprocate head motion of the animal , the in-active or active grazing status of the cow, and the grass offer (e.g. length and quality)
  • the estimated data may be determined by the model and possibly provided as output data.
  • Measurements may be obtained by a sensor module carried by the animal and the measurements may be wirelessly transmitted from the sensor module to a receiver, possibly via relay transceivers.
  • a sensor module carried by the animal and the measurements may be wirelessly transmitted from the sensor module to a receiver, possibly via relay transceivers.
  • the various aspects of the invention may be combined and coupled in any way possible within the scope of the invention.
  • Fig. 1 shows a grazing cow
  • Fig. 2 shows a sensor module comprising an accelerometer and a transmitter, as well as an external central receiver,
  • Fig. 3 shows head motion acceleration curves and a corresponding head position curve
  • Fig. 4 shows an artificial neural network for estimation of herbage uptake
  • Fig. 5 shows animals in a field 500 carrying sensor modules as well as a central receiver
  • Fig. 6 shows an animal analysis system for determining herbage uptake in the field
  • Fig. 7 schematically illustrates how grazing status may be determined from neck pitch data.
  • Fig. 1 shows a grazing cow 100.
  • the head When the cow rips of grass the head generally moves back and forth along the inclined direction 101 from a recessed position 102 to a protruded position 103.
  • the protruded position 103 In the protruded position 103 the cow's tongue grips the grass and subsequently the cow pulls its head back to rip off the grass.
  • the cow is able to swallow the ripped off grass without any chewing and, thereby, without moving its jaws.
  • the invention applies to a large range of different animals including but not limited to sheep, goats, horses and pigs. Even though grass is used here as an example, the invention applies to any herbage growing in the field where the animal is eating . It is difficult to determine how much herbage e.g. grass is taken up by a cow during a given period of time. It is desirable to control the total amount of feed, i.e. the feeding value, consumed by a cow during a day including grass from the field and supplementary feed stuff given in the barn or a feeding through in the field. However, since it is difficult to control the amount of grass taken up by the cow, it becomes difficult to determine how much supplementary feed stuff that should be given to the cow.
  • a sensor 110 for detecting head acceleration during head motion 101 is carried by the cow 100.
  • the sensor 110 can detect the acceleration in a single direction 111 or both a first direction 111 and a second direction 112, preferably being perpendicular to each other.
  • the sensor 110 may be attached to the neck, the head or elsewhere on the cow so that the sensor is able to detect the motion of the head.
  • the sensor module 110 should be attached to the cow so that the single acceleration direction 111 or both the first and second 111,112 acceleration directions are parallel or substantially parallel with a plane spanned by the direction of gravity and a forward moving direction of the cow.
  • the sensor 110 comprises an accelerometer 201 which may be capable of sensing accelerations in a single direction 111 or in two directions 111, 112.
  • the sensor 110 may contain two accelerometers, one for each acceleration direction 111, 112.
  • the acceleration data generated by the accelerometer 201 may be supplied to a processor 202 for processing of the acceleration data.
  • the processed acceleration data may be supplied to a transmitter 203 for transmitting the processed acceleration data wirelessly to a receiver 210.
  • the acceleration data from the accelerometer 201 is supplied directly to the transmitter 203 without having been processed by a processor 202.
  • the receiver 210 may contain or be connected to an external processor 211 for processing of data received from the sensor 110.
  • the sensor 110 may additionally comprise a memory device 204 for temporary storage of acceleration data or processed acceleration data.
  • the accelerometer 201 may be a piezoelectric accelerometer, a fiber optic accelerometer, strain gage accelerometer or other accelerometer. Whereas it has been mentioned that one or two accelerometer devices 201 may be provided for measuring accelerations in one or two directions 111,112, this does not exclude the possibility using more accelerometers or for measuring accelerations in three or more directions, e.g. using a three-axis accelerometer.
  • the sensor 110 may comprise a temperature sensor 205, a light sensor 206 to quantify sunny and cloudy conditions and/or a rain or humidity sensor 207 to quantify the actual or accumulated amount of rain.
  • Fig. 3 shows a head movement curve 303 which illustrates motion of the head from a recessed position 102 to a protruded position 103.
  • curve 303 may illustrate a vertical coordinate 112, a horizontal coordinate 111 or a coordinate along direction 101 of the head position.
  • Fig . 3 also shows the acceleration data of head movements in the form of first head acceleration data 304 corresponding to a first acceleration direction 111 and second head acceleration data 305
  • the head movement frequency can be determined from the reciprocal value of the period T between successive peaks 310.
  • the head motion frequency can be determined from the acceleration data 111,112 in other ways e.g. by Fast Fourier analysis of the acceleration data.
  • the head movement frequency correlates with the amount of grass consumed by the cow. That is, the acceleration peaks 310 are characteristic for the grazing behavior and, therefore, detection of peaks 310 can be used for estimating the grass uptake by the cow.
  • the acceleration peaks may be distinguished from other acceleration peaks by comparing the acceleration value with a threshold and/or comparing the slope of the acceleration curve 304 or curves 304, 305 with a threshold, or the depth of the peaks 310
  • the cow may not need to chew the grass which has been ripped off, but may swallow the grass without chewing, detection of chewing movement is not useful for estimating grass uptake. Indeed, the cow chews during the rumination where when eating the cow rips off grass with minor jaw movements.
  • the grass uptake may be estimated very roughly as a function of the head movement frequency, e.g. simply by multiplying the head movement frequency with a factor.
  • the estimation of the grass uptake may be improved by use of other data than the head movement frequency.
  • the head movement frequency may be combined with the head acceleration and possibly other data.
  • the head movement frequency and possibly the head acceleration data may be provided as input data to a model, e.g. a neural network model, for estimating the grass uptake.
  • the estimation of grass uptake may be improved by combining neck pitch data, i.e. the angle of the neck of a cow, with for example the head movement frequency, e.g. by supplying head movement frequency and neck pitch as input data to a model.
  • the neck pitch can be derived directly from the acceleration data 304, 305 by calculating the inverse sine and cosine of the vertical 112 and horizontal 111 acceleration (acc.) components divided by the gravity acceleration, i.e. the pitch angle may be derived as arccosine (horizontal acc. component 111 divided by g) or arcsine (vertical acc. component 111 divided by g), where g is the gravity acceleration.
  • acceleration data it may be sufficient to simply input the acceleration data to the model instead of supplying calculated neck pitch data as input data to the model.
  • the estimation of grass uptake may also be improved by combining grazing status data with the head acceleration data 304, 305 and/or the head movement frequency.
  • the grazing status indicates whether the animal is grazing or not. Even though the head movement frequency determined from acceleration peaks may be sufficient to distinguish between active grazing and inactive grazing, combining the head movement frequency with the grazing status may improve the
  • the grazing status may be estimated from neck pitch data. That is, since during grazing the neck pitch generally varies between minus 10° (with the head down) and minus 40° (with the head upright), the grazing status may be estimated from neck pitch data.
  • a one-axis or two-axis inclination, pitch or angle sensor may possibly be added to the sensor module carried by the animal and signals from this sensor used as an input for the estimation of herbage uptake.
  • the acceleration sensor is of an alternative type, which, contrary to the main embodiment of the sensor described herein, is unable to detect a direction of gravity and/or is unable to determine horizontal from vertical direction
  • the one axis or two axis inclination sensor can be used to improve detection of grazing status.
  • any deviation of an inclination or pitch pattern relative to an expected inclination or pitch pattern may be used as an indication of a dislocated sensor and appropriate measures may be taken, such as to neglect data from an animal with such sensor until the sensor e.g . has been repositioned.
  • Fig. 7 schematically illustrates how the grazing status 702 may be determined from neck pitch data 701.
  • the grazing status 701 is set high 703 to indicate active grazing .
  • the grazing status 701 is set low 704 to indicate in-active grazing.
  • the accuracy of the estimated grass uptake could also be improved by use of an herbage parameter indicating for example the length of grass straws, the feeding value pr. kg dry matter of the grass or a combination thereof.
  • the grass length or average grass length can be measured directly in the field by manual inspection or by cameras or optical detection means mounted on robots traveling around in the field.
  • the grass quality i.e. the feeding value of the grass
  • the grass quality may be determined using spectroscopy of the grass.
  • the grass quality may be estimated from season data since generally the grass quality is high during spring periods, medium during summer and low during autumn and winter periods.
  • the grass length may also be estimated from head movement frequency, relative position data of the animal and/or time of year.
  • the grass length is measured or estimated with time intervals small enough to follow the continuous shortening of the grass length caused by the grazing, for example with time intervals shorter than 4 hours, preferably shorter than 2 hours and more preferred shorter than 1 hour.
  • the grass length between measurements may be estimated by extrapolation of previous measurements.
  • Feeding value determines the amount herbage needed for efficient milk and meat production. It is expressed as feeding value pr. kg dry matter.
  • the herbage parameter such as the herbage length or herbage quality or value may be estimated using the head movement frequency, the 10 position data of one or more cows, season data, grass mixture data or weather data, or any combination of these data.
  • the model may be capable of estimating the herbage parameter and, therefore, the model may only need to be provided with acceleration data or head frequency 15 data and possibly season and weather data. Alternatively, the model may be provided with the estimated herbage parameter.
  • Further input data may be used as input to the estimation model such as the race of animal, the lactation stage and the animal age for improving estimation of the 20 herbage uptake.
  • Fig. 4 shows a mathematical model 400 in the form of an artificial neural network
  • the neural network comprises an input layer
  • Each of the input neurons 411-415 might be connected to each of the neurons 421 of the first hidden layer 402 via connections 409, each of the neurons 421 of the first hidden layer 402 might be connected to each of the neurons 431 of the second hidden layer 403, and each the neurons 431 of the second hidden layer 403 might be
  • connection 409 may represent a multiplication, and each connection may additionally contain a weight 410 for scaling the multiplication. Note that not all connections 409 have been drawn for convenience.
  • a feedback may be applied in the artificial neural network 400 by connecting neurons of the output layer 404 to one or more of the neurons of any hidden layer 402, 403. Whereas Fig . 4 shows two layers 202,203, any number of neuron layers may be used.
  • the neurons 421, 431 in the first and second hidden layers contain a function, such as a linear or non-linear function of the input to the neuron.
  • the neural network may contain one or more input neurons 411-415. According to the previous discussion of relevant input data for estimating herbage uptake, any of these input data may be used as input to the neural network.
  • first acceleration data 304 corresponding to acceleration direction 111 may be supplied to input neuron 411.
  • second acceleration data 305 corresponding to acceleration direction 112 may be provided to another input neuron 412.
  • the head movement frequency may be supplied to a third input neuron 412.
  • data such a measured or estimated herbage parameter may additionally be supplied to a fourth neuron 414.
  • grazing status and/or position data of one or more cows or a position parameter characterizing the spread of cows may be supplied to other available input neurons to supplement other neuron inputs for estimating the herbage uptake. Still other measurements such a time of day, season, weather data, lactation stage and other data may be supplied to the neural network.
  • the parameters such as the estimated grazing status, the neck pitch, estimated herbage parameters such as grass length may not need to be supplied to input neurons 411-415 since such data may be estimated from acceleration data 304, 305 by the neural network 400.
  • such parameters may be obtained by the complex combination of neuron outputs from one or more neuron layers 402, 403 and the input layer 401.
  • Each of the output neurons may output different estimated values.
  • the first output neuron 441 may generate the estimated herbage uptake for a given cow.
  • the second output neuron 442 may output the grazing status.
  • the grazing status could be used by the farmer for monitoring the health of the cows.
  • Still other output neurons may output for example herbage parameters.
  • the actual output parameter provided by an output neuron is determined according to the training of the neural network.
  • the training may consist of adjusting weights or scaling factors 410 of connections between neurons until the actual neuron output approximates a desired output.
  • training of a neural network for estimating herbage uptake may comprise the step of comparing the calculated herbage uptake from neuron 441 with a measured herbage uptake, adjusting the a number of weights 410, and repeating the comparison and adjusting until the calculated herbage uptake approximates the measured herbage uptake.
  • the neuron network 400 only represents one possible mathematical model for estimating herbage uptake and other desired parameters.
  • other models such as linear mathematical models, probabilistic (stochastic) models or distributed models may be used.
  • Fig. 5 shows a number of animals 100 in a field 500, each of them carrying a sensor module 110.
  • the sensor modules 110 are additionally provided with relay transmitters 510 for receiving data, e.g . acceleration data or processed acceleration data, transmitted from another sensor module 110, and for transmitting the received data again.
  • the one or more relay transmitters 510 constitute part of a wireless network for transmitting data from a sensor module 110 to a central receiver 210.
  • the relay transmitter may be comprised by the sensor module 110, or the relay transmitter may be an independent unit.
  • data may be transmitted directly from a sensor module 110 to the central receiver 210.
  • the relay transmitters 510 enable large transmission distances, even by use of transmitters 203 with limited transmission range. Instead of attaching the relay transmitters to the animals, the relay transmitters may be stationary transmitters placed in the field.
  • Fig. 6 shows a data analysis system 600 for determining herbage uptake in the field.
  • the system 600 comprises the sensor module 110, a data processor 211 arranged to process the head acceleration to determine a head movement frequency of the animal and to determine an amount of herbage uptake consumed by the animal on basis of the head acceleration and head movement frequency.
  • the animal analysis system 600 comprises one or more additional processors 601.
  • the head motion frequency may be determined by a processor 211 contained by the sensor module 110 carried by the cow 100, whereas a second processor 601 may be responsible for running an algorithm corresponding to a mathematical model 602, e.g . the neural network, for estimating the herbage uptake.
  • the second processor 601 may be contained by the sensor module 110 or an external unit capable of communicating with the sensor module 110. It is understood that the system 600 may additionally comprise a transmitter 203, a central receiver 210 and relay transmitters 510 as described elsewhere. Fig. 6 also shows a feeding management system 650 comprising the data analysis system 600 and a feeding decision system 660 configured to determine a supplementary amount of feed stuff to be given to the animal from the estimated amount of herbage uptake, so that the sum of the required feed intake comprising supplementary feed stuff and the estimated herbage uptake by grazing
  • the feeding decision system 660 has an input 661 for receiving the estimated herbage uptake and an input 662 for receiving the desired total ration, so that the supplementary amount of feed stuff (concentrate and silage) can be calculated and outputted via output 663.
  • the output 663 may be a monitor output designed to show relevant data, such as the supplementary amount of feed stuff, to the user.

Abstract

L'invention porte sur un procédé et sur un système d'estimation de la valeur nutritive ou de la quantité d'herbe consommée par des animaux herbivores. La consommation d'herbe estimée est basée sur des données mesurées et éventuellement estimées qui sont fournies en tant que données d'entrée dans un modèle mathématique. Les données d'entrée mesurées peuvent être des données d'accélération de la tête de l'animal, de longueur et de qualité de l'herbe, c'est-à-dire de la valeur nutritive de l'herbe. Les données d'entrée estimées peuvent être la fréquence des mouvements de va-et-vient de la tête de l'animal et l'état inactif ou actif de pâture de la vache. En variante, les données estimées peuvent être déterminées par le modèle et éventuellement fournies en tant que données de sortie. On peut obtenir les mesures au moyen d'un module de détection porté par l'animal et on peut transmettre sans fil lesdites mesures du module de détection à un récepteur, éventuellement par l'intermédiaire d'émetteurs-récepteurs relais.
EP10796267A 2009-12-11 2010-12-10 Procédé et système d'estimation de la consommation d'herbe d'un animal Withdrawn EP2509408A1 (fr)

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