Quality sensor, computer-implemented method of predicting inhomogeneities in milk extracted from an animal, computer program and non-volatile data carrier
AssigneeDeLaval Holding AB
InventorsDorota Anglart, Charlotte Hallén Sandgren
Life of this patent
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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.
Claims15
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 (DIN) via the input interface, the input variables (DIN) 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 (DIN) 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 (DOUT) of a predicted degree of inhomogeneities in the milk of the animal,
- 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 (DTR-IN) expressing the set of input variables, the scoring of the densities of milk clot deposits on the filters (410) expressing output training data (DTR-OUT).
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 (DIN) from an input interface of the computer, the input variables (DIN) 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 (DIN) 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 (DOUT) of a predicted degree of inhomogeneities in the milk of the animal,
- 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 (DTR-IN) expressing the set of input variables, the scoring of the densities of milk clot deposits on the filters (410) expressing output training data (DTR-OUT).
Dependencies are inferred from the claim text.