WO2021033732A1 - Grazing animal management system - Google Patents

Grazing animal management system Download PDF

Info

Publication number
WO2021033732A1
WO2021033732A1 PCT/JP2020/031342 JP2020031342W WO2021033732A1 WO 2021033732 A1 WO2021033732 A1 WO 2021033732A1 JP 2020031342 W JP2020031342 W JP 2020031342W WO 2021033732 A1 WO2021033732 A1 WO 2021033732A1
Authority
WO
WIPO (PCT)
Prior art keywords
animal
grazing
management system
animals
state
Prior art date
Application number
PCT/JP2020/031342
Other languages
French (fr)
Japanese (ja)
Inventor
啓司 岡田
千田　廉
山本　倫之
伸孝 清野
響輝 善方
Original Assignee
国立大学法人岩手大学
株式会社ズコーシャ
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 国立大学法人岩手大学, 株式会社ズコーシャ filed Critical 国立大学法人岩手大学
Priority to JP2021540975A priority Critical patent/JP7228171B2/en
Publication of WO2021033732A1 publication Critical patent/WO2021033732A1/en

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01K—ANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K29/00—Other apparatus for animal husbandry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
    • G01P15/18—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration in two or more dimensions
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/70—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in livestock or poultry

Definitions

  • the present invention relates to a grazing animal management system that manages the state of a plurality of animals grazing on a grazing land.
  • a collar is attached to a cow grazing on a pasture, livestock position data based on radio waves from GPS satellites is transmitted from the collar, and the monitoring center transmits the received livestock position data on the screen of the pasture. Describes that a point image is displayed as the livestock position on the map, that the point image showing the position of the cow is displayed as a mass on the map, and that it is determined from the movement trajectory data file that a calf has been born. Has been done.
  • Grazing cattle are bred and managed in a different environment than barn cattle.
  • individual position / travel distance information GPS
  • behavior information by an acceleration sensor e.g., a Bosch Sensortec BMA150
  • weather information e.g., weather information that affects behavior.
  • the daily work at the grazing multi-head management site is carried out by a small number of people, and covers a wide range of tasks such as grassland condition, transhumance work, observation of each cow, insemination work associated with estrus detection, and delivery confirmation / assistance.
  • an object of the present invention is to provide a grazing animal management system that can reduce the management burden on a large number of animals by focusing on the habit of animals that form a herd and determining an animal that is out of the herd as an abnormality. To do.
  • the grazing animal management system of the present invention provides an acceleration sensor 11, a position detection sensor 12, and a transmission unit 13 that transmits these detected values together with an individual identification code to a plurality of animals grazing on the grazing land.
  • a server 20 that is a grazing animal management system that manages the state of the animal and has an animal information database 22 that registers the management information of the animal is specified by the detection value that is attached and transmitted from the transmission unit 13.
  • the group determination step and the management target candidate animal extraction step are repeated a plurality of times at different times, and the management target animal identification step for identifying the animal extracted as the management target candidate animal a predetermined number of times or more as a management target animal. It is characterized by having and.
  • the server 20 in the grazing animal management system according to the first aspect, the server 20 exists at a predetermined position for a predetermined time or longer with respect to the controlled animal specified in the controlled animal identification step.
  • the animal state determination step for determining whether or not the animal is present and the animal condition determination step, the managed animal determined to be present at the predetermined position for the predetermined time or longer is output as a delivery state or a rescue-requiring state.
  • the detection value of the acceleration sensor 11 is obtained for the controlled animal identified by the server 20 in the controlled animal identification step. It is characterized by having an animal state determination step of calculating exercise intensity from the acceleration per unit time or the standard deviation of the acceleration, and determining the estrus state from the calculated exercise intensity.
  • the animal condition determination step in the grazing animal management system according to the third aspect, the exercise intensity on the day (n) and one day before (n-1) to X days before (n).
  • the present invention according to claim 5 is determined from the exercise intensity on the day (n) in comparison with one day before (n-1) in the animal state determination step in the grazing animal management system according to claim 3. It is characterized by that.
  • the interval period from the previous estrus date is set to the estrus state. It is characterized by adding to the judgment.
  • the unit time is determined from the detection value of the position detection sensor 12.
  • the present invention is the acceleration sensor 11 for the controlled animal identified by the server 20 in the controlled animal identification step in the grazing animal management system according to claim 1 or 2. It has a controlled animal display step that displays changes in the detected values in time series, and in the managed animal display step, the acceleration sensor 11 of the controlled animal identified in the managed animal specifying step. It is characterized in that the acceleration per unit time or the change in exercise intensity calculated from the standard deviation of the acceleration is displayed in time series using the detected value.
  • the present invention by setting an animal that moves away from the herd as a controlled animal, it is possible to detect estrus, monitor delivery, detect a disease, detect a fence, and detect an accident, which is difficult when targeting all animals. Therefore, the condition of the grassland and the timing of transhumance can be grasped, and the management burden can be reduced even if a large number of animals are grazing.
  • Flow chart of the grazing animal management system Image diagram at specific time t1 to explain the grazing animal management system Image diagram at specific time t2 to explain the grazing animal management system Image diagram at specific time t3 to explain the grazing animal management system A graph showing an animal state determination processing method in a grazing animal management system according to another embodiment of the present invention.
  • the figure which shows the animal state judgment processing method in the grazing animal management system by still another Example of this invention A graph showing an animal state determination processing method in a grazing animal management system according to still another embodiment of the present invention.
  • Explanatory drawing which shows the grazing animal management system by still another Example of this invention
  • the grazing animal management system includes a herd determination step in which a server having an animal information database for registering animal management information determines a herd from the existence positions of a plurality of animals at a specific time.
  • the management target candidate animal extraction step for extracting the animals that do not belong to the flock judged in the flock judgment step as the management target candidate animals, and the flock judgment step and the management target candidate animal extraction step are repeated multiple times at different times for management. It has a controlled animal identification step of identifying an animal extracted as a target candidate animal a predetermined number of times or more as a controlled animal.
  • estrus detection, delivery monitoring, disease detection, transhumance detection, and accident detection are difficult when targeting all animals. It is possible to grasp the condition of grassland and the timing of transhumance, and even if a large number of animals are grazing, the management burden can be reduced.
  • the server in the grazing animal management system according to the first embodiment, exists at a predetermined position for a predetermined time or longer with respect to the managed animal specified in the managed animal identification step.
  • the managed animal determined to be present at a predetermined position for a predetermined time or longer is output as a grazing state or a rescue-requiring state, and is output at a predetermined position for a predetermined time. It has an output step that outputs a controlled animal determined to be nonexistent as an estrus state. According to the present embodiment, it is possible to predict whether an animal that can be presumed to be abnormal is in a state of parturition, a state requiring rescue, or a state of estrus.
  • a third embodiment of the present invention is the grazing animal management system according to the first embodiment, in which the controlled animal identified by the server in the controlled animal identification step is used for a unit time using the detection value of the acceleration sensor. It has an animal state determination step in which the exercise intensity is calculated from the hit acceleration or the standard deviation of the acceleration, and the estrus state is determined from the calculated exercise intensity. According to this embodiment, the estrus state can be predicted for an animal that can be presumed to be abnormal.
  • the fourth embodiment of the present invention is the grazing animal management system according to the third embodiment, in the animal condition determination step, the exercise intensity on the day (n) and one day before (n-1) to X days before (n-1). It is judged from the comparison with the average exercise intensity up to nX). According to this embodiment, the estrus state can be predicted by comparing with the past average exercise intensity.
  • the fifth embodiment of the present invention is determined from the exercise intensity of the day (n) in comparison with the one day before (n-1) in the animal state determination step in the grazing animal management system according to the third embodiment. It is something to do. According to this embodiment, the estrus state can be predicted by comparing with the exercise intensity of the previous day.
  • a sixth embodiment of the present invention is a grazing animal management system according to any one of the third to fifth embodiments.
  • the interval period from the previous estrus date is used to determine the estrus state. It is something to add.
  • the estrus state can be predicted more accurately by adding the interval period from the past estrus date to the judgment.
  • the moving distance per unit time is calculated from the detection value of the position detection sensor. It is calculated and the calculated travel distance is added to the judgment of the estrus state. According to the present embodiment, the estrus state can be predicted more accurately by adding the moving distance to the judgment.
  • the change of the detection value of the acceleration sensor is timed for the controlled animal specified in the controlled animal identification step. It has a managed animal display step that displays in series, and in the managed animal display step, the acceleration per unit time, or the above, for the managed animal identified in the managed animal identification step, using the detection value of the acceleration sensor.
  • the change in exercise intensity calculated from the standard deviation of acceleration is displayed in chronological order. According to this embodiment, the health condition of an animal that can be presumed to be abnormal can be determined.
  • FIG. 1 is a block diagram showing a grazing animal management system according to an embodiment of the present invention by means for realizing functions.
  • the grazing animal management system according to the present embodiment includes a terminal 10 attached to an animal grazing on the grazing land and a server 20 that manages the state of the grazing animal on the grazing land.
  • the terminal 10 when the animal is a ruminant such as a cow, the terminal 10 is preferably attached to the neck.
  • the terminal 10 includes an acceleration sensor 11, a position detection sensor 12, and a transmission unit 13 that transmits these detected values together with an individual identification code.
  • the acceleration sensor 11 is preferably a 3-axis acceleration sensor, and by using the 3-axis acceleration sensor, it is possible to detect anteroposterior acceleration, a vertical acceleration, and a lateral acceleration, and it is possible to detect foraging, chewing during reclamation, and lying down. Rest can be discriminated from the anteroposterior acceleration, the longitudinal acceleration, and the lateral acceleration.
  • the anteroposterior acceleration, the vertical acceleration, and the lateral acceleration exceed the threshold value at a predetermined time in the continuous elapsed time, it is determined that the food is eaten instead of chewing and lying down during rumination. be able to.
  • the regularity of the chewing acceleration waveform at the time of reflex is determined from at least one of the longitudinal acceleration, the vertical acceleration, and the horizontal acceleration, and if a high regularity is recognized in the acceleration waveform, it is healthy. It can be evaluated as being.
  • the position detection sensor 12 is preferably a sensor based on GNSS (Global Navigation Satellite System). As the individual identification code, it is sufficient that the individual animal can be identified, and the terminal identification code assigned to the terminal 10 can also be used.
  • the server 20 has a receiving unit 21 that receives the detected value transmitted from the transmitting unit 13, an animal information database 22 that stores the received detected value together with time information for each individual identification code, a control unit 23, and a display unit 24. And have.
  • the control unit 23 includes a herd determination means 23A for determining a herd, a management target animal extraction means 23B for extracting an individual not included in the herd as a management target animal, and a management target animal identification means 23C for identifying a management target animal.
  • the animal condition determination means 23D for determining the individual condition of the animal to be managed is provided.
  • the herd determination means 23A determines the herd from the individual existence positions of a plurality of animals at a specific time.
  • the grazing land can be divided into mesh areas, a mesh area in which a predetermined number of individuals exist can be extracted, and a range in which the extracted mesh areas are continuous can be determined as a herd area. Further, if a predetermined number of other individuals exist in a predetermined range centered on an individual individual, it can be determined that the individual is in a group.
  • the management target candidate animal extraction means 23B extracts an individual that does not belong to the herd determined in the herd determination step as a management target candidate animal at a specific time.
  • the managed animal identification means 23C identifies an individual extracted as a management target candidate animal a predetermined number of times or more as a management target animal within a predetermined period.
  • the animal state determining means 23D determines whether or not the managed animal identified in the managed animal identification step has been present at a predetermined position for a predetermined time or longer, and when it has been present at the predetermined position for a predetermined time or longer. Determines the managed animal as a calving state or a rescue-requiring state, and determines that the managed animal is in an estrus state if it does not exist at a predetermined position for a predetermined time or longer.
  • the display unit 24 can display the change in the detection value of the acceleration sensor 11 in time series and the change in the detection value of the position detection sensor 12 in time series for the animal to be managed.
  • FIG. 2 is a flow chart of the grazing animal management system.
  • the server 20 receives the detected value in the receiving unit 21 (S1), the server 20 registers the received detected value in the animal information database 22 (S2).
  • the flock determination means 23A extracts the detection value of the individual position detection sensor 12 at the specific time from the animal information database 22 (S3), and the individual at the specific time from the extracted detection value of the position detection sensor 12.
  • Judge the flock of (S4) In the management target candidate animal extraction step, individuals that do not belong to the herd determined in S4 are extracted as management target candidate animals (S5).
  • the herd determination step (S3, S4) and the management target candidate animal extraction step (S5) are repeated a plurality of times at different times until a predetermined period elapses (No in S6).
  • the predetermined period here is, for example, 6 hours, 12 hours, or 24 hours.
  • the process proceeds to the controlled animal identification step.
  • the management target animal identification step it is determined whether or not the animal has been extracted as a management target candidate animal a predetermined number of times or more (S7), and the individual extracted as a management target candidate animal a predetermined number of times or more is specified as a management target animal (S8).
  • changes in the detection value of the acceleration sensor 11 are displayed in chronological order for the managed animal specified in the managed animal identification step (S11), and the managed animal specified in the managed animal identification step.
  • changes in the detected values of the position detection sensor 12 are displayed in chronological order (S12).
  • the animal state determination step it is determined whether or not the managed animal identified in the control target animal identification step exists at a predetermined position for a predetermined time or longer (S13), and in the output step, it is determined at a predetermined position for a predetermined time or longer.
  • the managed animal determined to exist is output as a delivery state or a rescue-required state (S14), and the managed animal determined to not exist at a predetermined position for a predetermined time or longer is output as an estrus state (S15).
  • FIG. 3 to 5 are image diagrams for explaining the grazing animal management system, FIG. 3 shows the position of an animal at a specific time t1 in a predetermined rangeland, and FIG. 4 shows a position at a specific time t2 in the predetermined rangeland. The location of the animal is shown, and FIG. 5 shows the location of the animal at a specific time t3 in a predetermined rangeland.
  • the broken line frame X is an area indicating the group determined in the group determination step.
  • animals 1 to 7 are judged not to be in the herd
  • animals 1 to 3 are judged not to be in the herd
  • animals 1 to 3, 8 and 9 are in the herd. It is an individual that is judged not to enter, and these individuals are extracted as management target candidate animals at the specific time t1, the specific time t2, and the specific time t3, respectively.
  • animals 1 to 3 are specified as managed animals. Since the animals 1 and 3 exist at different positions at the specific time t1, the specific time t2, and the specific time t3, it can be determined that the animals 1 and 3 do not exist at the predetermined position for a predetermined time or more, and can be predicted to be in an estrus state. Further, since the animal 2 exists at the same position at the specific time t1, the specific time t2, and the specific time t3, it can be predicted that the animal 2 is in a delivery state or a rescue-requiring state.
  • the management burden can be reduced even if a large number of animals are grazing, and the change in the detection value of the acceleration sensor 11 for the managed animals can be changed.
  • By displaying in chronological order or by displaying the change in the detection value of the position detection sensor 12 in chronological order it is possible to judge the health condition of an animal that can be presumed to be abnormal, or to determine the state of delivery, the state requiring rescue, or estrus. You can predict if you are in a state.
  • FIG. 6 is a graph showing an animal state determination processing method in a grazing animal management system according to another embodiment of the present invention
  • FIG. 6A shows a change in exercise intensity on the day (n)
  • FIG. b) shows the standard deviation of the exercise intensity of the day (n) with respect to the average exercise intensity from one day ago (n-1) to X days ago (nX)
  • FIG. 6 (c) shows one from FIG. 6 (b).
  • a graph obtained by graphing the data of the part is shown
  • FIG. 6 (d) shows a graph of the frequency of FIG. 6 (c).
  • the animal condition determination process according to this embodiment is performed by the animal condition determination means 23D shown in FIG. FIG.
  • FIG. 6A is a graph in which the acceleration per unit time is standardized using the detection value of the acceleration sensor 11 for the day (n), and the vertical axis is the acceleration and the horizontal axis is the time.
  • a high value means strong exercise intensity
  • a low value means weak exercise intensity.
  • FIG. 6B the acceleration per unit time of the day (n) was standardized based on the mean value and standard deviation of the acceleration per unit time from one day ago (n-1) to X days ago (nX).
  • the vertical axis is acceleration and the horizontal axis is time.
  • a high value means strong exercise intensity
  • a low value means weak exercise intensity.
  • FIG. 6 (c) cuts out the data 24 hours before the “time for determining estrus” shown in FIG.
  • FIG. 6D shows the time distribution of exercise intensity
  • the estrus state can be determined from the time distribution of exercise intensity.
  • the estrus state can be predicted by comparing with the past average exercise intensity.
  • the exercise intensity can be calculated from the acceleration at 10-minute, 20-minute, 30-minute, and 60-minute intervals or the standard deviation of the acceleration, and the estrus state can be determined from the calculated exercise intensity.
  • FIG. 7 is a diagram showing an animal state determination processing method in a grazing animal management system according to still another embodiment of the present invention
  • FIG. 7A is a diagram after calculating and setting a classification threshold value of exercise intensity by machine learning.
  • 7 (b) shows the time distribution of the exercise intensity shown in FIG. 7 (a)
  • FIG. 7 (c) shows the time distribution of the time distribution shown in FIG. 7 (a) in three categories
  • FIG. 7 (c) shows 3 shown in FIG. 7 (b). It shows the changes in the time distribution divided into two categories before and after estrus.
  • the animal state determination process according to this embodiment is performed by the animal state determination means 23D shown in FIG. 1, but the machine learning calculation can also be performed by a server other than the server 20.
  • FIG. 7 shows the changes in the time distribution divided into two categories before and after estrus.
  • FIG. 7A the magnitudes of exercise intensity at regular intervals are classified into eight categories.
  • FIG. 7 (b) the eight categories in FIG. 7 (a) are summarized into three categories. The magnitudes of exercise intensity in each of the three categories are evenly spaced.
  • FIG. 7 (c) shows the increase / decrease in exercise intensity on the day of estrus (August 30), the day before estrus (August 29), and the day after estrus (August 31).
  • estrus days in the three category distributions are that estrus reduces rest time, eating behavior, and rumination, resulting in decreased behavior with low exercise intensity and moderate (walking). Etc.) will increase.
  • the animal state determination process can be determined from the exercise intensity of the day (n) in comparison with the one day before (n-1), and is compared with the exercise intensity of the previous day.
  • the state of estrus can be predicted.
  • FIG. 8 is a diagram showing an animal state determination processing method in a grazing animal management system according to still another embodiment of the present invention.
  • FIG. 8 shows the detection of estrus at an arbitrary time using 24-hour data of grazing breeding cows.
  • the user can use the accelerometer 11 to obtain data for a predetermined period before the instruction, for example, 24 hours before the instruction, using the animal state determination means 23D.
  • "Emotional signs / estrus” can be predicted by calculating the exercise intensity from the acceleration per unit time or the standard deviation of the acceleration using the detected value and determining the estrus state from the calculated exercise intensity.
  • the estrus state in a predetermined period before the set time can be predicted and displayed. Therefore, it is possible to provide information according to the work schedule of the manager / artificial insemination because information on the presence or absence of estrus can be obtained by setting an arbitrary instruction time according to the convenience of the work / working hours of the grazing manager. Become.
  • FIG. 9A and 9B are diagrams showing an animal state determination processing method in a grazing animal management system according to still another embodiment of the present invention
  • FIG. 9A shows a processing flow
  • FIG. 9B shows a processing result. It is a graph which shows.
  • the animal condition determination process according to this embodiment is performed by the animal condition determination means 23D shown in FIG.
  • estrus can be predicted in real time by using, for example, 10-minute, 20-minute, 30-minute, and 1-hour (variable) interval data of grazing breeding cows.
  • the detection value of the acceleration sensor 11 sent from the transmission unit 13 is standardized (S31), and the exercise intensity standardized in S31 is added to obtain 1-hour data (S32).
  • the 1-hour data is squared (S34), and the result is displayed as a graph (S35).
  • the exercise intensity is calculated for each individual to calculate the coefficient of the estrus level (S33), and the coefficient for each individual is used in the graphing in S35, and the emission information is detected when the threshold value is exceeded (S36).
  • S34 the threshold value is displayed on the graph.
  • the threshold value is set for each individual grazing breeding cow, and when this threshold value (slice level / estrus detection level) is exceeded, the estrus start time can be predicted. If there is no manager on the grazing land for business purposes, it is possible to sequentially calculate (predict) cattle that require estrus time information for other reasons as a "real-time estrus mode".
  • the calving prediction which is one of the animal conditions of grazing cattle, can be made according to the following criteria. Significant decrease in feeding time (estrus day ⁇ delivery day), decrease in travel time that occurs at the same time as the change in feeding time (estrus day> delivery date), staying time in a specific area (radius 150-500 m) 60 It can be judged by the criteria of when it becomes more than a minute and when it is 30 to 1000 m or more away from the center of the flock.
  • the feeding time can be detected by the acceleration fluctuation by the acceleration sensor 11, and the moving time, the staying time in the specific area, and the distance from the center of the flock can be detected by the position detection by the position detection sensor 12.
  • FIG. 10 is a graph showing a method for determining an animal state in a grazing animal management system according to still another embodiment of the present invention, and shows a method for determining an abnormal cattle (disease / accident / death).
  • FIG. 10 is a correlation distribution diagram of the moving distance and the acceleration variable. As shown in FIG. 10, when the level becomes almost zero after 19:30 on August 1, 2019, it can be estimated that there is an abnormality such as a disease, an accident, or death. Therefore, when the acceleration difference value does not fluctuate within a predetermined time such as 1 hour and the moving distance is within a predetermined range such as 10 m, it is determined as an abnormal behavior and notified. be able to.
  • FIG. 11 is a graph showing the animal state determination processing method in the grazing animal management system according to still another embodiment of the present invention, and shows the number of times the animals went to the water fountain.
  • FIG. 11 shows a distribution map obtained from latitude / longitude data for a certain individual of grazing breeding cows for a period of one week, and the circled part is a drinking fountain. The number plotted in the area surrounded by the black frame is the number of visits to the drinking fountain. When it rains, the number of times you go to the drinking fountain decreases. In addition, the distance traveled is also on a downward trend, and movement during estrus is also suppressed. Therefore, in the process of determining the animal state, it is preferable to change the threshold value and the algorithm in the case of rainy weather as in the case of weather other than rainy weather.
  • FIG. 12 is a screen image diagram showing a grazing animal management system according to still another embodiment of the present invention, and shows the staying time of grazing breeding cattle on a map.
  • the small diameter circle indicates a stay of 20 minutes or less
  • the medium diameter circle indicates a stay of 21 minutes or more and 60 minutes or less
  • the large diameter circle indicates 61 minutes or more and up to 2 hours.
  • grassland management can be performed by outputting the fluctuation information of the grassland staying time of the grazing breeding cow together with the position information (map information).
  • FIG. 13 is a screen image diagram showing a grazing animal management system according to still another embodiment of the present invention, and shows a distribution showing which side the entire herd stayed at at what time.
  • the white circles indicate the stays from 4:00 to 18:00
  • the black circles indicate the stays from 18:00 to 4:00
  • FIG. 13 (b) A to F in FIG. 13 (a). It shows the average staying time in a group in the place mesh-divided by P1 to P5. In this way, it is possible to visualize where and how long it takes to eat in the grassland and display it on the map.
  • FIG. 14 is a screen image diagram showing a grazing animal management system according to still another embodiment of the present invention, and is an output image diagram capable of extracting a changing individual.
  • FIG. 14 (a) is a list list output comparing the distance traveled and the amount of activity between the previous day and the current day for each individual, and
  • FIG. 14 (b) is the current position of a specific individual from the list in FIG. 14 (a). Is displayed on the map. In this way, the distance traveled and the amount of activity between the previous day and the day can be compared for each individual, and individuals with changes can be extracted, and managed in place of the flock judgment means 23A or together with the judgment by the flock judgment means 23A. It can also be extracted as a target candidate animal.
  • FIG. 15 is an explanatory diagram showing a grazing animal management system according to still another embodiment of the present invention.
  • FIG. 15A is a diagram for explaining the estimation of the center position of the flock and the distance information from the center position of each individual
  • FIG. 15B is a calculation formula for the center of the flock.
  • the center point of the flock is estimated from the detection value of the position detection sensor 12, the distance from the coordinates of the center point by a linear distance is measured, and the data is registered in the animal information data base 22.
  • This information can be used as information such as calving prediction (delivery at a position away from the herd), difficulty in moving due to motor disease, management of cows with low social ranking, and so on.
  • the estimated distance from the flock center point at 5 minute, 10 minute, 30 minute, and 1 hour intervals is recorded and used as management information.
  • the center point can be obtained by the formula shown in FIG. 15 (b).
  • the center point (XO, YO) is replaced with the coordinates of latitude and longitude, and the distance (Ln) of each individual position (Xn, Yn) is obtained.
  • Distance information at 1-minute, 2-minute, 10-minute, and 20-minute intervals is registered in the animal information database 22.
  • estrus detection, parturition monitoring, disease detection, and transhumance detection are difficult when targeting all animals. It is possible to detect accidents, grasp the condition of grasslands and the timing of transhumance, and reduce the management burden even if a large number of animals are grazing.
  • the estrus state can be predicted more accurately by adding the interval period from the previous estrus date to the estrus state determination. Further, in the animal state determination step, the movement distance per unit time is calculated from the detection value of the position detection sensor 12, and the calculated movement distance is added to the determination of the estrus state, so that the estrus state can be predicted more accurately. ..
  • the grazing animal management system adjusts the influence of the weather on the behavior by using the weather sensor installed on the pasture and the widely provided weather information in addition to the acceleration sensor 11 and the position detection sensor 12. This makes it possible to notify the timing of estrus detection, delivery detection, disease detection, fence removal, and accidents in grazing, grassland conditions, or transhumance.
  • the data transmission interval from the transmission unit 13 attached to the collar is as long as 5 to 20 minutes, and the loss rate is 3% or less by limiting the number of transmission data to the detection value of the acceleration sensor 11 and the detection value of the position detection sensor 12. Can be done.
  • the data from the transmission unit 13 can be transmitted to and stored in the server 20 by using an internet line via a public wireless system (LPWA).
  • LPWA public wireless system
  • the data received by the receiving unit 21 can be recalculated in the server 20 and displayed by a WEB-compatible application using a large PC monitor in the office or a mobile terminal that can be carried on site.
  • the estrus of each individual can be detected by recalculating in the server 20 at an arbitrary time according to the business hours of the grazing manager.
  • the estrus prediction method is a method of recalculating and predicting 24-hour data retroactively from an arbitrary time, analysis results of 5 minutes, 10 minutes, 20 minutes, and 1 hour intervals and an interval from the previous estrus date. You can choose how to detect estrus signs over time. At the same time, the accuracy can be improved by using the increase / decrease in the moving distance by the position detection sensor 12 as a determination factor for estrus detection.
  • the decrease data of the feeding behavior by the position detection sensor 12 and the acceleration sensor 11 can be used as a trigger. Grazing cows tend to feed on the grass near the fence, travel longer distances, or not go to specific locations when there is less grass in the grassland. Therefore, it is possible to display a tendency that the time zone near the fence is increasing and an alarm prompting the judgment of the edible state of the grassland currently grazing from the number of animals.
  • Notification of cows that have become stuck in grazing can be notified of abnormal cows (state in which they cannot move due to illness, etc.) or dead cows when they are separated from the group for 2 hours or more, or depending on the reaction between the movement distance and acceleration. ..
  • the present invention is suitable for estrus, calving, and injury management, especially for grazing cattle.

Abstract

Provided is a grazing animal management system that includes the following steps: a herd determination step in which a server 20 having an animal information database 22 in which management information for animals is registered determines a herd from the positions in which a plurality of animals are present at a specific time t1; a management candidate animal extraction step in which animals not in the herd determined in the herd determination step are extracted as management candidate animals; and a managed animal specification step in which animals extracted as management candidate animals at least a predetermined number of times when the herd determination step and the management candidate animal extraction step are performed repeatedly at different times are specified as managed animals. As a result, it is possible to focus on the animal behavior of forming a herd, determine that animals straying from the herd are behaving abnormally, and thereby reduce the burden involved in managing a large number of animals.

Description

放牧動物管理システムGrazing animal management system
　本発明は、放牧地に放牧された複数の動物の状態を管理する放牧動物管理システムに関する。 The present invention relates to a grazing animal management system that manages the state of a plurality of animals grazing on a grazing land.
　特許文献１には、放牧地に放牧された牛に首輪をつけ、ＧＰＳ衛星からの電波に基づいた家畜位置データを首輪から送信し、監視センターでは受信した家畜位置データを画面上の放牧地の地図に家畜位置として点画像を表示すること、地図上に牛の位置を示す点画像がかたまりとなって表示されること、移動軌跡データファイルから、子牛が誕生したことを判定することについて記載されている。 In Patent Document 1, a collar is attached to a cow grazing on a pasture, livestock position data based on radio waves from GPS satellites is transmitted from the collar, and the monitoring center transmits the received livestock position data on the screen of the pasture. Describes that a point image is displayed as the livestock position on the map, that the point image showing the position of the cow is displayed as a mass on the map, and that it is determined from the movement trajectory data file that a calf has been born. Has been done.
特開平１０－１６０８２０号公報Japanese Unexamined Patent Publication No. 10-160820
　放牧牛は舎飼牛とは異なる環境下で飼育管理されている。
　一般に昼夜野外にいる放牧環境において個体の管理情報を得るには、個体の位置・移動距離情報（ＧＰＳ）、加速度センサによる行動情報および行動に影響を与える気象情報が必要となる。
　しかし、放牧多頭管理現場での日常業務は少人数で行われ、草地の状態、移牧作業、各牛の観察、発情発見に伴う授精業務、分娩確認・補助と多岐に渡る。
　例えば放牧牛の場合、数十頭程度であれば、全ての牛の移動軌跡や現在位置を監視することも可能であるが、１００頭を越える放牧牛の全ての移動軌跡や現在位置を監視することは困難である。
　すなわち、全頭牛の発情発見、分娩監視、疾病（異状）発見は少人数では困難となっている。 Grazing cattle are bred and managed in a different environment than barn cattle.
Generally, in order to obtain individual management information in a grazing environment outdoors day and night, individual position / travel distance information (GPS), behavior information by an acceleration sensor, and weather information that affects behavior are required.
However, the daily work at the grazing multi-head management site is carried out by a small number of people, and covers a wide range of tasks such as grassland condition, transhumance work, observation of each cow, insemination work associated with estrus detection, and delivery confirmation / assistance.
For example, in the case of grazing cattle, it is possible to monitor the movement locus and current position of all cattle if there are several tens of cattle, but monitor all the movement loci and current position of more than 100 grazing cattle. That is difficult.
In other words, it is difficult for a small number of people to detect estrus, monitor calving, and detect diseases (abnormalities) in all cows.
　そこで、本発明は、群れを作るという動物の習性に着目し、群れから外れた動物を異常として判断することで、多数の動物に対する管理負担を軽減できる放牧動物管理システムを提供することを目的とする。 Therefore, an object of the present invention is to provide a grazing animal management system that can reduce the management burden on a large number of animals by focusing on the habit of animals that form a herd and determining an animal that is out of the herd as an abnormality. To do.
　請求項１記載の本発明の放牧動物管理システムは、放牧地に放牧された複数の動物に、加速度センサ１１、位置検出センサ１２、及びこれらの検出値を個体識別コードとともに送信する送信部１３を取り付け、前記送信部１３から送信される前記検出値によって、前記動物の状態を管理する放牧動物管理システムであって、前記動物の管理情報を登録する動物情報データーベース２２を有するサーバー２０が、特定時刻ｔ１における複数の前記動物の存在位置から群れを判断する群れ判断ステップと、前記群れ判断ステップで判断した前記群れに入らない前記動物を、管理対象候補動物として抽出する管理対象候補動物抽出ステップと、前記群れ判断ステップ及び前記管理対象候補動物抽出ステップを、異なる時刻で複数回繰り返し行い、前記管理対象候補動物として所定回数以上抽出された前記動物を、管理対象動物として特定する管理対象動物特定ステップとを有することを特徴とすることを特徴とする。
　請求項２記載の本発明は、請求項１に記載の放牧動物管理システムにおいて、前記サーバー２０が前記管理対象動物特定ステップで特定された前記管理対象動物について、所定位置に、所定時間以上存在しているか否かを判断する動物状態判断ステップと、前記動物状態判断ステップで、前記所定位置に前記所定時間以上存在していると判断した前記管理対象動物を分娩状態又は要救出状態として出力し、前記所定位置に前記所定時間以上存在していないと判断した前記管理対象動物を発情状態として出力する出力ステップとを有することを特徴とすることを特徴とする。
　請求項３記載の本発明は、請求項１に記載の放牧動物管理システムにおいて、前記サーバー２０が前記管理対象動物特定ステップで特定された前記管理対象動物について、前記加速度センサ１１の前記検出値を用いて単位時間当たりの加速度、又は前記加速度の標準偏差から運動強度を算出し、算出される前記運動強度から発情状態を判断する動物状態判断ステップを有することを特徴とする。
　請求項４記載の本発明は、請求項３に記載の放牧動物管理システムにおいて、前記動物状態判断ステップでは、当日（ｎ）における前記運動強度と、１日前（ｎ－１）からＸ日前（ｎ－Ｘ）までの平均運動強度との比較から判断することを特徴とする。
　請求項５記載の本発明は、請求項３に記載の放牧動物管理システムにおいて、前記動物状態判断ステップでは、１日前（ｎ－１）との比較における当日（ｎ）の前記運動強度から判断することを特徴とする。
　請求項６記載の本発明は、請求項３から請求項５のいずれか１項に記載の放牧動物管理システムにおいて、前記動物状態判断ステップでは、前回の発情日からの間隔期間を前記発情状態の判断に加えることを特徴とする。
　請求項７記載の本発明は、請求項３から請求項６のいずれか１項に記載の放牧動物管理システムにおいて、前記動物状態判断ステップでは、前記位置検出センサ１２の前記検出値から前記単位時間当たりの移動距離を算出し、算出される前記移動距離を前記発情状態の判断に加えることを特徴とする。
　請求項８記載の本発明は、請求項１又は請求項２に記載の放牧動物管理システムにおいて、前記サーバー２０が、前記管理対象動物特定ステップで特定された前記管理対象動物について、前記加速度センサ１１の前記検出値の変化を時系列で表示する管理対象動物表示ステップを有し、前記管理対象動物表示ステップでは、前記管理対象動物特定ステップで特定された前記管理対象動物について、前記加速度センサ１１の前記検出値を用いて単位時間当たりの加速度、又は前記加速度の標準偏差から算出する運動強度の変化を時系列で表示することを特徴とする。 The grazing animal management system of the present invention according to claim 1 provides an acceleration sensor 11, a position detection sensor 12, and a transmission unit 13 that transmits these detected values together with an individual identification code to a plurality of animals grazing on the grazing land. A server 20 that is a grazing animal management system that manages the state of the animal and has an animal information database 22 that registers the management information of the animal is specified by the detection value that is attached and transmitted from the transmission unit 13. A group determination step of determining a flock from the existence positions of a plurality of the animals at time t1 and a management target candidate animal extraction step of extracting the animals that do not belong to the group determined in the group determination step as management target candidate animals. , The group determination step and the management target candidate animal extraction step are repeated a plurality of times at different times, and the management target animal identification step for identifying the animal extracted as the management target candidate animal a predetermined number of times or more as a management target animal. It is characterized by having and.
According to the second aspect of the present invention, in the grazing animal management system according to the first aspect, the server 20 exists at a predetermined position for a predetermined time or longer with respect to the controlled animal specified in the controlled animal identification step. In the animal state determination step for determining whether or not the animal is present, and the animal condition determination step, the managed animal determined to be present at the predetermined position for the predetermined time or longer is output as a delivery state or a rescue-requiring state. It is characterized by having an output step of outputting the controlled animal determined to be absent for the predetermined time or longer at the predetermined position as an estrus state.
According to the third aspect of the present invention, in the grazing animal management system according to the first aspect, the detection value of the acceleration sensor 11 is obtained for the controlled animal identified by the server 20 in the controlled animal identification step. It is characterized by having an animal state determination step of calculating exercise intensity from the acceleration per unit time or the standard deviation of the acceleration, and determining the estrus state from the calculated exercise intensity.
According to the fourth aspect of the present invention, in the grazing animal management system according to the third aspect, in the animal condition determination step, the exercise intensity on the day (n) and one day before (n-1) to X days before (n). It is characterized in that it is judged by comparison with the average exercise intensity up to −X).
The present invention according to claim 5 is determined from the exercise intensity on the day (n) in comparison with one day before (n-1) in the animal state determination step in the grazing animal management system according to claim 3. It is characterized by that.
According to the sixth aspect of the present invention, in the grazing animal management system according to any one of claims 3 to 5, in the animal state determination step, the interval period from the previous estrus date is set to the estrus state. It is characterized by adding to the judgment.
According to the seventh aspect of the present invention, in the grazing animal management system according to any one of claims 3 to 6, in the animal state determination step, the unit time is determined from the detection value of the position detection sensor 12. It is characterized in that the movement distance per hit is calculated and the calculated movement distance is added to the determination of the estrus state.
The present invention according to claim 8 is the acceleration sensor 11 for the controlled animal identified by the server 20 in the controlled animal identification step in the grazing animal management system according to claim 1 or 2. It has a controlled animal display step that displays changes in the detected values in time series, and in the managed animal display step, the acceleration sensor 11 of the controlled animal identified in the managed animal specifying step. It is characterized in that the acceleration per unit time or the change in exercise intensity calculated from the standard deviation of the acceleration is displayed in time series using the detected value.
　本発明によれば、群れから離れて行動する動物を管理対象動物とすることで、全頭を対象とした場合に困難な、発情発見、分娩監視、疾病発見、脱柵発見、事故発見が可能となり、草地の状態や移牧のタイミングも把握可能となり、多数の動物が放牧されていても、管理負担を低減できる。 According to the present invention, by setting an animal that moves away from the herd as a controlled animal, it is possible to detect estrus, monitor delivery, detect a disease, detect a fence, and detect an accident, which is difficult when targeting all animals. Therefore, the condition of the grassland and the timing of transhumance can be grasped, and the management burden can be reduced even if a large number of animals are grazing.
本発明の一実施例による放牧動物管理システムを機能実現手段で表したブロック図A block diagram showing a grazing animal management system according to an embodiment of the present invention as a means for realizing functions. 同放牧動物管理システムのフロー図Flow chart of the grazing animal management system 同放牧動物管理システムを説明する、特定時刻ｔ１におけるイメージ図Image diagram at specific time t1 to explain the grazing animal management system 同放牧動物管理システムを説明する、特定時刻ｔ２におけるイメージ図Image diagram at specific time t2 to explain the grazing animal management system 同放牧動物管理システムを説明する、特定時刻ｔ３におけるイメージ図Image diagram at specific time t3 to explain the grazing animal management system 本発明の他の実施例による放牧動物管理システムでの動物状態判断処理方法を示すグラフA graph showing an animal state determination processing method in a grazing animal management system according to another embodiment of the present invention. 本発明の更に他の実施例による放牧動物管理システムでの動物状態判断処理方法を示す図The figure which shows the animal state judgment processing method in the grazing animal management system by still another Example of this invention. 本発明の更に他の実施例による放牧動物管理システムでの動物状態判断処理方法を示す図The figure which shows the animal state judgment processing method in the grazing animal management system by still another Example of this invention. 本発明の更に他の実施例による放牧動物管理システムでの動物状態判断処理方法を示す図The figure which shows the animal state judgment processing method in the grazing animal management system by still another Example of this invention. 本発明の更に他の実施例による放牧動物管理システムでの動物状態判断処理方法を示すグラフA graph showing an animal state determination processing method in a grazing animal management system according to still another embodiment of the present invention. 本発明の更に他の実施例による放牧動物管理システムでの動物状態判断処理方法を示すグラフA graph showing an animal state determination processing method in a grazing animal management system according to still another embodiment of the present invention. 本発明の更に他の実施例による放牧動物管理システムを示す画面イメージ図Screen image diagram showing a grazing animal management system according to still another embodiment of the present invention. 本発明の更に他の実施例による放牧動物管理システムを示す画面イメージ図Screen image diagram showing a grazing animal management system according to still another embodiment of the present invention. 本発明の更に他の実施例による放牧動物管理システムを示す画面イメージ図Screen image diagram showing a grazing animal management system according to still another embodiment of the present invention. 本発明の更に他の実施例による放牧動物管理システムを示す説明図Explanatory drawing which shows the grazing animal management system by still another Example of this invention
　本発明の第１の実施の形態による放牧動物管理システムは、動物の管理情報を登録する動物情報データーベースを有するサーバーが、特定時刻における複数の動物の存在位置から群れを判断する群れ判断ステップと、群れ判断ステップで判断した群れに入らない動物を、管理対象候補動物として抽出する管理対象候補動物抽出ステップと、群れ判断ステップ及び管理対象候補動物抽出ステップを、異なる時刻で複数回繰り返し行い、管理対象候補動物として所定回数以上抽出された動物を、管理対象動物として特定する管理対象動物特定ステップとを有するものである。本実施の形態によれば、群れから離れて行動する動物を管理対象動物とすることで、全頭を対象とした場合に困難な、発情発見、分娩監視、疾病発見、脱柵発見、事故発見が可能となり、草地の状態や移牧のタイミングも把握可能となり、多数の動物が放牧されていても、管理負担を低減できる。 The grazing animal management system according to the first embodiment of the present invention includes a herd determination step in which a server having an animal information database for registering animal management information determines a herd from the existence positions of a plurality of animals at a specific time. , The management target candidate animal extraction step for extracting the animals that do not belong to the flock judged in the flock judgment step as the management target candidate animals, and the flock judgment step and the management target candidate animal extraction step are repeated multiple times at different times for management. It has a controlled animal identification step of identifying an animal extracted as a target candidate animal a predetermined number of times or more as a controlled animal. According to this embodiment, by targeting animals that move away from the herd as managed animals, estrus detection, delivery monitoring, disease detection, transhumance detection, and accident detection are difficult when targeting all animals. It is possible to grasp the condition of grassland and the timing of transhumance, and even if a large number of animals are grazing, the management burden can be reduced.
　本発明の第２の実施の形態は、第１の実施の形態による放牧動物管理システムにおいて、サーバーが管理対象動物特定ステップで特定された管理対象動物について、所定位置に、所定時間以上存在しているか否かを判断する動物状態判断ステップと、動物状態判断ステップで、所定位置に所定時間以上存在していると判断した管理対象動物を分娩状態又は要救出状態として出力し、所定位置に所定時間以上存在していないと判断した管理対象動物を発情状態として出力する出力ステップとを有するものである。本実施の形態によれば、異常と推定できる動物に対して、分娩状態若しくは要救出状態か、又は発情状態にあるかを予測することができる。 In the second embodiment of the present invention, in the grazing animal management system according to the first embodiment, the server exists at a predetermined position for a predetermined time or longer with respect to the managed animal specified in the managed animal identification step. In the animal condition determination step for determining whether or not the animal is present, and the animal condition determination step, the managed animal determined to be present at a predetermined position for a predetermined time or longer is output as a grazing state or a rescue-requiring state, and is output at a predetermined position for a predetermined time. It has an output step that outputs a controlled animal determined to be nonexistent as an estrus state. According to the present embodiment, it is possible to predict whether an animal that can be presumed to be abnormal is in a state of parturition, a state requiring rescue, or a state of estrus.
　本発明の第３の実施の形態は、第１の実施の形態による放牧動物管理システムにおいて、サーバーが管理対象動物特定ステップで特定された管理対象動物について、加速度センサの検出値を用いて単位時間当たりの加速度、又は加速度の標準偏差から運動強度を算出し、算出される運動強度から発情状態を判断する動物状態判断ステップとを有するものである。本実施の形態によれば、異常と推定できる動物に対して、発情状態を予測することができる。 A third embodiment of the present invention is the grazing animal management system according to the first embodiment, in which the controlled animal identified by the server in the controlled animal identification step is used for a unit time using the detection value of the acceleration sensor. It has an animal state determination step in which the exercise intensity is calculated from the hit acceleration or the standard deviation of the acceleration, and the estrus state is determined from the calculated exercise intensity. According to this embodiment, the estrus state can be predicted for an animal that can be presumed to be abnormal.
　本発明の第４の実施の形態は、第３の実施の形態による放牧動物管理システムにおいて、動物状態判断ステップでは、当日（ｎ）における運動強度と、１日前（ｎ－１）からＸ日前（ｎ－Ｘ）までの平均運動強度との比較から判断するものである。本実施の形態によれば、過去の平均運動強度との比較で、発情状態を予測することができる。 The fourth embodiment of the present invention is the grazing animal management system according to the third embodiment, in the animal condition determination step, the exercise intensity on the day (n) and one day before (n-1) to X days before (n-1). It is judged from the comparison with the average exercise intensity up to nX). According to this embodiment, the estrus state can be predicted by comparing with the past average exercise intensity.
　本発明の第５の実施の形態は、第３の実施の形態による放牧動物管理システムにおいて、動物状態判断ステップでは、１日前（ｎ－１）との比較における当日（ｎ）の運動強度から判断するものである。本実施の形態によれば、前日の運動強度との比較で、発情状態を予測することができる。 The fifth embodiment of the present invention is determined from the exercise intensity of the day (n) in comparison with the one day before (n-1) in the animal state determination step in the grazing animal management system according to the third embodiment. It is something to do. According to this embodiment, the estrus state can be predicted by comparing with the exercise intensity of the previous day.
　本発明の第６の実施の形態は、第３から第５のいずれかの実施の形態による放牧動物管理システムにおいて、動物状態判断ステップでは、前回の発情日からの間隔期間を発情状態の判断に加えるものである。本実施の形態によれば、過去の発情日からの間隔期間を判断に加えることで、発情状態をより正確に予測することができる。 A sixth embodiment of the present invention is a grazing animal management system according to any one of the third to fifth embodiments. In the animal state determination step, the interval period from the previous estrus date is used to determine the estrus state. It is something to add. According to the present embodiment, the estrus state can be predicted more accurately by adding the interval period from the past estrus date to the judgment.
　本発明の第７の実施の形態は、第３から第６のいずれかの実施の形態による放牧動物管理システムにおいて、動物状態判断ステップでは、位置検出センサの検出値から単位時間当たりの移動距離を算出し、算出される移動距離を発情状態の判断に加えるものである。本実施の形態によれば、移動距離を判断に加えることで、発情状態をより正確に予測することができる。 According to the seventh embodiment of the present invention, in the grazing animal management system according to any one of the third to sixth embodiments, in the animal state determination step, the moving distance per unit time is calculated from the detection value of the position detection sensor. It is calculated and the calculated travel distance is added to the judgment of the estrus state. According to the present embodiment, the estrus state can be predicted more accurately by adding the moving distance to the judgment.
　本発明の第８の実施の形態は、第１又は第２の実施の形態による放牧動物管理システムにおいて、管理対象動物特定ステップで特定された管理対象動物について、加速度センサの検出値の変化を時系列で表示する管理対象動物表示ステップを有し、管理対象動物表示ステップでは、管理対象動物特定ステップで特定された管理対象動物について、加速度センサの検出値を用いて単位時間当たりの加速度、又は前記加速度の標準偏差から算出する運動強度の変化を時系列で表示するものである。本実施の形態によれば、異常と推定できる動物に対して健康状態を判断できる。 In the eighth embodiment of the present invention, in the grazing animal management system according to the first or second embodiment, the change of the detection value of the acceleration sensor is timed for the controlled animal specified in the controlled animal identification step. It has a managed animal display step that displays in series, and in the managed animal display step, the acceleration per unit time, or the above, for the managed animal identified in the managed animal identification step, using the detection value of the acceleration sensor. The change in exercise intensity calculated from the standard deviation of acceleration is displayed in chronological order. According to this embodiment, the health condition of an animal that can be presumed to be abnormal can be determined.
　以下本発明の一実施例について図面とともに説明する。
　図１は、本発明の一実施例による放牧動物管理システムを機能実現手段で表したブロック図である。
　本実施例による放牧動物管理システムは、放牧地に放牧された動物に取り付ける端末機１０と、放牧地に放牧された動物の状態を管理するサーバー２０とで構成される。 Hereinafter, an embodiment of the present invention will be described with reference to the drawings.
FIG. 1 is a block diagram showing a grazing animal management system according to an embodiment of the present invention by means for realizing functions.
The grazing animal management system according to the present embodiment includes a terminal 10 attached to an animal grazing on the grazing land and a server 20 that manages the state of the grazing animal on the grazing land.
　例えば、動物が牛のように反芻動物である場合には、端末機１０は頸部に取り付けることが好ましい。端末機１０は、加速度センサ１１、位置検出センサ１２、及びこれらの検出値を個体識別コードとともに送信する送信部１３を備えている。
　加速度センサ１１は、３軸加速度センサが好ましく、３軸加速度センサを用いることで、前後方向加速度、上下方向加速度、及び左右方向加速度を検出することができ、採食、反芻時の咀嚼、及び伏臥休息を、前後方向加速度、上下方向加速度、及び左右方向加速度から判別することができる。例えば、継続する経過時刻における所定時間に、前後方向加速度、上下方向加速度、及び左右方向加速度が、閾値を越えている場合には、反芻時の咀嚼及び伏臥休息ではなく採食であると判別することができる。また、前後方向加速度、上下方向加速度、及び左右方向加速度の少なくともいずれかから、反芻時の咀嚼加速度波形の規則性を判別することで、加速度波形に高い規則性が認められる場合には、健康であると評価することができる。
　位置検出センサ１２は、GNSS(Global Navigation Satellite System/全球測位衛星システム)によるセンサであることが好ましい。
　個体識別コードは、動物の個体を識別できればよく、端末機１０に付与された端末識別コードを用いることもできる。 For example, when the animal is a ruminant such as a cow, the terminal 10 is preferably attached to the neck. The terminal 10 includes an acceleration sensor 11, a position detection sensor 12, and a transmission unit 13 that transmits these detected values together with an individual identification code.
The acceleration sensor 11 is preferably a 3-axis acceleration sensor, and by using the 3-axis acceleration sensor, it is possible to detect anteroposterior acceleration, a vertical acceleration, and a lateral acceleration, and it is possible to detect foraging, chewing during reclamation, and lying down. Rest can be discriminated from the anteroposterior acceleration, the longitudinal acceleration, and the lateral acceleration. For example, if the anteroposterior acceleration, the vertical acceleration, and the lateral acceleration exceed the threshold value at a predetermined time in the continuous elapsed time, it is determined that the food is eaten instead of chewing and lying down during rumination. be able to. In addition, if the regularity of the chewing acceleration waveform at the time of reflex is determined from at least one of the longitudinal acceleration, the vertical acceleration, and the horizontal acceleration, and if a high regularity is recognized in the acceleration waveform, it is healthy. It can be evaluated as being.
The position detection sensor 12 is preferably a sensor based on GNSS (Global Navigation Satellite System).
As the individual identification code, it is sufficient that the individual animal can be identified, and the terminal identification code assigned to the terminal 10 can also be used.
　サーバー２０は、送信部１３から送信される検出値を受信する受信部２１と、受信した検出値を個体識別コード別に時刻情報とともに記憶する動物情報データーベース２２と、制御部２３と、表示部２４とを有する。
　制御部２３は、群れを判断する群れ判断手段２３Ａと、群れに入らない個体を管理対象候補動物として抽出する管理対象候補動物抽出手段２３Ｂと、管理対象動物を特定する管理対象動物特定手段２３Ｃと、管理対象動物について個体の状態を判断する動物状態判断手段２３Ｄとを有する。
　群れ判断手段２３Ａでは、特定時刻における複数の動物の個体存在位置から群れを判断する。例えば、放牧地を所定エリアにメッシュ分割し、所定数の個体が存在するメッシュエリアを抽出し、抽出したメッシュエリアが連続する範囲を群れのエリアと判断することができる。また、個別の個体を中心とする所定範囲に他の個体が所定数存在していれば、群れにいる個体と判断することもできる。
　管理対象候補動物抽出手段２３Ｂでは、特定時刻において、群れ判断ステップで判断した群れに入らない個体を、管理対象候補動物として抽出する。
　管理対象動物特定手段２３Ｃでは、所定期間内に、管理対象候補動物として所定回数以上抽出された個体を、管理対象動物として特定する。
　動物状態判断手段２３Ｄでは、管理対象動物特定ステップで特定された管理対象動物について、所定位置に、所定時間以上存在しているか否かを判断し、所定位置に所定時間以上存在している場合には管理対象動物を分娩状態又は要救出状態と判断し、所定位置に所定時間以上存在していない場合には管理対象動物を発情状態と判断する。
　表示部２４では、管理対象としている動物について、加速度センサ１１の検出値の変化を時系列で表示し、位置検出センサ１２の検出値の変化を時系列で表示することができる。 The server 20 has a receiving unit 21 that receives the detected value transmitted from the transmitting unit 13, an animal information database 22 that stores the received detected value together with time information for each individual identification code, a control unit 23, and a display unit 24. And have.
The control unit 23 includes a herd determination means 23A for determining a herd, a management target animal extraction means 23B for extracting an individual not included in the herd as a management target animal, and a management target animal identification means 23C for identifying a management target animal. The animal condition determination means 23D for determining the individual condition of the animal to be managed is provided.
The herd determination means 23A determines the herd from the individual existence positions of a plurality of animals at a specific time. For example, the grazing land can be divided into mesh areas, a mesh area in which a predetermined number of individuals exist can be extracted, and a range in which the extracted mesh areas are continuous can be determined as a herd area. Further, if a predetermined number of other individuals exist in a predetermined range centered on an individual individual, it can be determined that the individual is in a group.
The management target candidate animal extraction means 23B extracts an individual that does not belong to the herd determined in the herd determination step as a management target candidate animal at a specific time.
The managed animal identification means 23C identifies an individual extracted as a management target candidate animal a predetermined number of times or more as a management target animal within a predetermined period.
The animal state determining means 23D determines whether or not the managed animal identified in the managed animal identification step has been present at a predetermined position for a predetermined time or longer, and when it has been present at the predetermined position for a predetermined time or longer. Determines the managed animal as a calving state or a rescue-requiring state, and determines that the managed animal is in an estrus state if it does not exist at a predetermined position for a predetermined time or longer.
The display unit 24 can display the change in the detection value of the acceleration sensor 11 in time series and the change in the detection value of the position detection sensor 12 in time series for the animal to be managed.
　図２は、同放牧動物管理システムのフロー図である。
　サーバー２０では、受信部２１で検出値を受信すると（Ｓ１）、受信した検出値を動物情報データーベース２２に登録する（Ｓ２）。
　群れ判断ステップでは、群れ判断手段２３Ａが、特定時刻における個体の位置検出センサ１２の検出値を動物情報データーベース２２から抽出し（Ｓ３）、抽出した位置検出センサ１２の検出値から特定時刻における個体の群れを判断する（Ｓ４）。
　管理対象候補動物抽出ステップでは、Ｓ４で判断した群れに入らない個体を、管理対象候補動物として抽出する（Ｓ５）。 FIG. 2 is a flow chart of the grazing animal management system.
When the server 20 receives the detected value in the receiving unit 21 (S1), the server 20 registers the received detected value in the animal information database 22 (S2).
In the flock determination step, the flock determination means 23A extracts the detection value of the individual position detection sensor 12 at the specific time from the animal information database 22 (S3), and the individual at the specific time from the extracted detection value of the position detection sensor 12. Judge the flock of (S4).
In the management target candidate animal extraction step, individuals that do not belong to the herd determined in S4 are extracted as management target candidate animals (S5).
　群れ判断ステップ（Ｓ３、Ｓ４）及び管理対象候補動物抽出ステップ（Ｓ５）を、所定期間が経過するまで、異なる時刻で複数回繰り返し行う（Ｓ６でＮｏ）。ここでの所定期間は、例えば、６時間、１２時間、又は２４時間である。
　所定期間が経過した場合には（Ｓ６でＹｅｓ）、管理対象動物特定ステップに移行する。
　管理対象動物特定ステップでは、管理対象候補動物として所定回数以上抽出されたか否かを判断し（Ｓ７）、管理対象候補動物として所定回数以上抽出された個体を管理対象動物として特定する（Ｓ８）。管理対象候補として抽出されなかった個体、及び管理対象候補動物として抽出された回数が所定回数未満である個体は正常と判断する（Ｓ９）。
　Ｓ８において、管理対象動物として特定された個体については、動物情報データーベース２２から、その個体に関する検出値を抽出する（Ｓ１０）。 The herd determination step (S3, S4) and the management target candidate animal extraction step (S5) are repeated a plurality of times at different times until a predetermined period elapses (No in S6). The predetermined period here is, for example, 6 hours, 12 hours, or 24 hours.
When the predetermined period has elapsed (Yes in S6), the process proceeds to the controlled animal identification step.
In the management target animal identification step, it is determined whether or not the animal has been extracted as a management target candidate animal a predetermined number of times or more (S7), and the individual extracted as a management target candidate animal a predetermined number of times or more is specified as a management target animal (S8). Individuals that are not extracted as management target candidates and individuals that have been extracted as management target candidate animals less than a predetermined number of times are judged to be normal (S9).
For the individual identified as the animal to be managed in S8, the detection value for that individual is extracted from the animal information database 22 (S10).
　管理対象動物表示ステップでは、管理対象動物特定ステップで特定された管理対象動物について、加速度センサ１１の検出値の変化を時系列で表示し（Ｓ１１）、管理対象動物特定ステップで特定された管理対象動物について、位置検出センサ１２の検出値の変化を時系列で表示する（Ｓ１２）。 In the managed animal display step, changes in the detection value of the acceleration sensor 11 are displayed in chronological order for the managed animal specified in the managed animal identification step (S11), and the managed animal specified in the managed animal identification step. For animals, changes in the detected values of the position detection sensor 12 are displayed in chronological order (S12).
　動物状態判断ステップでは、管理対象動物特定ステップで特定された管理対象動物について、所定位置に、所定時間以上存在しているか否かを判断し（Ｓ１３）、出力ステップでは、所定位置に所定時間以上存在していると判断した管理対象動物を分娩状態又は要救出状態として出力し（Ｓ１４）、所定位置に所定時間以上存在していないと判断した管理対象動物を発情状態として出力する（Ｓ１５）。 In the animal state determination step, it is determined whether or not the managed animal identified in the control target animal identification step exists at a predetermined position for a predetermined time or longer (S13), and in the output step, it is determined at a predetermined position for a predetermined time or longer. The managed animal determined to exist is output as a delivery state or a rescue-required state (S14), and the managed animal determined to not exist at a predetermined position for a predetermined time or longer is output as an estrus state (S15).
　図３から図５は、同放牧動物管理システムを説明するイメージ図であり、図３は所定の放牧地における特定時刻ｔ１における動物の存在位置を示し、図４は所定の放牧地における特定時刻ｔ２における動物の存在位置を示し、図５は所定の放牧地における特定時刻ｔ３における動物の存在位置を示している。 3 to 5 are image diagrams for explaining the grazing animal management system, FIG. 3 shows the position of an animal at a specific time t1 in a predetermined rangeland, and FIG. 4 shows a position at a specific time t2 in the predetermined rangeland. The location of the animal is shown, and FIG. 5 shows the location of the animal at a specific time t3 in a predetermined rangeland.
　図３から図５において、破線枠Ｘは群れ判断ステップで判断された群れを示すエリアである。図３では動物１～７が群れに入らないと判断される個体、図４では動物１～３が群れに入らないと判断される個体、図５では動物１～３、８、９が群れに入らないと判断される個体であり、これらの個体は、それぞれの特定時刻ｔ１、特定時刻ｔ２、及び特定時刻ｔ３において管理対象候補動物として抽出される。 In FIGS. 3 to 5, the broken line frame X is an area indicating the group determined in the group determination step. In FIG. 3, animals 1 to 7 are judged not to be in the herd, in FIG. 4 animals 1 to 3 are judged not to be in the herd, and in FIG. 5, animals 1 to 3, 8 and 9 are in the herd. It is an individual that is judged not to enter, and these individuals are extracted as management target candidate animals at the specific time t1, the specific time t2, and the specific time t3, respectively.
　管理対象動物特定ステップでの所定回数を３回とすると、動物１～３が管理対象動物として特定される。
　動物１、３については、特定時刻ｔ１、特定時刻ｔ２、及び特定時刻ｔ３において、異なる位置に存在するため、所定位置に所定時間以上存在していないと判断し、発情状態であると予測できる。
　また、動物２については、特定時刻ｔ１、特定時刻ｔ２、及び特定時刻ｔ３において、同じ位置に存在するため、分娩状態又は要救出状態であると予測できる。 Assuming that the predetermined number of times in the managed animal identification step is three, animals 1 to 3 are specified as managed animals.
Since the animals 1 and 3 exist at different positions at the specific time t1, the specific time t2, and the specific time t3, it can be determined that the animals 1 and 3 do not exist at the predetermined position for a predetermined time or more, and can be predicted to be in an estrus state.
Further, since the animal 2 exists at the same position at the specific time t1, the specific time t2, and the specific time t3, it can be predicted that the animal 2 is in a delivery state or a rescue-requiring state.
　このように、群れから離れて行動する動物を管理対象動物とすることで、多数の動物が放牧されていても、管理負担を低減でき、この管理対象動物について加速度センサ１１の検出値の変化を時系列で表示し、又は位置検出センサ１２の検出値の変化を時系列で表示することで、異常と推定できる動物に対し、健康状態を判断し、又は分娩状態若しくは要救出状態か、又は発情状態にあるかを予測することができる。 In this way, by setting the animals that move away from the flock as the managed animals, the management burden can be reduced even if a large number of animals are grazing, and the change in the detection value of the acceleration sensor 11 for the managed animals can be changed. By displaying in chronological order or by displaying the change in the detection value of the position detection sensor 12 in chronological order, it is possible to judge the health condition of an animal that can be presumed to be abnormal, or to determine the state of delivery, the state requiring rescue, or estrus. You can predict if you are in a state.
　図６は、本発明の他の実施例による放牧動物管理システムでの動物状態判断処理方法を示すグラフであり、図６（ａ）は当日（ｎ）における運動強度の変化を示し、図６（ｂ）は１日前（ｎ－１）からＸ日前（ｎ－Ｘ）までの平均運動強度に対する当日（ｎ）の運動強度の標準偏差を示し、図６（ｃ）は図６（ｂ）から一部のデータをヒストグラム化したグラフを示し、図６（ｄ）は図６（ｃ）の頻度をグラフ化したものを示している。
　本実施例による動物状態判断処理は、図１に示す動物状態判断手段２３Ｄで行われる。
　図６（ａ）は、当日（ｎ）について、加速度センサ１１の検出値を用いて単位時間当たりの加速度を標準化したグラフであり、縦軸を加速度とし横軸を時間としている。高い値が運動強度が強であり、低い値が運動強度が弱となる。
　図６（ｂ）は、１日前（ｎ－１）からＸ日前（ｎ－Ｘ）までの単位時間当たりの加速度の平均値と標準偏差を基に当日（ｎ）の単位時間の加速度を標準化したグラフであり、縦軸を加速度とし横軸を時間としている。高い値は運動強度が強であり、低い値は運動強度が弱である。
　図６（ｃ）は、図６（ｂ）に示す「発情判定したい時間」から２４時間前のデータを切り出し、切り出した２４時間のデータをヒストグラム化している。
　図６（ｄ）は、運動強度の時間分布を示しており、運動強度の時間分布から発情状態を判断することができる。
　また、過去の平均運動強度との比較で、発情状態を予測することができる。
　なお、例えば、１０分、２０分、３０分、及び６０分間隔の加速度又は加速度の標準偏差で運動強度を算出し、算出される運動強度から発情状態を判断することもできる。 FIG. 6 is a graph showing an animal state determination processing method in a grazing animal management system according to another embodiment of the present invention, and FIG. 6A shows a change in exercise intensity on the day (n), and FIG. b) shows the standard deviation of the exercise intensity of the day (n) with respect to the average exercise intensity from one day ago (n-1) to X days ago (nX), and FIG. 6 (c) shows one from FIG. 6 (b). A graph obtained by graphing the data of the part is shown, and FIG. 6 (d) shows a graph of the frequency of FIG. 6 (c).
The animal condition determination process according to this embodiment is performed by the animal condition determination means 23D shown in FIG.
FIG. 6A is a graph in which the acceleration per unit time is standardized using the detection value of the acceleration sensor 11 for the day (n), and the vertical axis is the acceleration and the horizontal axis is the time. A high value means strong exercise intensity, and a low value means weak exercise intensity.
In FIG. 6B, the acceleration per unit time of the day (n) was standardized based on the mean value and standard deviation of the acceleration per unit time from one day ago (n-1) to X days ago (nX). In the graph, the vertical axis is acceleration and the horizontal axis is time. A high value means strong exercise intensity, and a low value means weak exercise intensity.
FIG. 6 (c) cuts out the data 24 hours before the “time for determining estrus” shown in FIG. 6 (b), and visualizes the cut out 24-hour data.
FIG. 6D shows the time distribution of exercise intensity, and the estrus state can be determined from the time distribution of exercise intensity.
In addition, the estrus state can be predicted by comparing with the past average exercise intensity.
In addition, for example, the exercise intensity can be calculated from the acceleration at 10-minute, 20-minute, 30-minute, and 60-minute intervals or the standard deviation of the acceleration, and the estrus state can be determined from the calculated exercise intensity.
　図７は、本発明の更に他の実施例による放牧動物管理システムでの動物状態判断処理方法を示す図であり、図７（ａ）は運動強度の分類閾値を機械学習で計算し設定した後の運動強度の時間分布を示し、図７（ｂ）は図７（ａ）に示す時間分布を３つのカテゴリーにまとめた時間分布を示し、図７（ｃ）は図７（ｂ）に示す３つのカテゴリーに分けた時間分布の発情前後での変化を示している。
　本実施例による動物状態判断処理は、図１に示す動物状態判断手段２３Ｄで行われるが、機械学習の計算はサーバー２０以外で行うこともできる。
　図７（ａ）では、一定間隔の運動強度の大きさについて８つのカテゴリーに分類している。運動強度の大きさは不等間隔とした。機械学習は実験測定により発情牛（＝正解データ、ｎ≧８０ケース）を用いて行っている。
　図７（ｂ）では、図７（ａ）での８つのカテゴリーを３つのカテゴリーにまとめている。３つのカテゴリーでのそれぞれの運動強度の大きさは等間隔である。
　図７（ｃ）では、発情日（８月３０日）、発情の前日（８月２９日）、及び発情の翌日（８月３１日）における、運動強度の増減を示している。
　３つのカテゴリー分布（回数・数値）での発情日の特徴は、発情による休息時間の減少、採食行動の低下、反芻の低下が起こるため、運動強度の低い行動が低下し、中程度（歩行等）が増加する。
　図７（ｃ）に示すように、動物状態判断処理は、１日前（ｎ－１）との比較における当日（ｎ）の運動強度から判断することができ、前日の運動強度との比較で、発情状態を予測することができる。 FIG. 7 is a diagram showing an animal state determination processing method in a grazing animal management system according to still another embodiment of the present invention, and FIG. 7A is a diagram after calculating and setting a classification threshold value of exercise intensity by machine learning. 7 (b) shows the time distribution of the exercise intensity shown in FIG. 7 (a), and FIG. 7 (c) shows the time distribution of the time distribution shown in FIG. 7 (a) in three categories, and FIG. 7 (c) shows 3 shown in FIG. 7 (b). It shows the changes in the time distribution divided into two categories before and after estrus.
The animal state determination process according to this embodiment is performed by the animal state determination means 23D shown in FIG. 1, but the machine learning calculation can also be performed by a server other than the server 20.
In FIG. 7A, the magnitudes of exercise intensity at regular intervals are classified into eight categories. The magnitude of exercise intensity was set at unequal intervals. Machine learning is performed by experimental measurement using estrus cows (= correct answer data, n ≧ 80 cases).
In FIG. 7 (b), the eight categories in FIG. 7 (a) are summarized into three categories. The magnitudes of exercise intensity in each of the three categories are evenly spaced.
FIG. 7 (c) shows the increase / decrease in exercise intensity on the day of estrus (August 30), the day before estrus (August 29), and the day after estrus (August 31).
The characteristics of estrus days in the three category distributions (number of times / numerical values) are that estrus reduces rest time, eating behavior, and rumination, resulting in decreased behavior with low exercise intensity and moderate (walking). Etc.) will increase.
As shown in FIG. 7 (c), the animal state determination process can be determined from the exercise intensity of the day (n) in comparison with the one day before (n-1), and is compared with the exercise intensity of the previous day. The state of estrus can be predicted.
　図８は、本発明の更に他の実施例による放牧動物管理システムでの動物状態判断処理方法を示す図である。
　図８では、放牧育成牛の２４時間データを用いた任意時間での発情検知を示している。
　ユーザーは任意の時間に、「発情兆候・発情」の有無について「検知」指示を行うことで、指示から所定期間前、例えば指示から２４時間前データを動物状態判断手段２３Ｄで、加速度センサ１１の検出値を用いて単位時間当たりの加速度、又は加速度の標準偏差から運動強度を算出し、算出される運動強度から発情状態を判断することにより「発情兆候・発情」を予測することができる。
　また、あらかじめ指示時刻を設定しておくことで、設定時刻前の所定期間における発情状態を予測して表示することができる。従って、放牧管理者の業務・勤務時間の都合に合わせて、任意の指示時刻を設定して発情の有無に関する情報が得られるため、管理者・人工授精の業務スケジュールに合わせた情報提供が可能となる。 FIG. 8 is a diagram showing an animal state determination processing method in a grazing animal management system according to still another embodiment of the present invention.
FIG. 8 shows the detection of estrus at an arbitrary time using 24-hour data of grazing breeding cows.
By issuing a "detection" instruction regarding the presence or absence of "symptoms of estrus / estrus" at an arbitrary time, the user can use the accelerometer 11 to obtain data for a predetermined period before the instruction, for example, 24 hours before the instruction, using the animal state determination means 23D. "Emotional signs / estrus" can be predicted by calculating the exercise intensity from the acceleration per unit time or the standard deviation of the acceleration using the detected value and determining the estrus state from the calculated exercise intensity.
Further, by setting the indicated time in advance, the estrus state in a predetermined period before the set time can be predicted and displayed. Therefore, it is possible to provide information according to the work schedule of the manager / artificial insemination because information on the presence or absence of estrus can be obtained by setting an arbitrary instruction time according to the convenience of the work / working hours of the grazing manager. Become.
　図９は、本発明の更に他の実施例による放牧動物管理システムでの動物状態判断処理方法を示す図であり、図９（ａ）は処理流れを示し、図９（ｂ）は処理結果を示すグラフである。
　本実施例による動物状態判断処理は、図１に示す動物状態判断手段２３Ｄで行われる。
　本実施例は、放牧育成牛の例えば、１０分、２０分、３０分、及び１時間（変動可能）間隔データを用いることでリアルタイムに発情を予測できるものである。
　図９（ａ）に示すように、送信部１３から送られる加速度センサ１１の検出値を標準化処理し（Ｓ３１）、Ｓ３１で標準化処理した運動強度を加算して１時間データとし（Ｓ３２）、この１時間データを２乗処理し（Ｓ３４）、その結果をグラフ表示する（Ｓ３５）。なお、個体ごとに運動強度を算出して発情レベルの係数を算出しておき（Ｓ３３）、Ｓ３５におけるグラフ化では個体ごとの係数を用い、閾値以上時に発情報知を行う（Ｓ３６）。
表示されるグラフには閾値ラインを表示することで、閾値以上のタイミングを発情と予測することができる。
　図９（ｂ）は、Ｓ３４の結果を示すグラフであり、グラフには閾値を表示する。
　閾値は、放牧育成牛の個体ごとに設定し、この閾値（スライスレベル・発情検知レベル）を超えた際に、発情スタート時間として予測が可能となる。業務上、放牧地に管理者がいない場合、その他の事情で発情時間情報が必要な牛について「リアルタイム発情モード」として逐次計算（予測）することが可能となる。 9A and 9B are diagrams showing an animal state determination processing method in a grazing animal management system according to still another embodiment of the present invention, FIG. 9A shows a processing flow, and FIG. 9B shows a processing result. It is a graph which shows.
The animal condition determination process according to this embodiment is performed by the animal condition determination means 23D shown in FIG.
In this embodiment, estrus can be predicted in real time by using, for example, 10-minute, 20-minute, 30-minute, and 1-hour (variable) interval data of grazing breeding cows.
As shown in FIG. 9A, the detection value of the acceleration sensor 11 sent from the transmission unit 13 is standardized (S31), and the exercise intensity standardized in S31 is added to obtain 1-hour data (S32). The 1-hour data is squared (S34), and the result is displayed as a graph (S35). It should be noted that the exercise intensity is calculated for each individual to calculate the coefficient of the estrus level (S33), and the coefficient for each individual is used in the graphing in S35, and the emission information is detected when the threshold value is exceeded (S36).
By displaying the threshold line on the displayed graph, it is possible to predict the timing above the threshold as estrus.
FIG. 9B is a graph showing the result of S34, and the threshold value is displayed on the graph.
The threshold value is set for each individual grazing breeding cow, and when this threshold value (slice level / estrus detection level) is exceeded, the estrus start time can be predicted. If there is no manager on the grazing land for business purposes, it is possible to sequentially calculate (predict) cattle that require estrus time information for other reasons as a "real-time estrus mode".
　次に、本発明の更に他の実施例による放牧動物管理システムでの動物状態判断処理方法を以下に説明する。
　放牧牛の動物状態の一つである分娩予測は下記の判定基準によって行うことができる。
　採食時間の顕著な減少（発情日＜分娩日）、採食時間の変化と同時に起る移動時間の減少（発情日＞分娩日）、特定エリア（半径１５０～５００ｍ）での滞在時間が６０分以上になった場合、及び群れの中心から３０～１０００ｍ以上離れている、の基準で判定できる。
　採食時間は加速度センサ１１による加速度変動によって検出でき、移動時間、特定エリアでの滞在時間、及び群れの中心からの距離は位置検出センサ１２による位置検出によって検出できる。 Next, the animal state determination processing method in the grazing animal management system according to still another embodiment of the present invention will be described below.
The calving prediction, which is one of the animal conditions of grazing cattle, can be made according to the following criteria.
Significant decrease in feeding time (estrus day <delivery day), decrease in travel time that occurs at the same time as the change in feeding time (estrus day> delivery date), staying time in a specific area (radius 150-500 m) 60 It can be judged by the criteria of when it becomes more than a minute and when it is 30 to 1000 m or more away from the center of the flock.
The feeding time can be detected by the acceleration fluctuation by the acceleration sensor 11, and the moving time, the staying time in the specific area, and the distance from the center of the flock can be detected by the position detection by the position detection sensor 12.
　図１０は、本発明の更に他の実施例による放牧動物管理システムでの動物状態判断処理方法を示すグラフであり、異常牛（疾病・事故・死亡）の判定方法を示している。
　図１０は移動距離と加速度変量との相関分布図である。
　図１０に示すように、２０１９／８／１　１９：３０以降ほぼゼロレベルとなることで、疾病、事故、又は死亡などの異常と推定することができる。
　従って、例えば１時間のような所定時間内に、加速度差分値の変動がなく、移動距離が例えば１０ｍのような所定範囲内という、２つの条件を満たした場合に、異常行動と判定し報知することができる。 FIG. 10 is a graph showing a method for determining an animal state in a grazing animal management system according to still another embodiment of the present invention, and shows a method for determining an abnormal cattle (disease / accident / death).
FIG. 10 is a correlation distribution diagram of the moving distance and the acceleration variable.
As shown in FIG. 10, when the level becomes almost zero after 19:30 on August 1, 2019, it can be estimated that there is an abnormality such as a disease, an accident, or death.
Therefore, when the acceleration difference value does not fluctuate within a predetermined time such as 1 hour and the moving distance is within a predetermined range such as 10 m, it is determined as an abnormal behavior and notified. be able to.
　図１１は、本発明の更に他の実施例による放牧動物管理システムでの動物状態判断処理方法を示すグラフであり、水のみ場へ行った回数を示している。
　図１１では、放牧育成牛のある個体について、一週間の期間、緯度・経度データから得られた分布図を示しており、丸で囲った個所が水飲み場である。
　黒枠に囲まれたエリアにプロットされた数が水飲み場に来た回数である。
　雨天時は水飲み場への回数が減少する。また、移動距離も減少傾向にあり、発情時の移動も抑制される。
　従って、動物状態判断の処理では、雨天の場合には雨天以外の天候の場合と、閾値やアルゴリズムを変更することが好ましい。 FIG. 11 is a graph showing the animal state determination processing method in the grazing animal management system according to still another embodiment of the present invention, and shows the number of times the animals went to the water fountain.
FIG. 11 shows a distribution map obtained from latitude / longitude data for a certain individual of grazing breeding cows for a period of one week, and the circled part is a drinking fountain.
The number plotted in the area surrounded by the black frame is the number of visits to the drinking fountain.
When it rains, the number of times you go to the drinking fountain decreases. In addition, the distance traveled is also on a downward trend, and movement during estrus is also suppressed.
Therefore, in the process of determining the animal state, it is preferable to change the threshold value and the algorithm in the case of rainy weather as in the case of weather other than rainy weather.
　図１２は、本発明の更に他の実施例による放牧動物管理システムを示す画面イメージ図であり、放牧育成牛の滞在時間をマップ上に示している。
　図１２において、小径丸印は２０分以下の滞在、中径丸印は２１分以上６０分以下、大径丸印は６１分以上２時間までを示している。
　このように、放牧育成牛の草地滞在時間の変動情報を位置情報（マップ情報）とともに出力することで草原管理を行うことができる。 FIG. 12 is a screen image diagram showing a grazing animal management system according to still another embodiment of the present invention, and shows the staying time of grazing breeding cattle on a map.
In FIG. 12, the small diameter circle indicates a stay of 20 minutes or less, the medium diameter circle indicates a stay of 21 minutes or more and 60 minutes or less, and the large diameter circle indicates 61 minutes or more and up to 2 hours.
In this way, grassland management can be performed by outputting the fluctuation information of the grassland staying time of the grazing breeding cow together with the position information (map information).
　図１３は、本発明の更に他の実施例による放牧動物管理システムを示す画面イメージ図であり、群れ全体がどの時間にどの辺に滞在していたかを分布で示している。
　図１３（ａ）では、白丸印は４：００～１８：００、黒丸印は１８：００～４：００の滞在を示し、図１３（ｂ）では、図１３（ａ）におけるＡ～ＦとＰ１～Ｐ５とでメッシュ区分された場所における群れでの平均滞在時間を示している。
　このように、草原の中のどの場所でどれくらいの時間をかけて採食しているかを可視化しマップ上において表示することができる。 FIG. 13 is a screen image diagram showing a grazing animal management system according to still another embodiment of the present invention, and shows a distribution showing which side the entire herd stayed at at what time.
In FIG. 13 (a), the white circles indicate the stays from 4:00 to 18:00, the black circles indicate the stays from 18:00 to 4:00, and in FIG. 13 (b), A to F in FIG. 13 (a). It shows the average staying time in a group in the place mesh-divided by P1 to P5.
In this way, it is possible to visualize where and how long it takes to eat in the grassland and display it on the map.
　図１４は、本発明の更に他の実施例による放牧動物管理システムを示す画面イメージ図であり、変化のある個体を抽出することができる出力イメージ図である。
　図１４（ａ）は、個体別に前日と当日との移動距離及び活動量を比較した一覧リスト出力であり、図１４（ｂ）は、図１４（ａ）の一覧リストから特定の個体の現在位置をマップ上に表示したものである。
　このように、個体別に前日と当日との移動距離及び活動量を比較し、変化がある個体を摘出することもでき、群れ判断手段２３Ａに代えて、又は群れ判断手段２３Ａでの判断とともに、管理対象候補動物として抽出することもできる。 FIG. 14 is a screen image diagram showing a grazing animal management system according to still another embodiment of the present invention, and is an output image diagram capable of extracting a changing individual.
FIG. 14 (a) is a list list output comparing the distance traveled and the amount of activity between the previous day and the current day for each individual, and FIG. 14 (b) is the current position of a specific individual from the list in FIG. 14 (a). Is displayed on the map.
In this way, the distance traveled and the amount of activity between the previous day and the day can be compared for each individual, and individuals with changes can be extracted, and managed in place of the flock judgment means 23A or together with the judgment by the flock judgment means 23A. It can also be extracted as a target candidate animal.
　図１５は、本発明の更に他の実施例による放牧動物管理システムを示す説明図である。
　図１５（ａ）は、群れの中心位置の推定と各個体の中心位置からの距離情報を説明する図であり、図１５（ｂ）は群れ中心の計算式である。
　位置検出センサ１２の検出値から群れの中心点を推測し、中心点の座標から直線距離でどれくらい離れているかを計測し動物情報データ－ベース２２に登録する。この情報は、分娩予知（群れから離れた位置での分娩）、運動疾患による移動の困難、社会的順位の低い牛の管理、などの情報として利用可能となる。５分、１０分、３０分、及び１時間間隔での、推定される群れ中心点からの距離を記録し管理情報として使用する。
　なお、中心点は図１５（ｂ）の計算式によって求めることができ、中心点（ＸＯ，ＹＯ）を緯度経度の座標に置き換え、各個体位置（Ｘｎ，Ｙｎ）の距離(Ln)を求め、１分、２分、１０分、及び２０分間隔の距離情報を動物情報データ－ベース２２に登録する。 FIG. 15 is an explanatory diagram showing a grazing animal management system according to still another embodiment of the present invention.
FIG. 15A is a diagram for explaining the estimation of the center position of the flock and the distance information from the center position of each individual, and FIG. 15B is a calculation formula for the center of the flock.
The center point of the flock is estimated from the detection value of the position detection sensor 12, the distance from the coordinates of the center point by a linear distance is measured, and the data is registered in the animal information data base 22. This information can be used as information such as calving prediction (delivery at a position away from the herd), difficulty in moving due to motor disease, management of cows with low social ranking, and so on. The estimated distance from the flock center point at 5 minute, 10 minute, 30 minute, and 1 hour intervals is recorded and used as management information.
The center point can be obtained by the formula shown in FIG. 15 (b). The center point (XO, YO) is replaced with the coordinates of latitude and longitude, and the distance (Ln) of each individual position (Xn, Yn) is obtained. Distance information at 1-minute, 2-minute, 10-minute, and 20-minute intervals is registered in the animal information database 22.
　以上のように本実施例によれば、群れから離れて行動する動物を管理対象動物とすることで、全頭を対象とした場合に困難な、発情発見、分娩監視、疾病発見、脱柵発見、事故発見が可能となり、草地の状態や移牧のタイミングも把握可能となり、多数の動物が放牧されていても、管理負担を低減できる。
　また、動物状態判断ステップでは、前回の発情日からの間隔期間を発情状態の判断に加えることで、発情状態をより正確に予測することができる。
　また、動物状態判断ステップでは、位置検出センサ１２の検出値から単位時間当たりの移動距離を算出し、算出される移動距離を発情状態の判断に加える、発情状態を更に正確に予測することができる。 As described above, according to this embodiment, by targeting animals that move away from the herd as managed animals, estrus detection, parturition monitoring, disease detection, and transhumance detection are difficult when targeting all animals. It is possible to detect accidents, grasp the condition of grasslands and the timing of transhumance, and reduce the management burden even if a large number of animals are grazing.
Further, in the animal state determination step, the estrus state can be predicted more accurately by adding the interval period from the previous estrus date to the estrus state determination.
Further, in the animal state determination step, the movement distance per unit time is calculated from the detection value of the position detection sensor 12, and the calculated movement distance is added to the determination of the estrus state, so that the estrus state can be predicted more accurately. ..
　本発明による放牧動物管理システムは、加速度センサ１１及び位置検出センサ１２に加えて、放牧地に設置した気象センサや、広く提供されている気象情報を用いて、気象による行動への影響を調整することにより、発情検知、分娩検知、疾病検知、脱柵、及び放牧内での事故、草地の状態、又は移牧のタイミングを報知することができる。
　首輪に取り付けた送信部１３からのデータ送信間隔は、５～２０分間隔と長く、送信データ数を加速度センサ１１の検出値と位置検出センサ１２の検出値に限ることで欠損率は３％以下とすることができている。
　送信部１３からのデータは公衆無線システム（LPWA）を介してインターネット回線を用いることでサーバー２０に送信し保存することができる。
　受信部２１で受信するデータはサーバー２０内で再計算し、ＷＥＢ対応アプリケーションにより事務所内の大型ＰＣモニターや現場にキャリー可能なモバイル端末を用いて表示できる。
　放牧管理者の業務時間に合わせた任意の時間にサーバー２０内で再計算し、各個体の発情を検知することができる。
　発情予測方法は、任意の時間から２４時間データを遡って再計算して予測する方法と、５分、１０分、２０分、及び１時間間隔の分析結果と１つ前の発情日からの間隔期間を使った発情兆候を検出する方法とを選択できる。また同時に位置検出センサ１２による移動距離の増減を発情検知の判断要素として用いていることで確度を上げることができる。
　分娩が近い牛は、移動距離が長く採食量が減少するため位置検出センサ１２と加速度センサ１１とによる採食行動の減少データをトリガーとすることができる。
　放牧牛は草地内に草が少なくなると、牧柵近くの淵の草を採食する傾向、移動距離が長くなる傾向、又は特定場所に行かなくなる傾向がある。従って、牧柵近くにいる時間帯が増加する傾向と、頭数から現在放牧している草地の採食可能状態の判断を促す警報を表示することができる。
　放牧内で動かなくなった牛の報知は、群から２時間以上離れた場合や、移動距離と加速度の反応により、異常牛（疾病等により移動ができない状態）か死亡牛の報知を行うことができる。 The grazing animal management system according to the present invention adjusts the influence of the weather on the behavior by using the weather sensor installed on the pasture and the widely provided weather information in addition to the acceleration sensor 11 and the position detection sensor 12. This makes it possible to notify the timing of estrus detection, delivery detection, disease detection, fence removal, and accidents in grazing, grassland conditions, or transhumance.
The data transmission interval from the transmission unit 13 attached to the collar is as long as 5 to 20 minutes, and the loss rate is 3% or less by limiting the number of transmission data to the detection value of the acceleration sensor 11 and the detection value of the position detection sensor 12. Can be done.
The data from the transmission unit 13 can be transmitted to and stored in the server 20 by using an internet line via a public wireless system (LPWA).
The data received by the receiving unit 21 can be recalculated in the server 20 and displayed by a WEB-compatible application using a large PC monitor in the office or a mobile terminal that can be carried on site.
The estrus of each individual can be detected by recalculating in the server 20 at an arbitrary time according to the business hours of the grazing manager.
The estrus prediction method is a method of recalculating and predicting 24-hour data retroactively from an arbitrary time, analysis results of 5 minutes, 10 minutes, 20 minutes, and 1 hour intervals and an interval from the previous estrus date. You can choose how to detect estrus signs over time. At the same time, the accuracy can be improved by using the increase / decrease in the moving distance by the position detection sensor 12 as a determination factor for estrus detection.
Since the cows that are close to calving have a long travel distance and the amount of food intake decreases, the decrease data of the feeding behavior by the position detection sensor 12 and the acceleration sensor 11 can be used as a trigger.
Grazing cows tend to feed on the grass near the fence, travel longer distances, or not go to specific locations when there is less grass in the grassland. Therefore, it is possible to display a tendency that the time zone near the fence is increasing and an alarm prompting the judgment of the edible state of the grassland currently grazing from the number of animals.
Notification of cows that have become stuck in grazing can be notified of abnormal cows (state in which they cannot move due to illness, etc.) or dead cows when they are separated from the group for 2 hours or more, or depending on the reaction between the movement distance and acceleration. ..
　本発明は、特に放牧牛について、発情、分娩、及び怪我の管理に適している。 The present invention is suitable for estrus, calving, and injury management, especially for grazing cattle.
　１～９　動物
　１０　端末機
　１１　加速度センサ
　１２　位置検出センサ
　１３　送信部
　２０　サーバー
　２１　受信部
　２２　動物情報データーベース
　２３　制御部
　２３Ａ　群れ判断手段
　２３Ｂ　管理対象候補動物抽出手段
　２３Ｃ　管理対象動物特定手段
　２３Ｄ　動物状態判断手段
　Ｘ　破線枠 1 to 9 Animals 10 Terminals 11 Accelerometer 12 Position detection sensor 13 Transmitter 20 Server 21 Receiver 22 Animal information database 23 Control unit 23A Flock judgment means 23B Management target candidate animal extraction means 23C Management target animal identification means 23D Animal condition Judgment means X Broken frame

Claims (8)

  1. 　放牧地に放牧された複数の動物に、加速度センサ、位置検出センサ、及びこれらの検出値を個体識別コードとともに送信する送信部を取り付け、
    前記送信部から送信される前記検出値によって、前記動物の状態を管理する放牧動物管理システムであって、
    前記動物の管理情報を登録する動物情報データーベースを有するサーバーが、
    特定時刻における複数の前記動物の存在位置から群れを判断する群れ判断ステップと、
    前記群れ判断ステップで判断した前記群れに入らない前記動物を、管理対象候補動物として抽出する管理対象候補動物抽出ステップと、
    前記群れ判断ステップ及び前記管理対象候補動物抽出ステップを、異なる時刻で複数回繰り返し行い、前記管理対象候補動物として所定回数以上抽出された前記動物を、管理対象動物として特定する管理対象動物特定ステップと
    を有することを特徴とする放牧動物管理システム。     Accelerometers, position detection sensors, and transmitters that transmit these detections along with individual identification codes are attached to multiple animals grazing on the pasture.
    A grazing animal management system that manages the state of the animal based on the detected value transmitted from the transmitting unit.
    A server that has an animal information database that registers the animal management information
    A herd determination step for determining a herd from the existence positions of a plurality of the animals at a specific time,
    A management target candidate animal extraction step for extracting the animals that do not belong to the herd determined in the herd determination step as management target candidate animals,
    The group determination step and the management target animal extraction step are repeated a plurality of times at different times, and the animal extracted as the management target animal a predetermined number of times or more is specified as a management target animal identification step. A grazing animal management system characterized by having.
  2. 　前記サーバーが
    前記管理対象動物特定ステップで特定された前記管理対象動物について、所定位置に、所定時間以上存在しているか否かを判断する動物状態判断ステップと、
    前記動物状態判断ステップで、前記所定位置に前記所定時間以上存在していると判断した前記管理対象動物を分娩状態又は要救出状態として出力し、前記所定位置に前記所定時間以上存在していないと判断した前記管理対象動物を発情状態として出力する出力ステップと
    を有することを特徴とする請求項１に記載の放牧動物管理システム。     An animal state determination step for determining whether or not the server exists at a predetermined position for a predetermined time or longer with respect to the controlled animal identified in the managed animal identification step.
    In the animal state determination step, the managed animal determined to be present at the predetermined position for the predetermined time or longer is output as a delivery state or a rescue required state, and the animal is not present at the predetermined position for the predetermined time or longer. The grazing animal management system according to claim 1, further comprising an output step of outputting the determined managed animal as an estrus state.
  3. 　前記サーバーが
    前記管理対象動物特定ステップで特定された前記管理対象動物について、前記加速度センサの前記検出値を用いて単位時間当たりの加速度、又は前記加速度の標準偏差から運動強度を算出し、算出される前記運動強度から発情状態を判断する動物状態判断ステップ
    を有することを特徴とする請求項１に記載の放牧動物管理システム。     For the controlled animal identified by the server in the controlled animal identification step, the exercise intensity is calculated from the acceleration per unit time or the standard deviation of the acceleration using the detected value of the acceleration sensor. The grazing animal management system according to claim 1, further comprising an animal state determination step for determining an estrus state from the exercise intensity.
  4. 　前記動物状態判断ステップでは、
    当日（ｎ）における前記運動強度と、１日前（ｎ－１）からＸ日前（ｎ－Ｘ）までの平均運動強度との比較から判断する
    ことを特徴とする請求項３に記載の放牧動物管理システム。     In the animal condition determination step,
    The grazing animal management according to claim 3, wherein the grazing animal management is determined by comparing the exercise intensity on the day (n) with the average exercise intensity from one day before (n-1) to X days before (nX). system.
  5. 　前記動物状態判断ステップでは、
    １日前（ｎ－１）との比較における当日（ｎ）の前記運動強度から判断する
    ことを特徴とする請求項３に記載の放牧動物管理システム。     In the animal condition determination step,
    The grazing animal management system according to claim 3, wherein the grazing animal management system is determined from the exercise intensity of the day (n) in comparison with one day before (n-1).
  6. 　前記動物状態判断ステップでは、
    前回の発情日からの間隔期間を前記発情状態の判断に加える
    ことを特徴とする請求項３から請求項５のいずれか１項に記載の放牧動物管理システム。     In the animal condition determination step,
    The grazing animal management system according to any one of claims 3 to 5, wherein an interval period from the previous estrus date is added to the determination of the estrus state.
  7. 　前記動物状態判断ステップでは、
    前記位置検出センサの前記検出値から前記単位時間当たりの移動距離を算出し、算出される前記移動距離を前記発情状態の判断に加える
    ことを特徴とする請求項３から請求項６のいずれか１項に記載の放牧動物管理システム。     In the animal condition determination step,
    Any one of claims 3 to 6, wherein the movement distance per unit time is calculated from the detection value of the position detection sensor, and the calculated movement distance is added to the determination of the estrus state. The grazing animal management system described in the section.
  8. 　前記サーバーが、
    前記管理対象動物特定ステップで特定された前記管理対象動物について、前記加速度センサの前記検出値の変化を時系列で表示する管理対象動物表示ステップ
    を有し、
    前記管理対象動物表示ステップでは、
    前記管理対象動物特定ステップで特定された前記管理対象動物について、前記加速度センサの前記検出値を用いて単位時間当たりの加速度、又は前記加速度の標準偏差から算出する運動強度の変化を時系列で表示する
    ことを特徴とする請求項１又は請求項２に記載の放牧動物管理システム。     The server
    For the controlled animal identified in the controlled animal identification step, the controlled animal display step for displaying the change of the detected value of the acceleration sensor in time series is provided.
    In the controlled animal display step,
    For the controlled animal identified in the controlled animal identification step, the acceleration per unit time or the change in exercise intensity calculated from the standard deviation of the acceleration is displayed in chronological order using the detected value of the acceleration sensor. The grazing animal management system according to claim 1 or 2, wherein the grazing animal management system is characterized in that.
PCT/JP2020/031342 2019-08-20 2020-08-19 Grazing animal management system WO2021033732A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2021540975A JP7228171B2 (en) 2019-08-20 2020-08-19 Grazing animal management system

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2019-150738 2019-08-20
JP2019150738 2019-08-20

Publications (1)

Publication Number Publication Date
WO2021033732A1 true WO2021033732A1 (en) 2021-02-25

Family

ID=74661151

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2020/031342 WO2021033732A1 (en) 2019-08-20 2020-08-19 Grazing animal management system

Country Status (2)

Country Link
JP (1) JP7228171B2 (en)
WO (1) WO2021033732A1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114287361A (en) * 2022-01-20 2022-04-08 中国农业科学院农业信息研究所 Livestock behavior monitoring and analyzing system and method

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS5846292B2 (en) * 1980-01-24 1983-10-15 カバヤ食品株式会社 Continuous manufacturing equipment for rod-shaped tyokolate
JPH10295212A (en) * 1997-04-28 1998-11-10 Matsushita Electric Works Ltd Pasturing managing device and pasturing managing system using the same
JPH11128210A (en) * 1997-10-29 1999-05-18 Matsushita Electric Works Ltd Animal exercise quantity controlling device and its system, and recording medium storing animal exercise control program
US20070044732A1 (en) * 2005-08-29 2007-03-01 St-Infonox Animal-herd management using distributed sensor networks
JP4229757B2 (en) * 2003-05-16 2009-02-25 北海道 Estrus discovery method and apparatus for dairy cows
JP2011234668A (en) * 2010-05-10 2011-11-24 Technos Japan:Kk Sensor for detecting delivery and heat of livestock, detection device and detection method using the same
GB2548190A (en) * 2015-12-23 2017-09-13 Inst Of Tech Sligo A lambing monitoring system
WO2017183350A1 (en) * 2016-04-21 2017-10-26 ソニー株式会社 Information processing device, information processing system, and information processing method

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2832220A4 (en) * 2012-03-30 2015-06-24 Fujitsu Ltd Correction method for number of steps, correction device for number of steps, correction program for number of steps, estrus notification method, estrus notification device
WO2013145302A1 (en) * 2012-03-30 2013-10-03 富士通株式会社 Estrus notification method, estrus notification device and estrus notification program

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS5846292B2 (en) * 1980-01-24 1983-10-15 カバヤ食品株式会社 Continuous manufacturing equipment for rod-shaped tyokolate
JPH10295212A (en) * 1997-04-28 1998-11-10 Matsushita Electric Works Ltd Pasturing managing device and pasturing managing system using the same
JPH11128210A (en) * 1997-10-29 1999-05-18 Matsushita Electric Works Ltd Animal exercise quantity controlling device and its system, and recording medium storing animal exercise control program
JP4229757B2 (en) * 2003-05-16 2009-02-25 北海道 Estrus discovery method and apparatus for dairy cows
US20070044732A1 (en) * 2005-08-29 2007-03-01 St-Infonox Animal-herd management using distributed sensor networks
JP2011234668A (en) * 2010-05-10 2011-11-24 Technos Japan:Kk Sensor for detecting delivery and heat of livestock, detection device and detection method using the same
GB2548190A (en) * 2015-12-23 2017-09-13 Inst Of Tech Sligo A lambing monitoring system
WO2017183350A1 (en) * 2016-04-21 2017-10-26 ソニー株式会社 Information processing device, information processing system, and information processing method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114287361A (en) * 2022-01-20 2022-04-08 中国农业科学院农业信息研究所 Livestock behavior monitoring and analyzing system and method

Also Published As

Publication number Publication date
JP7228171B2 (en) 2023-02-24
JPWO2021033732A1 (en) 2021-02-25

Similar Documents

Publication Publication Date Title
US11589559B2 (en) Adaptive sensor performance based on risk assessment
US11763205B2 (en) Livestock and feedlot data collection and processing using UHF-band interrogation of radio frequency identification tags for feedlot arrival and risk assessment
US10045511B1 (en) Cattle and other veterinary monitoring
US20170202185A1 (en) Unmanned livestock monitoring system and methods of use
US8677941B2 (en) System and methods for health monitoring of anonymous animals in livestock groups
US20180146645A1 (en) System and method for monitoring livestock
Bailey et al. Opportunities to apply precision livestock management on rangelands
MX2011008205A (en) Vitality meter for health monitoring of anonymous animals in livestock groups.
Firk et al. Improving oestrus detection by combination of activity measurements with information about previous oestrus cases
US20220192150A1 (en) Livestock Management System
US20210076644A1 (en) Animal health, behavior, and inventory monitoring
Smith et al. An integrated cattle health monitoring system
Tekin et al. Precision livestock farming technologies: Novel direction of information flow
WO2021033732A1 (en) Grazing animal management system
US20030137431A1 (en) System and method for improving animal breeding efficiency
Dela Rue et al. Using activity-based monitoring systems to detect dairy cows in oestrus: a field evaluation
JPWO2021033732A5 (en)
US20200396962A1 (en) Smart cattle reproduction management and digital farm to market transparency metrics
Simanungkalit et al. Validation of automatic systems for monitoring the licking behaviour in Angus and Brahman cattle
WO2021015683A1 (en) A herd management system and a tracking sensor
Szczecinski Catch susceptibility and life history of barred javelin (Pomadasys kaakan) in north eastern Queensland, Australia
Glasson et al. ‘uSuckled’: Detection of maternal behaviours associated with suckling in beef cattle
WO2022115916A1 (en) Livestock monitoring and management
Tobin Potential for Precision Livestock Management Applications on Cattle Ranches
WO2024058724A1 (en) Method and system for matching task relating to cattle with entity

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20854881

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2021540975

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20854881

Country of ref document: EP

Kind code of ref document: A1