QUALITY SENSOR, COMPUTER-IMPLEMENTED METHOD OF PREDICTING INHOMOGENEITIES IN MILK EXTRACTED FROM AN ANIMAL, COMPUTER PROGRAM AND NON-VOLATILE DATA CARRIER

Abstract

A quality sensor that predicts a degree of inhomogeneities in milk extracted from an animal, by receiving a set of input variables reflecting at least one characteristic each of the animal, the extracted milk, and at least process during which milk was extracted from the animal, and by feeding the input variables into a trained artificial neural network in the quality sensor, which generates an estimate of a predicted degree of inhomogeneities in the milk of the animal.

Claims

1. A quality sensor (200) for predicting inhomogeneities in milk extracted during an extraction process, the quality sensor (200) comprising: a processor, in communication with a memory, an input interface, and an output interface, the processor being configured to implement a trained artificial neural network, and further configured to: receive a set of input variables (D.sub.IN) via the input interface, the input variables (D.sub.IN) reflecting: at least one characteristic of an animal, at least one characteristic of milk extracted from the animal, and at least one characteristic of at least one process during which the milk was extracted from the animal; feed the set of input variables (D.sub.IN) into the trained artificial neural network (210); and generate and output, from the trained artificial neural network (210) operating on the set of input variables, an estimate (D.sub.OUT) of a predicted degree of inhomogeneities in the milk of the animal.

2. The quality sensor (200) according to claim 1, wherein the trained artificial neural network (210) is a feedforward type neural network.

3. The quality sensor (200) according to claim 1, wherein the trained artificial neural network (210) has weights that have been determined via a backpropagation training process comprised of a scoring of densities of milk clot deposits on filters (410) through which extracted milk has been passed during milk extraction processes in relation to training data (D.sub.TR-IN) expressing the set of input variables, the scoring of the densities of milk clot deposits on the filters (410) expressing output training data (D.sub.TR-OUT).

4. The quality sensor (200) according to claim 3, wherein the scoring of densities of milk clot deposits on the filters (410) is based on at least one of: a visual classification of the milk clot deposits by a human assessor, an automatic image classification of the milk clot deposits by a computer-implemented algorithm, an electric impedance measurement on the filter (410) when the milk clot deposits are wet, an electric impedance measurement on the filter (410) when the milk clot deposits are dry, a wet weight of the milk clot deposits, a dry weight of the milk clot deposits, and an enzyme content in the milk clot deposits.

5. The quality sensor (200) according to claim 1, wherein the processor is configured to receive via the input interface at least one input variable of the set of input variables (D.sub.IN) from a milking apparatus used to extract milk from the animal.

6. The quality sensor (200) according to claim 1, wherein the at least one characteristic reflecting the animal comprises at least one of: at least one earlier estimate (D.sub.OUT) generated by the trained artificial neural network (210) with respect to the animal, a number of days in milk for the animal, an interval between consecutive milk extraction processes for the animal, and a parity number for the animal.

7. The quality sensor (200) according to claim 1, wherein the at least one characteristic reflecting extracted milk comprises at least one of: at least one udder quarter electric conductivity value registered during milk extraction from the animal, at least one mean udder quarter electric conductivity value for the animal determined based on milk extraction from the animal, a lactate dehydrogenase measure for the animal, a parameter expressing a color of the milk extracted from the animal, a set of parameters expressing a respective color of the milk extracted from each udder quarter of the animal, and a somatic cell count in milk extracted from the animal.

8. The quality sensor (200) according to claim 1, wherein the at least one characteristic reflecting the process during which the milk was extracted comprises at least one of: a duration of at least one milk extraction process for the animal, a milk yield from the animal in at least one milk extraction process, an expected milk yield from the animal in at least one milk extraction process, a set of parameters expressing a respective expected milk yield from each udder quarter of the animal in at least one milk extraction process, an expected rate of milk secretion in at least one milk extraction process for the animal, a set of parameters expressing a respective expected rate of milk secretion from each udder quarter of the animal in at least one milk extraction process, an udder quarter milk flow rate during a period of a milk extraction process for the animal, a peak milk flow rate during at least one milk extraction process for the animal, a set of parameters expressing a respective peak milk flow rate from each udder quarter of the animal in at least one milk extraction process, an average udder quarter milk flow rate during at least one milk extraction process for the animal, a set of parameters expressing a respective milk flow rate from each udder quarter of the animal during at least one milk extraction process, a time required for attaching teatcups to the animal's teats in connection with at least one milk extraction process, an indication whether the animal has kicked off at least one teatcup in connection with at least one milk extraction process, and an indication whether at least one udder quarter of the animal was not milked in at least one milk extraction process.

9. A method, performed by a computer programmed to implement a trained artificial neural network, for predicting inhomogeneities in milk that is extracted during an extraction process, the method comprising: receiving a set of input variables (D.sub.IN) from an input interface of the computer, the input variables (D.sub.IN) reflecting at least one characteristic of an animal, at least one characteristic of milk extracted from the animal, and at least one characteristic of at least one process during which the milk was extracted from the animal; feeding the set of input variables (D.sub.IN) into the trained artificial neural network (210); and generating and outputting, from the trained artificial neural network (210) operating on the set of input variables, an estimate (D.sub.OUT) of a predicted degree of inhomogeneities in the milk of the animal.

10. The method according to claim 9, wherein the trained artificial neural network (210) is a feedforward neural network.

11. The method according to claim 9, wherein the trained artificial neural network (210) has weights that have been determined via a backpropagation training process comprised of a scoring of densities of milk clot deposits on filters (410) through which extracted milk has been passed during milk extraction processes in relation to training data (D.sub.TR-IN) expressing the set of input variables, the scoring of the densities of milk clot deposits on the filters (410) expressing output training data (D.sub.TR-OUT).

12. The method according to claim 11, wherein the scoring of the densities of milk clot deposits on the filters (410) is based on at least one of: a visual classification of the milk clot deposits by a human assessor, an automatic image classification of the milk clot deposits by a computer-implemented algorithm, an electric impedance measurement on the filter when the milk clot deposits are wet, an electric impedance measurement on the filter when the milk clot deposits are dry, a wet weight of the milk clot deposits, a dry weight of the milk clot deposits, and an enzyme content in the milk clot deposits.

13. The method according to claim 9, further comprising: receiving at least one input variable of the set of input variables (D.sub.IN) from a milking apparatus used to extract milk from the animal.

14. The method according to claim 9, wherein the at least one characteristic reflecting the animal comprises at least one of: at least one earlier an estimate (D.sub.OUT) generated by the trained artificial neural network (210) with respect to the animal, a number of days in milk for the animal, an interval between consecutive milk extraction processes for the animal, and a parity number for the animal.

15. The method according to claim 9, wherein the at least one characteristic reflecting extracted milk comprises at least one of: at least one udder quarter electric conductivity value registered during milk extraction from the animal, at least one mean udder quarter electric conductivity value for the animal determined based on milk extraction from the animal, a lactate dehydrogenase measure for the animal, a parameter expressing a color of the milk extracted from the animal a set of parameters expressing a respective color of the milk extracted from each udder quarter of the animal, and a somatic cell count in milk extracted from the animal.

16. The method according to claim 9, wherein the at least one characteristic reflecting the process during which the milk was extracted comprises at least one of: a duration of at least one milk extraction process for the animal, a milk yield from the animal in at least one milk extraction process, an expected milk yield from the animal in at least one milk extraction process, a set of parameters expressing a respective expected milk yield from each udder quarter of the animal in at least one milk extraction process, an expected rate of milk secretion in at least one milk extraction process for the animal, a set of parameters expressing a respective expected rate of milk secretion from each udder quarter of the animal in at least one milk extraction process, an udder quarter milk flow rate during a period of a milk extraction process for the animal, a peak milk flow rate during at least one milk extraction process for the animal, a set of parameters expressing a respective peak milk flow rate from each udder quarter of the animal in at least one milk extraction process, an average udder quarter milk flow rate during at least one milk extraction process for the animal, a set of parameters expressing a respective milk flow rate from each udder quarter of the animal during at least one milk extraction process, a time required for attaching teatcups to the animal's teats in connection with at least one milk extraction process, an indication whether the animal has kicked off at least one teatcup in connection with at least one milk extraction process, and an indication whether at least one udder quarter of the animal was not milked in at least one milk extraction process.

17. A non-volatile, non-transitory data carrier (316) having stored thereon a computer program (317) readable by a processor (315), the computer program (317) comprising software that causes the processor (315) to execute the method according to claim 9 upon execution of the computer program (317) by the processor (315).

18. (canceled)

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The invention is now to be explained more closely by means of preferred embodiments, which are disclosed as examples, and with reference to the attached drawings.

[0025] FIG. 1 schematically illustrates how an artificial neural network is trained according to one embodiment of the invention;

[0026] FIG. 2 shows a functional block diagram of a quality sensor according to one embodiment of the invention;

[0027] FIG. 3 shows a structural block diagram of the quality sensor according to one embodiment of the invention;

[0028] FIGS. 4a-f illustrate how filters may be employed for scoring different densities of milk clot deposits according to one embodiment of the invention; and

[0029] FIG. 5 illustrates, by means of a flow diagram, the method according to a preferred embodiment of the invention.

DETAILED DESCRIPTION

[0030] In FIG. 1, we see a setup for training an artificial neural network (ANN) 110 to be included in a quality sensor 200 according to the invention after that the ANN 110 has been trained.

[0031] FIG. 2 shows a functional block diagram of the quality sensor 200 according to one embodiment of the invention, which quality sensor 200 includes the trained ANN 210, i.e. a version of the ANN that has been modified via training as described below.

[0032] The setup of FIG. 1 includes an ANN 110, which initially is untrained, i.e. a network structure where a respective bias b.sub.1, b.sub.2, . . . , b.sub.n of each node/neuron has been randomly assigned and a respective weight w.sub.1, . . . , w.sub.ij of each connection between the nodes/neurons has likewise been randomly assigned. The setup of FIG. 1 further includes an evaluation unit 120 configured to determine if an output D.sub.OUT of the ANN 110 is sufficiently close to an actual output D.sub.TR-OUT that is linked to a set of training data D.sub.IN-TR fed into the ANN 110. Here, the ANN output D.sub.OUT is a prediction of the actual output D.sub.TR-OUT. Ideally, a difference between the ANN output D.sub.OUT and the actual output D.sub.TR-OUT should be sufficiently small, however not zero. Namely, this may result in so-called overtraining and render the trained ANN 210 poorly suited to handle input data being only slightly different from the sets of training data D.sub.IN-TR used. If the difference between the ANN output D.sub.OUT and the actual output D.sub.TR-OUT is not yet sufficiently small, i.e. the difference is larger than an error threshold e.sub.th, the evaluation unit 120 is configured to generate a set of adjustment parameters {P}, which causes one or more of the weights w.sub.1, . . . , w.sub.ij to be modified to a respective higher or lower value.

[0033] The ANN 110 is preferably of feedforward type, and may, in its most generic form, be implemented as a directed acyclic graph, i.e. a multilayer network where one or more edges skip layers. According to one embodiment of the invention, the ANN 110 is a standard multilayer perceptron (MLP) network including an input layer IL, a single hidden layer HL and an output layer OL. Naturally, any higher number than one hidden layers HLs is likewise conceivable according to the invention.

[0034] Preferably, if the ANN 110 is arranged to generate a binary output 0 or 1 respectively via the output layer OL, the ANN 110 is configured to apply a sigmoid activation function, where a resulting value larger than or equal to 0.5 produces the output 1 and a resulting value smaller than 0.5 produces the output 0. This corresponds to an estimate D.sub.OUT of a predicted degree of inhomogeneities in the milk, where 0 represents no inhomogeneities/clots in the milk and 1 represents the occurrence of inhomogeneities/clots in the milk.

[0035] However, as will be discussed below, according to the invention, estimates D.sub.OUT in any form of multi-level outputs from the ANN are equally well conceivable, i.e. more nuanced estimates of the predicted degree of inhomogeneities in the milk.

[0036] In addition, the estimate D.sub.OUT generated by the ANN may be a vector containing a plurality of variables, for example one variable per udder quarter. Thus, the output from the ANN may have a format {Y1,Y2,Y3,Y4}, where Y1 designates a predicted degree of inhomogeneities in the milk from a first udder quarter, Y2 designates a predicted degree of inhomogeneities in the milk from a second udder quarter, Y3 designates a predicted degree of inhomogeneities in the milk from a third udder quarter and Y4 designates a predicted degree of inhomogeneities in the milk from a fourth udder quarter. For example, here, an output {0,0,1,0} could represent inhomogeneities/clots in the milk from the third udder quarter and no inhomogeneities/clots in the milk from the other udder quarters. Naturally, according to the invention such a vector-format output may also be combined with a multi-level output per variable.

[0037] According to the invention, the ANN 110 is trained with training data D.sub.TR-IN reflecting a set of variables of the same type as a set of input variables D.sub.IN that will be fed to the trained ANN 210 included in the quality sensor 200 during operation thereof.

[0038] Referring now to FIGS. 4a to 4f, we see illustrations of how a filter 410 may be employed according to one embodiment of the invention to score inhomogeneities in the form different densities of milk clot deposits. According to one embodiment of the invention, the trained artificial neural network 210 of the quality sensor 200 has weights w.sub.1, . . . w.sub.ij that have been determined via a backpropagation training process involving scoring of different densities of milk clots on the filter 410. The filter 410 is mounted in a frame 400, which, in turn, is placed in a holder in a milk conduit where milk is transported in such a manner that extracted milk passes through the filter 410 during a milk extraction process. For example, the holder for the filter 400 may be arranged along a milk tube, preferably before a respective milk meter of each teat cup.

[0039] FIGS. 4a to 4f show the filter 410 after that extracted milk with different degrees of inhomogeneities has been passed through the filter 410.

[0040] In FIG. 4a, there is no sign of clots on the filter 410. The extracted milk is therefore assigned a scoring SC0 designating no clots, or a lowest possible degree of inhomogeneities in the milk.

[0041] In FIG. 4b, a fraction of a clot 411 is identified on the filter 410, and the extracted milk is assigned a scoring SC1 designating a lowest level of inhomogeneities being present in the milk. For example, the scoring SC1 may represent an aggregate area of deposits on the filter 410 equivalent to a single clot having a diameter smaller than 3 mm.

[0042] In FIG. 4c, a mild amount of clots 412 is identified on the filter 410, and the extracted milk is assigned a scoring SC2 designating a second lowest level of inhomogeneities being present in the milk. For example, the scoring SC2 may represent an aggregate area of deposits on the filter 410 equivalent to a single clot having a diameter larger than 3 mm and smaller than 5 mm.

[0043] In FIG. 4d, a moderate amount of clots 413 is identified on the filter 410, and the extracted milk is assigned a scoring SC3 designating a third lowest level of inhomogeneities being present in the milk. For example, the scoring SC3 may represent an aggregate area of deposits on the filter 410 equivalent to a single clot having a diameter larger than 5 mm and covering less than or equal to 10% of a total area of the filter 410.

[0044] In FIG. 4e, the filter 410 contains a heavy amount of clots 414, and the extracted milk is assigned a scoring SC4 designating a second highest level of inhomogeneities being present in the milk, which is above the third lowest level. For example, the scoring SC4 may represent an aggregate area of deposits on the filter 410 covering more than 10% and less than 50% of the total area of the filter 410.

[0045] In FIG. 4f, the filter 410 contains a very heavy assemble of clots 415, and the extracted milk is assigned a scoring SC5 designating a highest level of inhomogeneities being present in the milk. For example, the scoring SC5 may represent an aggregate area of deposits on the filter 410 covering more than 50% of the total area of the filter 410.

[0046] For each set of training data D.sub.IN-TR, a respective scoring SC0, SC1, SC2, SC3, SC4 or SC5 is associated to express output training data D.sub.TR-OUT linked thereto. Based on this, the ANN 110 is trained as described above such that it is transformed into the trained ANN 210.

[0047] The scorings SC0, SC1, SC2, SC3, SC4 or SC5, in turn, may be derived in various ways. According to one embodiment of the invention, a particular scoring SC0, SC1, SC2, SC3, SC4 or SC5 of a density of milk clot deposits 411, 412, 413, 414 and 415 respectively on the filter 410 is based on one or more of the following: [0048] a visual classification of the milk clot deposits 411, 412, 413, 414 and 415 respectively made by at least one human assessor, for example a veterinarian, [0049] an automatic image classification of the milk clot deposits 411, 412, 413, 414 and 415 respectively performed by a computer-implemented algorithm, for instance based on artificial intelligence (AI), [0050] an electric impedance measurement on the filter 410 when the milk clot deposits 411, 412, 413, 414 and 415 respectively are wet, [0051] an electric impedance measurement on the filter 410 when the milk clot deposits 411, 412, 413, 414 and 415 respectively are dry, [0052] a wet weight of the milk clot deposits 411, 412, 413, 414 and 415 respectively, [0053] a dry weight of the milk clot deposits 411, 412, 413, 414 and 415 respectively, and [0054] an enzyme content in the milk clot deposits 411, 412, 413, 414 and 415 respectively, which enzyme content may indicate an amount of leukocytes.

[0055] According to the invention, the set of training data D.sub.IN-TR reflects at least one characteristic of the animal whose milk is to serve as a basis for training the ANN 110 in relation to the output training data D.sub.TR-OUT expressed by the scorings SC0, SC1, SC2, SC3, SC4 and SC5.

[0056] According to the invention, the set of training data D.sub.IN-TR also reflects at least one characteristic of the extracted milk serving as a basis for training the ANN 110 in relation to the output training data D.sub.TR-OUT expressed by the scorings SC0, SC1, SC2, SC3, SC4 and SC5.

[0057] Additionally, according to the invention, the set of training data D.sub.IN-TR reflects at least one characteristic of at least one process during which milk was extracted from the animal serving as a basis for training the ANN 110 in relation to the output training data D.sub.TR-OUT expressed by the scorings SC0, SC1, SC2, SC3, SC4 and SC5.

[0058] The set of training data D.sub.IN-TR consequently reflects the same type of variables as the set of input variables D.sub.IN that will be fed to the trained ANN 210 included in the quality sensor 200 to generate an estimate D.sub.OUT of a predicted degree of inhomogeneities in the milk.

[0059] The at least one characteristic of the animal in the set of training data D.sub.IN-TR has been registered before the milking is initiated based upon which the scorings SC0, SC1, SC2, SC3, SC4 and SC5 are assigned. The at least one characteristic of the animal in the set of training data D.sub.IN-TR may, according to embodiments of the invention relate to: a number of days in milk for the animal, an interval between consecutive milk extraction processes for the animal and/or a parity number for the animal.

[0060] The at least one characteristic of the extracted milk in the set of training data D.sub.IN-TR may either have been registered before the milking is initiated, and thus relate to one or more earlier milkings, and/or be registered during the milking based upon which the scorings SC0, SC1, SC2, SC3, SC4 and SC5 are assigned. According to embodiments of the invention, the at least one characteristic of the extracted milk may relate to: at least one udder quarter electric conductivity value registered during milk extraction from the animal, at least one mean udder quarter electric conductivity value for the animal determined based on milk extraction from the animal, an LDH measure for the animal, a parameter expressing a color of the milk extracted from the animal, a set of parameters expressing a respective color of the milk extracted from each udder quarter of the animal, and/or a somatic cell count in milk extracted from the animal.

[0061] Analogously, the at least one characteristic of at least one process during which milk was extracted from the animal included in the set of training data D.sub.IN-TR may either have been registered before the milking is initiated, and thus relate to one or more earlier milkings, and/or be registered during the milking based upon which the scorings SC0, SC1, SC2, SC3, SC4 and SC5 are assigned. According to embodiments of the invention, the at least one characteristic of at least one process during which milk was extracted from the animal may relate to: a duration of at least one milk extraction process for the animal, a milk yield from the animal in at least one milk extraction process, an expected milk yield from the animal in at least one milk extraction process, a set of parameters expressing a respective expected milk yield from each udder quarter of the animal in at least one milk extraction process, an expected rate of milk secretion in at least one milk extraction process for the animal, a set of parameters expressing a respective expected rate of milk secretion from each udder quarter of the animal in at least one milk extraction process, an udder quarter milk flow rate during a period of a milk extraction process for the animal, a peak milk flow rate during at least one milk extraction process for the animal, a set of parameters expressing a respective peak milk flow rate from each udder quarter of the animal in at least one milk extraction process, an average udder quarter milk flow rate during at least one milk extraction process for the animal, a set of parameters expressing a respective milk flow rate from each udder quarter of the animal during at least one milk extraction process, a time required for attaching teatcups to the animal's teats in connection with at least one milk extraction process, an indication whether the animal has kicked off at least one teatcup in connection with at least one milk extraction process, and/or an indication whether at least one udder quarter of the animal was not milked in at least one milk extraction process.

[0062] Before entering the set of training data D.sub.IN-TR into the ANN 110, it may be necessary to adjust and/or transform one or more pieces of data in order to render the data useful for training. For example, numerical variables may need to be normalized. Furthermore, one or more variables may need to be transferred to the logarithmic domain.

[0063] Similarly, this type of adjustments and/or transformations may be needed when operating the quality sensor 200 and receiving the set of input variables D.sub.IN. Therefore, the quality sensor 200 preferably contains an input interface 205 configured to adjust and/or transform at least one variable in the set of input variables D.sub.IN before feeding the set of input variables D.sub.IN into the trained artificial neural network 210.

[0064] According to the invention, in operation, the quality sensor 200 is configured to receive the set of input variables D.sub.IN, which reflect at least one characteristic of the animal to be milked, that is being milked; at least one characteristic of the extracted milk, and at least one characteristic of at least process during which milk was extracted from the animal, either in the present milking, in at least one previous milking, or both.

[0065] The quality sensor 200 is further configured to feed the set of input variables D.sub.IN into the trained ANN 210. In response thereto, the trained ANN 210 is configured to generate an estimate D.sub.OUT of a predicted degree of inhomogeneities in the milk, for example as a first output 0 representing no inhomogeneities/no clots, and a second output 1 representing an occurrence of inhomogeneities/clots.

[0066] Here, the scores SC0, SC1 and SC2 may for example be translated into the first output 0, and the scores SC3, SC4 and SC5 may be translated into the second output 1. However, technically, any other translation scheme is possible according to the invention. It is also feasible to generate an estimate D.sub.OUT of the predicted degree of inhomogeneities in the milk that reflects three or more different degrees of inhomogeneities, say one degree corresponding to each of the six levels represented by the scores SC0, SC1, SC2, SC3, SC4 and SC5 respectively. Nevertheless, in practice, a binary output is deemed to be the most useful tool to determine whether or not the milk fulfills a particular quality standard.

[0067] According to one embodiment of the invention, the quality sensor 200 contains the above input interface 205, which is further configured to receive at least one input variable in the set of input variables D.sub.IN from the milking apparatus that was used to extract the milk from the animal.

[0068] Moreover, the quality sensor 200 may contain an output interface 215 configured to provide the estimate D.sub.OUT to external units and/or present the estimate D.sub.OUT to a human operator.

[0069] FIG. 3 shows a block diagram of the quality sensor 200 according to one embodiment of the invention. It is generally advantageous if the quality sensor 200 is configured to effect the above-described procedure in an automatic manner by executing a computer program 317. Therefore, the quality sensor 200 may include a memory unit 316, i.e. non-volatile data carrier, storing the computer program 317, which, in turn, contains software for making processing circuitry in the form of at least one processor 315 in the quality sensor 200 execute the actions mentioned in this disclosure when the computer program 317 is run on the at least one processor 315.

[0070] In order to sum up, and with reference to the flow diagram in FIG. 5, we will now describe the computer-implemented method according to the invention of predicting inhomogeneities in milk extracted from an animal.

[0071] A first step 510, checks if a set of input variables D.sub.IN has been received. The set of input variables D.sub.IN reflects at least one characteristic of the animal, at least one characteristic of the extracted milk, and at least one characteristic of at least one process during which milk was extracted from the animal.

[0072] The at least one characteristic of the animal has been registered before the milking is initiated, and may, according to embodiments of the invention relate to: at least one earlier estimate of predicted inhomogeneities in milk extracted from the animal generated by the trained artificial neural network, a number of days in milk for the animal, an interval between consecutive milk extraction processes for the animal, and/or a parity number for the animal.

[0073] The at least one characteristic of the extracted milk may either have been registered before the milking is initiated, and thus relate to one or more earlier milkings, and/or be registered during the milking in which the inhomogeneities are to be predicted. According to embodiments of the invention, the at least one characteristic of the extracted milk may relate to: at least one udder quarter electric conductivity value registered during milk extraction from the animal, at least one mean udder quarter electric conductivity value for the animal determined based on milk extraction from the animal, an LDH measure for the animal, a parameter expressing a color of the milk extracted from the animal, a set of parameters expressing a respective color of the milk extracted from each udder quarter of the animal, and/or a somatic cell count in milk extracted from the animal.

[0074] Analogous to the above, the at least one characteristic of at least one process during which milk was extracted from the animal may either have been registered before the milking is initiated, and thus relate to one or more earlier milkings, and/or be registered during the milking in which the inhomogeneities are to be predicted. According to embodiments of the invention, the at least one characteristic of at least one process during which milk was extracted from the animal may relate to: a duration of at least one milk extraction process for the animal, a milk yield from the animal in at least one milk extraction process, an expected milk yield from the animal in at least one milk extraction process, a set of parameters expressing a respective expected milk yield from each udder quarter of the animal in at least one milk extraction process, an expected rate of milk secretion in at least one milk extraction process for the animal, a set of parameters expressing a respective expected rate of milk secretion from each udder quarter of the animal in at least one milk extraction process, an udder quarter milk flow rate during a period of a milk extraction process for the animal, a peak milk flow rate during at least one milk extraction process for the animal, a set of parameters expressing a respective peak milk flow rate from each udder quarter of the animal in at least one milk extraction process, an average udder quarter milk flow rate during at least one milk extraction process for the animal, a set of parameters expressing a respective milk flow rate from each udder quarter of the animal during at least one milk extraction process, a time required for attaching teatcups to the animal's teats in connection with at least one milk extraction process, an indication whether the animal has kicked off at least one teatcup in connection with at least one milk extraction process, and/or an indication whether at least one udder quarter of the animal was not milked in at least one milk extraction process.

[0075] If, in step 510, it is found that the set of input variables D.sub.IN has been received, a step 520 follows. Otherwise, the procedure loops back and stays in step 510.

[0076] In step 520, the set of input variables D.sub.IN is fed into the above-described trained artificial neural network.

[0077] Subsequently, in response the set of input variables D.sub.IN the trained artificial neural network generates an estimate D.sub.OUT of a predicted degree of inhomogeneities in the milk in a step 530.

[0078] Thereafter, the procedure ends.

[0079] All of the process steps, as well as any sub-sequence of steps, described with reference to FIG. 5 may be controlled by means of a programmed processor. Moreover, although the embodiments of the invention described above with reference to the drawings comprise processor and processes performed in at least one processor, the invention thus also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the invention into practice. The program may be in the form of source code, object code, a code intermediate source and object code such as in partially compiled form, or in any other form suitable for use in the implementation of the process according to the invention. The program may either be a part of an operating system, or be a separate application. The carrier may be any entity or device capable of carrying the program. For example, the carrier may comprise a storage medium, such as a Flash memory, a ROM (Read Only Memory), for example a DVD (Digital Video/Versatile Disk), a CD (Compact Disc) or a semiconductor ROM, an EPROM (Erasable Programmable Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), or a magnetic recording medium, for example a floppy disc or hard disc. Further, the carrier may be a transmissible carrier such as an electrical or optical signal which may be conveyed via electrical or optical cable or by radio or by other means. When the program is embodied in a signal, which may be conveyed, directly by a cable or other device or means, the carrier may be constituted by such cable or device or means. Alternatively, the carrier may be an integrated circuit in which the program is embedded, the integrated circuit being adapted for performing, or for use in the performance of, the relevant processes.

[0080] Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0081] The term comprises/comprising when used in this specification is taken to specify the presence of stated features, integers, steps or components. The term does not preclude the presence or addition of one or more additional elements, features, integers, steps or components or groups thereof. The indefinite article a or an does not exclude a plurality. In the claims, the word or is not to be interpreted as an exclusive or (sometimes referred to as XOR). On the contrary, expressions such as A or B covers all the cases A and not B, B and not A and A and B, unless otherwise indicated. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be construed as limiting the scope.

[0082] It is also to be noted that features from the various embodiments described herein may freely be combined, unless it is explicitly stated that such a combination would be unsuitable.

[0083] The invention is not restricted to the described embodiments in the figures, but may be varied freely within the scope of the claims.