The present invention relates to a method for controlling devices within an agricultural network system via a network bus such as a physical network bus or a logical network bus, wherein the devices are controlled by means of control information which is transmitted to the devices by means of messages comprising a content and a header title, the method comprising steps for:—determining a function for performing thereof by a device in the network,—determining the content of a message to be transmitted,—compiling header title information for inclusion thereof in a header title for the message to be transmitted, wherein—the header title information comprises a predetermined number of data fields, such as preferably a network identifier, a device type designation, a function designation, a location designation and/or a group designation, for the purpose of determining on the basis of compliance with one or more of these data fields which device or devices receive(s) the message.

A method for use with a multibody system includes two body sections assembled via a joint, one of the body sections including a camera which is moveable between a first position and a second position, includes detecting a reference object with the camera situated in the first position, determining a reference value of the reference object, requesting movement of the camera from the first position into the second position, initiating movement of the camera from the first position towards the second position, while iteratively sampling image of the reference object, determining a current object value of the reference object, based on the sampled image, matching the reference value with the current object value, and determining successfulness of the camera movement, based on the matching.

A method for use with a multibody system includes two body sections assembled via a joint, one of the body sections including a camera which is moveable between a first position and a second position, includes detecting a reference object with the camera situated in the first position, determining a reference value of the reference object, requesting movement of the camera from the first position into the second position, initiating movement of the camera from the first position towards the second position, while iteratively sampling image of the reference object, determining a current object value of the reference object, based on the sampled image, matching the reference value with the current object value, and determining successfulness of the camera movement, based on the matching.

An automatic milking system including an electronic control unit, a milking station, a production milk tank, a second milk tank, an animal identification system, a first sensor to measure at least one indicator of illness of an animal, a data memory in which measurements are gathered. The control unit configured to register a first point of time at which milk from an animal is diverted to the second tank instead of being conducted to the production milk tank, register a second point of time at which milk from the animal is shifted from being conducted to the second tank to being conducted to the production milk tank, and, when the time period between the first point of time and the second point of time is within a predetermined number days, the control unit determines that the animal is in an illness state.

An automatic milking system including an electronic control unit, a milking station, a production milk tank, a second milk tank, an animal identification system, a first sensor to measure at least one indicator of illness of an animal, a data memory in which measurements are gathered. The control unit configured to register a first point of time at which milk from an animal is diverted to the second tank instead of being conducted to the production milk tank, register a second point of time at which milk from the animal is shifted from being conducted to the second tank to being conducted to the production milk tank, and, when the time period between the first point of time and the second point of time is within a predetermined number days, the control unit determines that the animal is in an illness state.

Systems and methods for detecting udder disease based on machine learning methods and complementary supporting techniques are presented. Included are methods for assembling time sequences of images of each animal of a herd or set for subsequent use in per-animal image analysis for disease detection. Methods presented also include image pre-processing methods used prior to image analysis, resulting in contrast and resolution optimization such as appropriate image intensity level adjustment and resolution downsampling for more rapid and more accurate disease detection. Combinatorial techniques for compositing whole-udder images or udder-quarter images from partial images captures are described. Methods are provided for power usage optimization in regard to computing resources used in the computing-intensive AI analysis methods. Location-based and animal history-based detection refinements are incorporated into described systems. Further presented are methods for multi-modal and multi-factor detection of udder disease, as well as methods for infection type classification.

Systems and methods for detecting udder disease based on machine learning methods and complementary supporting techniques are presented. Included are methods for assembling time sequences of images of each animal of a herd or set for subsequent use in per-animal image analysis for disease detection. Methods presented also include image pre-processing methods used prior to image analysis, resulting in contrast and resolution optimization such as appropriate image intensity level adjustment and resolution downsampling for more rapid and more accurate disease detection. Combinatorial techniques for compositing whole-udder images or udder-quarter images from partial images captures are described. Methods are provided for power usage optimization in regard to computing resources used in the computing-intensive AI analysis methods. Location-based and animal history-based detection refinements are incorporated into described systems. Further presented are methods for multi-modal and multi-factor detection of udder disease, as well as methods for infection type classification.

Systems and methods for analysing milk are described. A plurality of First Type Sensors (FTPs) are provided, each associated with a respective one of a plurality of Milking Clusters (MKs) of a milking system and configured to analyse milk extracted from an animal by the MK to determine at least one FTP value of a parameter of the milk across an event period. At least one of a Second Type Sensor (STP) associated with at least one of the plurality of MKs is configured to analyse the milk to determine at least one STP value of the parameter within the event period, wherein the STP is less susceptible to animal specific bias than the FTP. An Animal Specific Bias Correction (ASBC) is determined based on the at least one FTP value and the at least one STP value and applied to FTP values for milk extracted from the animal.

Systems and methods for analysing milk are described. A plurality of First Type Sensors (FTPs) are provided, each associated with a respective one of a plurality of Milking Clusters (MKs) of a milking system and configured to analyse milk extracted from an animal by the MK to determine at least one FTP value of a parameter of the milk across an event period. At least one of a Second Type Sensor (STP) associated with at least one of the plurality of MKs is configured to analyse the milk to determine at least one STP value of the parameter within the event period, wherein the STP is less susceptible to animal specific bias than the FTP. An Animal Specific Bias Correction (ASBC) is determined based on the at least one FTP value and the at least one STP value and applied to FTP values for milk extracted from the animal.

The present invention relates to preparing dairy animal teats for being milked, and more particularly to teat preparation, rinsing, and milking that all take place in a milking machine teat cup liner. Such a method includes: applying a teat sanitizer to the teat; injecting air into the liner to force the teat sanitizer toward a waste milk line connected to the liner; pulsating the liner at a first pulsation rate; drawing a rinsing milk from the teat; directing the teat sanitizer, the air, and the rinsing fluid to the waste milk line; pulsating the liner at a second pulsation rate to draw additional milk from the teat; and directing the additional milk to a good milk line.