WO2022115916A1 - Surveillance et gestion de bétail - Google Patents

Surveillance et gestion de bétail Download PDF

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Publication number
WO2022115916A1
WO2022115916A1 PCT/AU2021/051445 AU2021051445W WO2022115916A1 WO 2022115916 A1 WO2022115916 A1 WO 2022115916A1 AU 2021051445 W AU2021051445 W AU 2021051445W WO 2022115916 A1 WO2022115916 A1 WO 2022115916A1
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WO
WIPO (PCT)
Prior art keywords
animal
asset
rumination
farm
pasture
Prior art date
Application number
PCT/AU2021/051445
Other languages
English (en)
Inventor
Krishnakumar SANTHANAM
Xin Zhang
Venkateswaran SANTHANAM
Original Assignee
Finchain.Ai Pty Ltd
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
Priority claimed from AU2020904493A external-priority patent/AU2020904493A0/en
Application filed by Finchain.Ai Pty Ltd filed Critical Finchain.Ai Pty Ltd
Priority to AU2021390589A priority Critical patent/AU2021390589A1/en
Publication of WO2022115916A1 publication Critical patent/WO2022115916A1/fr

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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
    • A01K29/00—Other apparatus for animal husbandry
    • A01K29/005—Monitoring or measuring activity, e.g. detecting heat or mating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1113—Local tracking of patients, e.g. in a hospital or private home
    • A61B5/1114—Tracking parts of the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61D—VETERINARY INSTRUMENTS, IMPLEMENTS, TOOLS, OR METHODS
    • A61D19/00—Instruments or methods for reproduction or fertilisation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61D—VETERINARY INSTRUMENTS, IMPLEMENTS, TOOLS, OR METHODS
    • A61D99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • 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
    • A01K11/00—Marking of animals
    • A01K11/006—Automatic identification systems for animals, e.g. electronic devices, transponders for animals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0204—Acoustic sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management

Definitions

  • the present invention relates to the field of agriculture.
  • the invention relates to the monitoring of the physiological condition of farm animals. In another form, the invention relates to the efficient use of farm resources.
  • Farm management involves optimising the use of farm resources and ensuring the wellbeing of farm animals.
  • the wellbeing of farm animals is vitally important in terms of animal produce and reproduction.
  • Certain systems have been developed to assist farmers with monitoring the animals.
  • problems with existing automated or monitored systems are many.
  • BRESs Bovine Rumination and Oestrus Prediction Systems
  • BRESs may track specific bovine assets, but in existing BRESs, a bovine asset with a specific ID may disassociate the ID with that asset when the bovine walks into a milk station meant for different bovine asset.
  • An object of the present invention is to provide methodologies for the efficient management of farm animals.
  • the present invention relates to the efficient management of ruminant farm animals.
  • a further object of the present invention is to alleviate at least one disadvantage associated with the related art.
  • a method and system adapted to monitor oestrus of a farm animal comprises the steps of: monitoring one or more physiological characteristics of the farm animal, wherein the one or more physiological characteristics are selected from: duration of rumination (in ruminant animals), animal movement/activity and vocal sounds; detecting a change in one or more of the physiological characteristics; and associating the change in one or more of the physiological characteristics with onset of oestrus.
  • a method and system adapted to monitor a body condition score (BCS) of a farm animal comprises the steps of: determining one or more physical dimensions of the farm animal; providing the one or more physical dimensions to a computer system; using the computer system to predict a BCS of the farm animal.
  • a method and system adapted to determine energy content of a pasture comprises the steps of: determining one or more physical dimensions of a sample of the pasture; providing the one or more physical dimensions to a computer system; and using the computer system to predict an energy content of the pasture.
  • Figure 1 shows a schematic diagram of the arrangement of an embodiment of a farm animal monitoring system of the present invention (1 A) and a schematic diagram illustrating the functional operations of an embodiment of a system according to the present invention (1 B).
  • Figure 2 is a schematic representation of a machine learning process as used in certain embodiments of the present invention.
  • Figure 3 is a schematic representation of a process according to certain embodiments of the present invention.
  • Figure 4 illustrates the features used in the manual determination of the BCS of a cow.
  • Figure 5 shows a series of screen shots from a mobile phone application that embodies the presently described invention.
  • Figure 6 shows an exemplary grazing schedule generated according to the present invention.
  • Figure 7 shows a series of screen shots from a mobile phone application that embodies the presently described invention.
  • the present invention relates to agriculture insofar as it relates to the efficient management of farm resources and farm animals.
  • the present invention relates to the efficient management of ruminant farm animals, including, but not limited to: bovines (including dairy cows), sheep, goats and deer.
  • bovines including dairy cows
  • the present invention is described with particular focus on bovines.
  • the invention is not to be so limited, the methodologies of the present invention are broadly applicable to farm animals and in particular, to ruminant farm animals.
  • the present invention provides a farm management system comprising: an animal asset monitoring system, wherein the animal asset monitoring system determines a body condition score (BCS) of an animal asset, and/or detects oestrus in the animal asset; and/or determines animal product production; and a pasture energy content determining system; wherein the farm management system correlates a BCS with animal product production and/or oestrus to inform a pasture grazing protocol maximise animal product production.
  • the animal product is selected from milk and meat.
  • the animal asset monitoring system comprises any one or more of: a rumination sensor to detect a daily animal asset rumination duration; a motion sensor to detect a daily animal asset motion; a rumination aberration detection module to detect when the daily animal asset rumination duration diverges from a known animal rumination pattern; and a motion aberration detection module to detect when the daily animal asset motion diverges from a known animal motion pattern.
  • the rumination sensor is an accelerometer configured to detect jaw motion; and/or the motion aberration detection module is configured to use dead reckoning to determine a daily motion pattern.
  • the rumination aberration detection module is configured to identify when the daily animal asset rumination duration falls below a minimum rumination value or exceeds a maximum rumination value; optionally wherein the minimum rumination value may be approximately 450 minutes per day, and the maximum rumination value may be 500 minutes per day.
  • the animal asset monitoring system of the present invention further comprises: a posture sensor to detect a daily animal posture; and a posture aberration detection module to detect when the daily animal posture diverges from a known animal posture pattern.
  • the posture sensor comprises an accelerometer configured to detect animal posture by detecting gravitational acceleration; or the posture sensor comprises a gyroscope configured to detect changes in animal posture by detecting gyroscopic rotation.
  • the animal asset monitoring system of the present invention further comprises a microphone.
  • the microphone is used to detect vocalization by the animal asset.
  • the animal asset monitoring system of the present invention may further comprise: a data storage module to record data from the sensors and/or microphone; and a wireless communication module to communicate recorded data to a computer system.
  • the present invention provides methodologies for monitoring individual animals in a herd.
  • individual animals in a herd may be fitted with one or more sensors.
  • the sensors comprise gyro-sensors and/or accelerometers and/or microphones.
  • Gyro-sensors and accelerometers may be used for measuring orientation and/or angular velocity of the animal to which they are fitted.
  • One exemplary monitor that is suitable for use in the present invention is the CowScout, manufactured by GEA. Such measurement may allow the detection of movement in up to three-dimensions.
  • Microphones may be used for measuring sound produced by the animal to which they are fitted.
  • one or more sensors may be attached to a collar worn around the neck of a farm animal.
  • one or more sensors may be attached to a strap.
  • the strap may be attached by any suitable means to any suitable part of a farm animal.
  • a strap may be attached to a front or hind leg of the farm animal in order to monitor leg movement; or the strap could be attached to, or near, the tail of a pregnant animal in order to detect imminent delivery.
  • the one or more sensors are used to monitor physiological characteristics such as rumination, animal movement/activity and vocal sounds.
  • the physiological characteristics may relate to duration of rumination; duration and type of activities, including, but not limited to: sitting, standing, walking, galloping, sprinting, eating, sleeping and running; and vocal information.
  • the one or more sensors may be adapted to generate an electrical signal upon activation by movement or sound of the farm animal.
  • the electrical signals comprise data concerning the physiological characteristic being detected or monitored by a respective sensor.
  • Data produced by the one or more sensors may be analysed to determine certain conditions relating to each individual animal.
  • the data is analysed in real-time.
  • each sensor is functionally linked to a transmission unit, wherein the transmission unit is adapted to transfer data from the one or more sensors to a computer system.
  • each sensor is functionally linked to a single transmission unit for each monitored animal.
  • each sensor may include its own transmission unit.
  • each farm animal, to which sensors are fitted has a unique identifier that is transmitted together with the data, whereby the data may be assigned to a specific animal for analysis.
  • Power for each sensor and transmission unit may be provided by any suitable means including, but not limited to, a battery, a rechargeable battery, a photovoltaic cell, or a combination thereof.
  • replaceable batteries that last a month may be used.
  • the batteries are preferably physically separated from the one or more sensors and transmission so that it they may be easily replaced, as required.
  • a single battery or battery pack may be used to power more than one sensor and/or transmission unit on a single farm animal.
  • more than one battery or battery pack may be used to power all of the sensors and transmission units on a single farm animal.
  • photovoltaic cells may be used to one or more sensors and/or transmission units on a single farm animal.
  • Data transfer to the computer system may be continuous or may be performed at specific times or intervals. For example, data transfer may occur at specific times every day, such as 0600, 0900, 1200, 1500 and 1800hrs, or data transfer may occur at regular intervals of time such as 1 hr, 2hr, 4hr, 6hr, 10hr, 12hr, 24hr or 36hr. It will be recognised that these are simply exemplary times and intervals and that the specific times and/or intervals may be adjusted by the skilled person, without inventive input, to suit the farm animals being monitored and the farm regimen. Data transfer is most conveniently facilitated or achieved by wireless transmission by any suitable means including, but not limited to: near-field-communication, Wi-Fi, Bluetooth or mobile telephone network.
  • the one or more sensors may be used to monitor a number of physiological characteristics of the animal.
  • data is collected from a dairy cow.
  • certain physiological conditions of a dairy cow may be used to predict oestrus and/or sickness in dairy cows. For example, shortly before oestrus, the duration of rumination decreases and activity increases, then on the day of oestrus, vocal sounds increase. Further, a decrease in both duration of rumination and activity is indicative of a cow becoming unwell within 24 hours. Being able to predict oestrus and/or sickness is of great importance and value to herd management.
  • data received from the one or more sensors fitted to a farm animal may be collected and stored by the processing unit. As additional data is collected, it may be analysed by comparison to previously collected and stored data for the same animal, in order to detect a change in any one or more of the physiological characteristics. Any changes detected in the physiological characteristics may then be compared with pre-stored feature information to determine a physiological condition of the farm animal; wherein the physiological characteristics comprise any one or more of: duration of rumination; duration and type of activities, including, but not limited to: sitting, standing, walking, galloping, sprinting, eating, sleeping and running; and vocal sounds including, but not limited to: mooing, bellowing, snorting and grunting.
  • the methods of the present invention may be used to detect and/or determine one or more physiological conditions of a farm animal.
  • physiological conditions may include, but are not limited to: oestrus, sickness and imminent delivery.
  • the present invention provides a method of profiling a farm animal, preferably a farm ruminant, and more preferably a dairy cow.
  • the method comprising the steps of: (i) monitoring one or more of the physiological characteristics of the farm animal, wherein the one or more physiological characteristics are selected from: duration of rumination (in ruminant animals), animal movement/activity and vocal sounds; (ii) detecting a change in one or more of the physiological characteristics; and (iii) associating the change in one or more of the physiological characteristics with a physiological condition of the farm animal.
  • the present invention provides a bovine asset monitoring system comprising: a rumination sensor to detect a daily bovine asset rumination duration; a motion sensor to detect a daily bovine asset motion; a rumination aberration detection module to detect when the daily bovine asset rumination duration diverges from a known bovine rumination pattern; and a motion aberration detection module to detect when the daily bovine asset motion diverges from a known bovine motion pattern.
  • the rumination sensor is an accelerometer configured to detect jaw motion.
  • the motion aberration detection module is configured to use dead reckoning to determine a daily motion pattern.
  • the rumination aberration detection module is configured to identify when the daily bovine asset rumination duration falls below a minimum rumination value or exceeds a maximum rumination value; for example, the minimum rumination value may be approximately 450 minutes per day, and the maximum rumination value may be 500 minutes per day.
  • the bovine asset monitoring system further comprises: a posture sensor to detect a daily bovine posture; and a posture aberration detection module to detect when the daily bovine posture diverges from a known bovine posture pattern.
  • the posture sensor comprises an accelerometer configured to detect bovine posture by detecting gravitational acceleration.
  • the posture sensor comprises a gyroscope configured to detect changes in bovine posture by detecting gyroscopic rotation.
  • the bovine asset monitoring system further comprises: a data storage module to record data; and a wireless communication module to communicate stored data to a computer system.
  • the present invention provides a bovine asset monitoring system comprising: an RFID patch; a thermal imaging sensor to capture thermal images of a bovine vulva; a bovine thermal aberration detection module to detect when the daily bovine thermal aberration diverges from a known bovine thermal pattern.
  • the RFID patch comprises: a rumination sensor to detect a daily bovine asset rumination duration; a motion sensor to detect a daily bovine asset motion and posture; and a transceiver coupled to receive information from the rumination sensor and the motion sensor.
  • the rumination sensor comprises an accelerometer to detect chewing by sensing jaw motion.
  • the motion sensor comprises an accelerometer positioned to detect orientation of the bovine asset, and optionally, the motion sensor further comprises a gyroscope positioned to detect rotation of the bovine asset.
  • the present invention provides a bovine asset monitoring device comprising: a rumination sensor to detect a daily bovine asset rumination duration; a motion sensor to detect a daily bovine asset motion and posture; a transceiver coupled to receive information from the rumination sensor and the motion sensor; and a patch to support the rumination sensor, motion sensor, and transceiver on the bovine asset.
  • the rumination sensor comprises an accelerometer to detect chewing by sensing jaw motion.
  • the motion sensor comprises an accelerometer positioned to detect orientation of the bovine asset, and optionally, the motion sensor further comprises a gyroscope positioned to detect rotation of the bovine asset.
  • the patch comprises an RFID.
  • the present invention provides a method comprising: receiving periodic rumination and posture information from sensors attached to a bovine asset; performing rumination aberration detection via a programmed computer to detect when the periodic bovine asset rumination duration diverges from a known bovine rumination pattern; and performing motion aberration detection to detect when the periodic bovine asset motion diverges from a known bovine motion pattern.
  • the periodic information is received on a daily basis.
  • the method further comprises receiving thermal images of a bovine vulva and determining via a computer when a daily bovine thermal aberration diverges from a known bovine thermal pattern.
  • the computer system comprises at least storage memory and a processor; the storage memory and the processor are operably connected to each other.
  • the computer system may also comprise a processing unit which is configured to store computer-readable instructions to control the processor to: acquire physiological data from the one or more sensors; store the physiological data in the storage memory; correlate, analyse and compare data obtained from the one or more sensors with data previously stored.
  • FIG. 1 A and 1 B One embodiment of a computer system 200 suitable for use in the present invention is shown in Figs. 1 A and 1 B.
  • computer system 200 comprises a processing unit 201 comprising input devices such as a keyboard 202, a mouse pointer device 203, and an external hard drive 227; and output devices including a display device 214.
  • video display 214 may comprise a touchscreen.
  • a Modulator-Demodulator (Modem) transceiver device 216 may be used by the computer module 201 for communicating to and from a communications network 220 via a connection 221.
  • the network 220 may be a wide-area network (WAN), such as the internet, a cellular telecommunications network, or a private WAN.
  • WAN wide-area network
  • computer module 201 may be connected to other similar personal devices 290 or server computers 291.
  • the modem 216 may be a traditional “dial-up” modem.
  • the modem 216 may be a broadband modem.
  • a wireless modem may also be used for wireless connection to network 220.
  • the computer module 201 typically includes at least one processor 205, and a memory 206 for example formed from semiconductor random access memory (RAM) and semiconductor read only memory (ROM).
  • the module 201 also includes a number of input/output (I/O) interfaces including: an audio-video interface 207 that couples to the video display 214; an I/O interface 213 for the keyboard 202, mouse 203 and external hard drive 227; and an interface 208 for the external modem 216 and printer 215.
  • modem 216 may be incorporated within the computer module 201 , for example within the interface 208.
  • the computer module 201 also has a local network interface 21 1 which, via a connection 223, permits coupling of the computer system 200 to sensors fitted to farm animals 222.
  • the connection 223 is preferably a wireless connection such as, but not limited to: Wi-Fi, Bluetooth or a cellular telecommunications network.
  • sensors fitted to farm animals 222 may also couple to the wide network 220 via a connection 224, which may be effected through a cellular telecommunications network or similar functionality.
  • the interface 21 1 may be formed by an Ethernet circuit card, a Bluetooth wireless arrangement or a Wi-Fi wireless arrangement or other suitable interface.
  • the I/O interfaces 208 and 213 may afford either or both of serial and parallel connectivity, the former typically being implemented according to the Universal Serial Bus (USB) standards and having corresponding USB connectors (not illustrated).
  • USB Universal Serial Bus
  • Storage devices 209 are provided and typically include a hard disk drive (HDD), solid state drive (SSD) or similar 210. Other storage devices such as, an external HD 227, a disk drive (not shown) and a magnetic tape drive (not shown) may also be used.
  • An optical disk drive 212 may be provided to act as a non-volatile source of data.
  • Portable memory devices such as optical disks (e.g. CD-ROM, DVD, Blu-Ray Disc), USB-RAM, external hard drives and floppy disks for example, may be used as appropriate sources of data to the computer system 200.
  • Another source of data to computer system 200 is provided by the at least one server computer 291 through network 220.
  • the components 205 to 213 of the computer module 201 typically communicate via an interconnected bus 204 in a manner that results in a conventional mode of operation of computer system 200.
  • processor 205 is coupled to system bus 204 through connections 218.
  • memory 206 and optical disk drive 212 are coupled to the system bus 204 by connections 219.
  • Examples of computer systems 200 on which the described arrangements can be practiced include IBM-PC's and compatibles, Sun Sparc stations, Apple computers; smart phones; tablet computers or like a device comprising a computer module like computer module 201 .
  • display device 214 may comprise a touchscreen and other input and output devices may or may not be included such as, mouse pointer device 203 and keyboard 202.
  • Figure 1 B is a detailed schematic block diagram of processor 205 and a memory 234.
  • the memory 234 represents a logical aggregation of all the memory modules, including the storage device 209 and semiconductor memory 206, which can be accessed by the computer module 201 shown in Figure 1 A.
  • the methods of the invention may be implemented using computer system 200 wherein the methods may be implemented as one or more software application programs 233 executable within computer module 201 .
  • the steps of the methods of the invention may be effected by instructions 231 in the software carried out within the computer module 201 .
  • the software instructions 231 may be formed as one or more code modules, each for performing one or more particular tasks.
  • the software 233 may also be divided into two separate parts, in which a first part and the corresponding code modules performs the method of the invention and a second part and the corresponding code modules manage a graphical user interface between the first part and the user.
  • the software 233 may be stored in a computer readable medium, including in a storage device of a type described herein. The software is loaded into the personal device 200 from the computer readable medium or through network 221 or 223, and then executed by computer system 200. In one example the software 233 is stored on storage medium 225 that is read by optical disk drive 212. Software 233 is typically stored in the SSD/HDD 210 or the memory 206.
  • a computer readable medium having such software 233 or computer program recorded on it is a computer program product.
  • the use of the computer program product in the computer system 200 preferably effects a device or apparatus for implementing the methods of the invention.
  • the software application programs 233 may be supplied to the user encoded on one or more disk storage medium 225 such as a CD-ROM, DVD or Blu- Ray disc, and read via the corresponding drive 212, or alternatively may be read by the user from the networks 220 or 222. Still further, the software can also be loaded into the computer system 200 from other computer readable media.
  • Computer readable storage media refers to any non-transitory tangible storage medium that provides recorded instructions and/or data to the computer module 201 or computer system 200 for execution and/or processing.
  • Examples of such storage media include floppy disks, magnetic tape, CD-ROM, DVD, Blu-ray Disc, a hard disk drive, a ROM or integrated circuit, USB memory, a magneto-optical disk, or a computer readable card such as a PCMCIA card, SD card and the like, whether or not such devices are internal or external of the computer module 201 .
  • Transitory or non-tangible computer readable transmission media that may also participate in the provision of software application programs 233, instructions 231 and/or data to the computer module 201 include radio or infra-red transmission channels as well as a network connection 221 , 223, 334, to another computer or networked device 290, 291 and the Internet or an Intranet including email transmissions and information recorded on Websites and the like.
  • GUIs graphical user interfaces
  • a user of computer system 200 and the methods of the invention may manipulate the interface in a functionally adaptable manner to provide controlling commands and/or input to the applications associated with the GLII(s).
  • the manipulations including mouse clicks and/or screen touches may be transmitted via network 220 or 222.
  • a power-on self-test (POST) program 250 may execute.
  • the POST program 250 is typically stored in a ROM 249 of the semiconductor memory 206.
  • a hardware device such as the ROM 249 is sometimes referred to as firmware.
  • the POST program 250 examines hardware within the computer module 201 to ensure proper functioning, and typically checks processor 205, memory 234 (209, 206), and a basic input-output systems software (BIOS) module 251 , also typically stored in ROM 249, for correct operation. Once the POST program 250 has run successfully, BIOS 251 activates hard disk drive 210.
  • BIOS basic input-output systems software
  • Hard disk drive 210 Activation of hard disk drive 210 causes a bootstrap loader program 252 that is resident on hard disk drive 210 to execute via processor 205. This loads an operating system 253 into RAM memory 206 upon which operating system 253 commences operation.
  • Operating system 253 is a system level application, executable by processor 205, to fulfill various high-level functions, including processor management, memory management, device management, storage management, software application interface, and generic user interface.
  • Operating system 253 manages memory 234 (209, 206) in order to ensure that each process or application running on computer module 201 has sufficient memory in which to execute without colliding with memory allocated to another process. Furthermore, the different types of memory available in the personal device 200 must be used properly so that each process can run effectively. Accordingly, the aggregated memory 234 is not intended to illustrate how particular segments of memory are allocated, but rather to provide a general view of the memory accessible by computer module 201 and how such is used.
  • Processor 205 includes a number of functional modules including a control unit 239, an arithmetic logic unit (ALU) 240, and a local or internal memory 248, sometimes called a cache memory.
  • the cache memory 248 typically includes a number of storage registers 244, 245, 246 in a register section storing data 247.
  • One or more internal busses 241 functionally interconnect these functional modules.
  • the processor 205 typically also has one or more interfaces 242 for communicating with external devices via the system bus 204, using a connection 218.
  • the memory 234 is connected to the bus 204 by connection 219.
  • Application program 233 includes a sequence of instructions 231 that may include conditional branch and loop instructions.
  • Program 233 may also include data 232 which is used in execution of the program 233.
  • the instructions 231 and the data 232 are stored in memory locations 228, 229, 230 and 235, 236, 237, respectively.
  • a particular instruction may be stored in a single memory location as depicted by the instruction shown in the memory location 230.
  • an instruction may be segmented into a number of parts each of which is stored in a separate memory location, as depicted by the instruction segments shown in the memory locations 228 and 229.
  • processor 205 is given a set of instructions 243 which are executed therein. The processor 205 then waits for a subsequent input, to which processor 205 reacts by executing another set of instructions.
  • Each input may be provided from one or more of a number of sources, including data generated by one or more of the input devices 202, 203, or 214 when comprising a touchscreen, data received from an external source across one of the networks 220, 222, data retrieved from one of the storage devices 206, 209 or data retrieved from a storage medium 225 inserted into the corresponding reader 212.
  • the execution of a set of the instructions may in some cases result in output of data. Execution may also involve storing data or variables to the memory 234.
  • the disclosed arrangements use input variables 254 that are stored in the memory 234 in corresponding memory locations 255, 256, 257, 258.
  • the described arrangements produce output variables 261 that are stored in the memory 234 in corresponding memory locations 262, 263, 264, 265.
  • Intermediate variables 268 may be stored in memory locations 259, 260, 266 and 267.
  • the register section 244, 245, 246, the arithmetic logic unit (ALU) 240, and the control unit 239 of the processor 205 work together to perform sequences of microoperations needed to perform "fetch, decode, and execute" cycles for every instruction in the instruction set making up the program 233.
  • Each fetch, decode, and execute cycle comprises:
  • control unit 239 determines which instruction has been fetched
  • a further fetch, decode, and execute cycle for the next instruction may be executed.
  • a store cycle may be performed by which the control unit 239 stores or writes a value to a memory location 232.
  • Each step or sub-process in the methods of the invention may be associated with one or more segments of the program 233, and may be performed by register section 244- 246, the ALU 240, and the control unit 239 in the processor 205 working together to perform the fetch, decode, and execute cycles for every instruction in the instruction set for the noted segments of program 233.
  • One or more other computers 290 may be connected to the communications network 220 as seen in Figure 1 A. Each such computer 290 may have a similar configuration to the computer module 201 and corresponding peripherals.
  • One or more other server computer 291 may be connected to the communications network 220. These server computers 291 response to requests from the personal device or other server computers to provide information.
  • Method 100 may alternatively be implemented in dedicated hardware such as one or more integrated circuits performing the functions or sub functions of the described methods.
  • dedicated hardware may include graphic processors, digital signal processors, or one or more microprocessors and associated memories.
  • each of the processors and/or the memories of the processing machine may be located in geographically distinct locations and connected so as to communicate in any suitable manner.
  • each of the processor and/or the memory may be composed of different physical pieces of equipment. Accordingly, it is not necessary that a processor be one single piece of equipment in one location and that the memory be another single piece of equipment in another location. That is, it is contemplated that the processor may be two pieces of equipment in two different physical locations. The two distinct pieces of equipment may be connected in any suitable manner.
  • the memory may include two or more portions of memory in two or more physical locations.
  • processing as described above is performed by various components and various memories. It will be understood, however, that the processing performed by two distinct components as described above may, in accordance with a further embodiment of the invention be performed by a single component. Further, the processing performed by one distinct component as described above may be performed by two distinct components. In a similar manner, the memory storage performed by two distinct memory portions as described above may, in accordance with a further embodiment of the invention, be performed by a single memory portion. Further, the memory storage performed by one distinct memory portion as described above may be performed by two memory portions.
  • various technologies may be used to provide communication between the various processors and/or memories, as well as to allow the processors and/or the memories of the invention to communicate with any other entity, i.e., so as to obtain further instructions or to access and use remote memory stores, for example.
  • Such technologies used to provide such communication might include a network, the Internet, Intranet, Extranet, LAN, an Ethernet, a telecommunications network (e.g., a cellular or wireless network) or any client server system that provides communication, for example.
  • Such communications technologies may use any suitable protocol such as TCP/IP, UDP, or OSI, for example.
  • a communication device is described that may be used in a communication system, unless the context otherwise requires, and should not be construed to limit the present invention to any particular communication device type.
  • a communication device may include, without limitation, a bridge, router, bridge-router (router), switch, node, or other communication device, which may or may not be secure.
  • logic blocks e.g., programs, modules, functions, or subroutines
  • logic elements may be added, modified, omitted, performed in a different order, or implemented using different logic constructs (e.g., logic gates, looping primitives, conditional logic, and other logic constructs) without changing the overall results or otherwise departing from the true scope of the invention.
  • Various embodiments of the invention may be embodied in many different forms, including computer program logic for use with a processor (e.g., a microprocessor, microcontroller, digital signal processor, or general purpose computer and for that matter, any commercial processor may be used to implement the embodiments of the invention either as a single processor, serial or parallel set of processors in the system and, as such, examples of commercial processors include, but are not limited to MercedTM, PentiumTM, XeonTM, CeleronTM, AthlonTM, AMDTM, ARMTM, CoreTM and the like), programmable logic for use with a programmable logic device (e.g., a Field Programmable Gate Array (FPGA) or other PLD), discrete components, integrated circuitry (e.g., an Application Specific Integrated Circuit (ASIC)), or any other means including any combination thereof.
  • a processor e.g., a microprocessor, microcontroller, digital signal processor, or general purpose computer and for that matter, any commercial processor may be
  • predominantly all of the communication between users and the server is implemented as a set of computer program instructions that is converted into a computer executable form, stored as such in a computer readable medium, and executed by a microprocessor under the control of an operating system.
  • Computer program logic implementing all or part of the functionality where described herein may be embodied in various forms, including a source code form, a computer executable form, and various intermediate forms (e.g., forms generated by an assembler, compiler, linker, or locator).
  • Source code may include a series of computer program instructions implemented in any of various programming languages (e.g., an object code, an assembly language, or a high-level language such as Fortran, C, C++, JAVA, or HTML.
  • the source code may define and use various data structures and communication messages.
  • the source code may be in a computer executable form (e.g., via an interpreter), or the source code may be converted (e.g., via a translator, assembler, or compiler) into a computer executable form.
  • the computer program may be fixed in any form (e.g., source code form, computer executable form, or an intermediate form) either permanently or transitorily in a tangible storage medium, such as a semiconductor memory device (e.g, a RAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a magnetic memory device (e.g., a diskette or fixed disk), an optical memory device (e.g., a CD-ROM or DVD-ROM), a PC card (e.g., PCMCIA card), or other memory device.
  • a semiconductor memory device e.g, a RAM, ROM, PROM, EEPROM, or Flash-Programmable RAM
  • a magnetic memory device e.g., a diskette or fixed disk
  • an optical memory device e.g., a CD-ROM or DVD-ROM
  • PC card e.g., PCMCIA card
  • the computer program may be fixed in any form in a signal that is transmittable to a computer using any of various communication technologies, including, but in no way limited to, analogue technologies, digital technologies, optical technologies, wireless technologies (e.g. Wi-Fi, Bluetooth), networking technologies, and internetworking technologies.
  • the computer program may be distributed in any form as a removable storage medium with accompanying printed or electronic documentation (e.g., shrink wrapped software), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over the communication system (e.g., the Internet or World Wide Web).
  • Hardware logic including programmable logic for use with a programmable logic device
  • implementing all or part of the functionality where described herein may be designed using traditional manual methods, or may be designed, captured, simulated, or documented electronically using various tools, such as Computer Aided Design (CAD), a hardware description language (e.g., VHDL or AHDL), or a PLD programming language (e.g., PALASM, ABEL, or CUPL).
  • Hardware logic may also be incorporated into display screens for implementing embodiments of the invention and which may be segmented display screens, analogue display screens, digital display screens, CRTs, LED screens, Plasma screens, liquid crystal diode screen, and the like.
  • Programmable logic may be fixed either permanently or transitorily in a tangible storage medium, such as a semiconductor memory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a magnetic memory device (e.g., a diskette or fixed disk), an optical memory device (e.g., a CD-ROM or DVD-ROM), or other memory device.
  • a semiconductor memory device e.g., a RAM, ROM, PROM, EEPROM, or Flash-Programmable RAM
  • a magnetic memory device e.g., a diskette or fixed disk
  • an optical memory device e.g., a CD-ROM or DVD-ROM
  • the programmable logic may be fixed in a signal that is transmittable to a computer using any of various communication technologies, including, but in no way limited to, analogue technologies, digital technologies, optical technologies, wireless technologies (e.g., Bluetooth), networking technologies, and internetworking technologies.
  • the programmable logic may be distributed as a removable storage medium with accompanying printed or electronic documentation (e.g., shrink wrapped software), preloaded with a computer system (e.g., on system ROM or fixed disk), or distributed from a server or electronic bulletin board over the communication system (e.g., the Internet or World Wide Web).
  • printed or electronic documentation e.g., shrink wrapped software
  • a computer system e.g., on system ROM or fixed disk
  • server or electronic bulletin board e.g., the Internet or World Wide Web
  • the present invention also provides methodologies for determining and monitoring an individual farm animal’s body condition score (BCS).
  • BCS body condition score
  • calculation of an animal’s BCS was performed by an expert who considered the “fatness” of the animal at a number of individual points on the animal. The expert then assigned a score of 0 to 1 .0 to each of the points, to arrive at a BCS which represented the total of the scores at each point. For example, for a cow or a horse, there are 9 points that are considered, resulting in a potential range for the BCS from 0.0 to 9.0. Typical BCS scores for cows and horses are in the range of 4.0 to 6.0.
  • the present invention provides a method of determining a body condition score (BCS) of a farm animal, the method comprising the steps of: determining one or more physical dimensions of the farm animal; providing the one or more physical dimensions to a computer system; and using the computer system to predict a BCS of the farm animal.
  • BCS body condition score
  • a computer system may use a machine learning approach to construct a virtual three-dimensional (3D) model of a farm animal, from which the same or a different computer system may determine a BCS of the farm animal.
  • the specific measurements include, but are not limited to: relative distance between one point on the animal to another and the relative contour of one area to another.
  • BCS of a cow may be determined by measuring the relative distances and contours between nine (9) different points on the cow, when viewed from behind. These points are indicated by circles on one of the images in Figure 4.
  • a machine learning model trained using a supervised or semi-supervised machine learning approach is suitable for use in the present invention.
  • the machine learning model could be trained with labelled data that includes data related to the specific animal measurements and the BCS of the animal from which the data was obtained. It is envisaged that a set of training data will be collected from farm animals of predetermined BCS. The training data will be provided to, and used by, a computer system to correlate specific data types with specific aspects of BCS calculations. Upon completion of training, the computer system will be able to predict BCS from sets of data collected from farm animals of unknown BCS.
  • the machine learning computer remembers the contour and the corresponding BCS value.
  • the new cow picture scans through the known cow contours and selects the contour closest to the test image. After it selects the closest contour, it calculates the difference (delta) between two contours (test and closest) and adjusts the BCS score of the closest contour proportionate to the delta to arrive at the BCS of the test contour.
  • FIG. 2 is a schematic representation of the methodology by which the computer system may use the collected data to predict an animal’s BCS.
  • Input data 301 such as animal dimensions, infrared/thermal information is collected and relevant feature information 302 is extracted.
  • the relevant feature information 302 is then used to make a virtual representation of the animal 303.
  • specific biological feature scores 304 are then determined and used to generate a BCS.
  • a set of reference animal images or sequences of frames (videos) with their corresponding body condition score (BCS) are pre-stored in our data storage. These images and videos are captured via the sensor devices that listed in this patent which contains visual biological information, such as skin colour, surface texture, shape and contour etc. Human experts are involved to provide the BCS for these reference animals via the standard methodology, for example, visually assessing the animal. Latent features are extracted out of the reference animals' images or videos to span a latent space (feature extraction). Then, expert-measured BCS are used to construct statistical regression models, such as linear regression, kernel regression etc, in this latent space. This system will form a pipeline for BCS inference by using animal images or videos via feature extraction and regression prediction. For example: when a new animal is registered, the system will extract the latent feature for this animal from its images or videos, and then the system will apply the latent feature into the regression models to obtain the predicted BCS.
  • BCS body condition score
  • Feature extraction takes an individual image or a sequence of frames (video) to extract the hidden information.
  • Traditional machine learning models are trained and used with a series of manually defined feature extraction tasks or processes.
  • deep learning models the machine learns how to extract a useful feature from the raw input via a complex artificial neural network.
  • An artificial neural network contains a number of connected nodes which are arranged in layers. An input of one layer is mutated with a mathematical calculation between the input and weight values that stored in nodes for this layer, then the output is served as input of next layer.
  • input of deep learning models is the vector representation of an animal image that is captured as a matrix of pixel values; and the output is a vector representation of a latent space feature for this given animal image.
  • Backpropagation involves computing the gradient of the loss function with respect to the weights of the network for a single input-output example, and does so efficiently, unlike a naive direct computation of the gradient with respect to each weight individually. This efficiency makes it feasible to use gradient methods for training multilayer networks, updating weights to minimize loss; gradient descent, or variants such as stochastic gradient descent, are commonly used.
  • the backpropagation algorithm works by computing the gradient of the loss function with respect to each weight by the chain rule, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule. Then the test 3D model is projected through the latent space to project into the BCS vector space.
  • Regression is a statistical method for estimating the relationships between two related variables.
  • the two related variables are animal’s latent feature and body condition score.
  • latent feature is treated as the independent variable and BCS is treated as the dependent variable.
  • BCS is treated as the dependent variable.
  • this system will generate a predicted BCS as the output.
  • Various linear regression algorithms are possible, such as least squares regression, maximum likelihood estimation, Bayesian linear regression, least absolute shrinkage and selection operator etc.
  • non-linear regression is also considered to improve the prediction accuracy.
  • a list of kernel functions for non-linear regression are possible: Gaussian function, exponential functions, logarithmic functions etc.
  • this trained machine learning model can be applied to unlabelled data.
  • the monitoring application operates on a user interface.
  • the machine learning models described above may similarly be trained/applied to ascertain changes as required by the present invention.
  • the processes described herein may be carried out by a computing device, such as computing device as described above. However, the process can be carried out by other types of devices or device subsystems. For example, the process could be carried out by a portable computer, such as a laptop or a tablet device.
  • An exemplary non-contact method for determining physical dimensions includes the use of depth and/or thermal imaging units or cameras. Such cameras may be fixed in position and animals may be analysed periodically as they pass the cameras. An exemplary location for a fixed camera is near a milking station, where cows are stationary for an extended period, allowing accurate measurements to be made. A record of the quantity of milk produced for each animal is also possible. The quantity of milk and the BCS for individual animals may then be correlated for analysis.
  • the cameras may be mounted to an unmanned aerial vehicle (UAV) or drone.
  • UAV unmanned aerial vehicle
  • data may be assigned to a specific animal by correlating individual identifiers located on each animal.
  • identifiers may comprise visual tags with a specific combination of features, such as but not limited to, letters, numerals, shapes, colours and RFID.
  • the identifiers may be visually or non-visually detected. Visually detectable identifiers may be of particular use with arial observations, while an RFID may be useful in enclosed areas where animals may come in close contact with an RFID reader during eating or milking, for example.
  • Determining the amount of available energy in a pasture may assist in the devising of a program of grazing of that pasture.
  • this information can be combined with information on the amount of milk produced to determine overall efficiency of the farm.
  • BCS information of individual animals may assist in selecting the most efficient animals for participation in breeding programs.
  • Animals with high feed efficiency may be selected for breeding programs with the intention of herd improvement.
  • Feed efficiency may be measured in % of energy an animal converts to milk or body tissue. For example, a bovine with a healthy BCS score takes the minimum energy for body maintenance and converts the rest into milk (considered for cow) or body tissue (considered for bull or heifers).
  • one bovine could convert given unit of energy after body maintenance into unit of milk or body tissue.
  • a unit of milk or body tissue generated by one animal could differ from another based on their genetic traits. Accordingly, animals with better genetic traits can be identified and selected for breeding programs.
  • the present invention provides a method of determining energy content of a pasture, the method comprising the steps of: determining one or more physical dimensions of a sample of the pasture; providing the one or more physical dimensions to a computer system; and using the computer system to predict an energy content of the pasture.
  • the one or more physical dimensions used in the method are selected from the group consisting of: grass height, grass density, and bare land proportion.
  • a method of using data obtained from depth, colour and/or thermal imaging units or cameras to determine or predict the amount of energy in a pasture that is available to farm animals In an exemplary embodiment of the present invention, a drone or a plurality of drones fly over the paddock to capture specific patches of the paddock and send the pictures of fixed area per patch to the server or computer system. For example, ten drones fly over a hectare of paddock (10,000m 2 ) each drone flying at a fixed altitude whereby the cameras attached thereto cover an area of 10 sq. metres per image. Each drone takes 100 pictures covering an area of 1 ,000 sq. metres.
  • a laboratory test reveals that a sample received from the paddock has 6 MJ of energy per Kg.
  • the above calculations may be used in order to most efficiently manage grazing. Frequent assessment of pasture energy content allows the growth of pasture to be continuously monitored and herd grazing can be directed to the paddocks/regions that will provide the most efficient use of pasture. This data allows profiling of a paddock and of the various paddocks on a property. Pasture energy content of pastures grazed can be correlated with, for example, milk production or BCS to inform decisions around paddock rotation. Further, since paddocks are assessed in small patches, it is possible to identify variations of pasture energy content across a paddock. If necessary, the farmer can temporarily fence off an area of a paddock so that the herd can graze on only a selected portion of the paddock. It is also possible to monitor residual pasture energy content following grazing, this assists in the assessment of grazing efficiency and informs further decisions regarding paddock rotation for grazing.
  • the above profiling of the paddock assists in timing of the grazing at optimal growth. This helps in efficient use of paddock and optimal milk production.
  • the milk production reduces due to reduction in feed and reduction in feed available for milk production by the animal.
  • the grazing is done over the optimal level, then the excess grass is left uneaten and becomes dead grass in the subsequent grazing.
  • the dead grass is eaten by the animal but contributes little nutritive value in the feed. So the subsequent grazing leads to reduction in milk production.
  • the dead grass reduces the paddock growth capacity as well.
  • FIG. 3 Illustrated in Figure 3 is an exemplary outline of a farm management system that uses the above-described monitoring of pasture energy content to inform efficient management of grazing.
  • the farm management system takes real world data from paddocks in the form of paddock energy and health data (for example, pasture energy content) as well as animal health and identity data.
  • a machine learning service uses the provided data to predict a BCS score for an animal.
  • the energy and health data are associated with specific paddocks (or patches thereof).
  • the current and predicted BCS scores are associated with specific animals. Accordingly, a paddock or animal profile can be requested from the farm management system.
  • the paddock energy and health data are also associated with GPS coordinates.
  • a map showing paddock energy and health information at a fine scale, such as but not limited to 10m 2 patches, depending on the resolution of the data captured.
  • the farm management system can therefore provide animal profiles; paddock (or patch) profiles; recommendations on paddock separation; recommendations on paddock rotation; predictions of animal production; predictions and measurements of residual pasture energy.
  • the measurements of residual energy and herd output BCS or milk production
  • Measurements of residual pasture energy content and animal BCS scores taken after grazing can be compared with the predicted values and any discrepancies used to further enhance the predictions made by the computer system.
  • a farm management app on a smart device is a preferred farm management system for the presentation of the measurements and predictions determined by the computer system described above. Such an app allows the farmer instant access to all the data and predictions above.
  • Paddock separation allows efficient use of pasture within a paddock by dividing it and grazing it according to the amount of pasture predicted by the computer system of the present invention. For example, the herd is sent to two paddocks in a day, one in the morning and one in the afternoon.
  • the farmer would divide a paddock by placing a fence equidistant from the gate to the far end of the paddock.
  • the data on grass distribution and energy content in the paddock allows the farmer to divide the paddock at the required distance(s) based on the grass and energy requirements of the herd for half a day.
  • the computer system may recommend dividing the paddock at 1 10m from the gate.
  • Figure 5 shows a series of screen shots from a mobile phone application that embodies the presently described invention.
  • paddock fencing separations in an exemplary paddock using exemplary data By marking the from and to point on the fence in the app, one can find the distance covered and the amount of grass available in the area.
  • the exemplary data indicates 3.5 Tonnes of grass available in the area covered by the rectangle. 36m - 5.1 T and 40m 6.3T.
  • FIG. 6 shows an exemplary grazing schedule generated according to the present invention. In this example grazing split into two sessions per day, AM and PM, so that the animals need to walk less distance for feed in a day. The paddocks are split into fractions so that each fraction provides half the quantity of the daily requirements.
  • Figure 7 shows screen shots from a mobile phone application that embodies the presently described invention.
  • the Machine Algorithm predicts the paddocks’ growth into the future. So, based on the growth rate, the system schedules grazing for optimal grazing. This optimal grazing leads to optimal residual. The system also shows the current growth in each paddock.
  • “Residual” is the amount of pasture remaining after grazing. For example, on a cow’s first round of grazing, grass will typically be eaten down to a length of approximately 100mm. After a second round of grazing, the grass will be eaten down to approximately 50mm. A length of approximately 50mm is the optimum length of grass to be left for regeneration. If, for example, grass is left at 100mm, the pasture will readily become overgrown. In addition, a significant amount of the pasture will be dead grass, which has a much lower nutrient value than live grass. Accordingly, cows grazing on overgrown pasture will eat the same amount but receive a lower amount of nutrition, resulting in poorer milk production, for example. Conversely, pasture that is over grazed may have a length of only 20mm. Such a pasture may take an extended period of time in which to fully recover. It is clear that optimal grazing is beneficial not only to the animal, but to the pasture.
  • any recitation herein of a phrase “comprising one or more claim element” e.g., “comprising A”
  • the phrase is intended to encompass the narrower, for example, “consisting essentially of A” and “consisting of A”.
  • the broader word “comprising” is intended to provide specific support in each use herein for either “consisting essentially of” or “consisting of”.
  • the invention illustratively described herein suitably may be practiced in the absence of any element or elements, limitation or limitations which is not specifically disclosed herein.

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Abstract

La présente invention concerne des systèmes de gestion de ferme comprenant : un système de surveillance d'animaux, les systèmes de surveillance d'animaux déterminant la santé animale ; et un système de détermination de teneur en énergie de pâturage ; les systèmes de gestion de ferme corrélant la santé des animaux et la teneur en énergie de pâturage pour informer un protocole de broutage en pâturage de manière à maximiser la production de produits animaux.
PCT/AU2021/051445 2020-12-04 2021-12-03 Surveillance et gestion de bétail WO2022115916A1 (fr)

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