Estimation system, estimation method, and program

Abstract

SOLUTION: Provided is an estimation system including a learning model storage unit configured to store a learning model generated by machine learning using learning data including measurement data obtained by measuring an inside of a body of a livestock animal and a body component of the livestock animal, a measurement data acquisition unit configured to acquire measurement data obtained by measuring the inside of the body of a livestock animal that is an estimation target, and an estimation unit configured to estimate the body component of the livestock animal that is the estimation target by using the measurement data acquired by the measurement data acquisition unit and the learning model. The measurement data may include at least one of electric measurement data generated by applying electricity to the inside of the body of the livestock animal, elastic wave measurement data generated by irradiating the inside of the body of the livestock animal with an elastic wave, magnetic field measurement data generated by applying a magnetic field to the inside of the body of the livestock animal, or electromagnetic wave measurement data generated by irradiating the inside of the body of the livestock animal with an electromagnetic wave.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an estimation system, an estimation method, and a program. [Background technology]

[0002] Patent Document 1 describes an "estimation system for estimating the health status of animals such as cows using a learning model." Patent Document 2 describes a "ruminant management device for managing the behavior of ruminants." Patent Document 3 describes a "livestock management system for managing livestock such as cows raised for meat consumption, and a livestock bioimpedance measuring device for use therein." Patent Document 4 describes a "calculation unit for calculating a body type value related to the body type of an animal body" and an "estimation unit for estimating the weight of the animal body based on the body type value of the animal body." [Prior art document] [Patent documents] [Patent Document 1] JP 2021-136868 A [Patent Document 2] JP 2011-103793 A [Patent Document 3] Japanese Patent Application Laid-Open No. 2002-253523 [Patent Document 4] JP 2021-016376 A Summary of the Invention [Means for solving the problem]

[0003] According to one embodiment of the present invention, there is provided an estimation system. The estimation system may include a learning model storage unit that stores a learning model generated by machine learning using learning data including measurement data that measures the inside of the livestock and internal components of the livestock. The estimation system may include a measurement data acquisition unit that acquires measurement data that measures the inside of the livestock that is to be estimated. The estimation system may include an estimation unit that estimates the internal components of the livestock that is to be estimated using the measurement data acquired by the measurement data acquisition unit and the learning model.

[0004] In the estimation system, the measurement data may include at least any of electrical measurement data generated by applying electricity to the body of the livestock, elastic wave measurement data generated by irradiating elastic waves into the body of the livestock, magnetic field measurement data generated by applying a magnetic field to the body of the livestock, and electromagnetic wave measurement data generated by irradiating electromagnetic waves into the body of the livestock. In any of the estimation systems, the measurement data may include two or more of the electrical measurement data, the elastic wave measurement data, the magnetic field measurement data, and the electromagnetic wave measurement data. In any of the estimation systems, the internal components may include at least any of muscle, fat, bone, and visceral elements.

[0005] In any of the estimation systems, the estimation unit may estimate a fat component in muscle of the livestock. In the estimation system, the estimation unit may estimate at least one of a carcass weight of the livestock to be estimated and a meat grade of the livestock to be estimated.

[0006] In any of the above estimation systems, the measurement data acquisition unit may acquire the measurement data in time series, and the estimation unit may estimate the internal components of the estimation target livestock in time series by inputting the time series measurement data into the learning model. The estimation system may further include a determination unit that determines whether a feeding method for the estimation target livestock needs to be changed based on the internal components in time series estimated by the estimation unit.

[0007] In any of the above estimation systems, the learning model storage unit may store the learning model generated by machine learning using learning data including the measurement data and the internal components of the livestock and attribute data of the livestock. The estimation system may further include an attribute data acquisition unit that acquires attribute data of the livestock to be estimated. The estimation unit may estimate the internal components of the livestock to be estimated by inputting the measurement data acquired by the measurement data acquisition unit and the attribute data acquired by the attribute data acquisition unit into the learning model.

[0008] Any of the estimation systems may further include a learning model generation unit that generates a learning model by machine learning using learning data including the measurement data obtained by measuring the inside of the livestock and the internal components of the livestock.

[0009] According to one embodiment of the present invention, there is provided an estimation method executed by a computer, which may include a measurement data acquisition step of acquiring measurement data obtained by measuring the inside of a livestock to be estimated, and an estimation step of estimating the inside components of the livestock to be estimated using a learning model generated by machine learning using learning data including the measurement data obtained in the measurement data acquisition step and the inside components of the livestock.

[0010] According to one embodiment of the present invention, there is provided a program for causing a computer to execute the estimation method.

[0011] The above summary of the invention does not list all of the necessary features of the present invention, and subcombinations of these features may also constitute inventions. [Brief explanation of the drawings]

[0012] [Figure 1] 1 illustrates a schematic diagram of an example estimation system 10. [Figure 2] 10A and 10B show schematic diagrams of an example of training data. [Figure 3] 10A and 10B show schematic diagrams of an example of training data. [Figure 4] 1 illustrates a schematic diagram of an example estimation system 10. [Figure 5] FIG. 2 is an explanatory diagram illustrating an example of a processing flow by the estimation system 10. [Figure 6] An example of the hardware configuration of a computer 1200 that functions as the estimation system 10 or the estimation device 100 is shown in schematic form. DETAILED DESCRIPTION OF THE INVENTION

[0013] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0014] Traditionally, livestock farmers have relied on experience and observation to control the weight and food intake of livestock. In recent years, technology has been reported that uses video data from livestock barns and data from sensors attached to the necks of livestock, such as body temperature, sound, and acceleration, to use AI (artificial intelligence) to estimate the health status (abnormalities such as infectious diseases) of individual livestock.

[0015] Although it has been possible to estimate the health condition of livestock through their behavior using images of the livestock or sensors attached to their necks, it has been difficult to estimate the detailed internal components of livestock (such as body weight and body fat percentage).The estimation system according to this embodiment estimates the internal components of a livestock from measurement data obtained by measuring the inside of the livestock's body and using a learning model generated by machine learning using learning data including measurement data obtained by measuring the inside of the livestock's body and the internal components of the livestock.

[0016] FIG. 1 schematically illustrates an example of an estimation system 10. The estimation system 10 includes an estimation device 100. The estimation device 100 may include a measurement data acquisition unit 110, a learning model storage unit 120, and an estimation unit 130. The estimation system 10 may include a measurement device 200. The measurement device 200 measures the inside of the body of a livestock 50 and generates measurement data 20. The measurement device 200 measures the inside of the body of the livestock 50, for example, by applying a non-invasive signal to the inside of the body of the livestock 50. The livestock 50 is, for example, a cow, a pig, a sheep, etc.

[0017] The type of the measuring device 200 included in the estimation system 10 is not particularly limited. For example, the measuring device 200 may be a measuring device that applies electricity to the inside of the body of the livestock 50. For example, the measuring device 200 may be a measuring device that irradiates elastic waves into the body of the livestock 50. For example, the measuring device 200 may be a measuring device that applies a magnetic field to the inside of the body of the livestock 50. For example, the measuring device 200 may be a measuring device that irradiates electromagnetic waves into the body of the livestock 50.

[0018] The measuring device 200 may have a function of emitting electricity, elastic waves, magnetic fields, electromagnetic waves, etc. (sometimes referred to as electricity, etc.), and a function of receiving electricity, etc. When the estimation system 10 includes a plurality of measuring devices 200, the function of emitting electricity, etc. and the function of receiving electricity, etc. may be distributed among the plurality of measuring devices 200. For example, electricity, etc. emitted by a measuring device 200 installed in one ear of the livestock 50 may be received by a measuring device 200 installed in the other ear of the livestock 50. For example, electricity, etc. emitted by a measuring device 200 installed in the ear of the livestock 50 may be received by a measuring device 200 installed in the buttocks of the livestock 50. For example, electricity, etc. emitted by one measuring device 200 may be received by one measuring device 200. For example, electricity, etc. emitted by multiple measuring devices 200 may be received by multiple measuring devices 200. For example, electricity, etc. emitted by multiple measuring devices 200 may be received by multiple measuring devices 200. The measurement data 20 may be data based on the intensity, phase, frequency, etc. of the emitted electricity, etc. and the received electricity, etc.

[0019] The type of measurement data 20 is not particularly limited. For example, the measurement data 20 may be data generated by applying electricity to the body of the livestock 50 (sometimes referred to as electrical measurement data). For example, the measurement data 20 may be data generated by irradiating elastic waves (sometimes referred to as elastic wave measurement data). For example, the measurement data 20 may be data generated by applying a magnetic field to the body of the livestock 50 (sometimes referred to as magnetic field measurement data). For example, the measurement data 20 may be data generated by irradiating electromagnetic waves to the body of the livestock 50 (sometimes referred to as electromagnetic wave measurement data).

[0020] The electrical measurement data may be, for example, electrical conductivity or electrical resistance. The elastic wave measurement data may be, for example, the conductivity of ultrasound, sound waves, or low-frequency vibrations, and the frequency dependence of conductivity. The magnetic field measurement data may be, for example, the conductivity of an irradiated magnetic field. The magnetic field measurement data may be data measuring weak changes in magnetic flux density caused by blood flow or the like in the livestock 50. The electromagnetic wave measurement data may be data measuring phenomena such as scattering, refraction, reflection, diffraction, interference, and transmission of X-rays, ultraviolet rays, visible light, infrared rays, radio waves, or the like irradiated to the livestock 50.

[0021] The measurement data 20 may include data on the measurements of the livestock 50 (sometimes referred to as dimension data), data on the weight (sometimes referred to as weight data), data on the blood (sometimes referred to as blood data), data on the saliva (sometimes referred to as saliva data), data on the amount of movement (sometimes referred to as movement amount data), data on the body temperature (sometimes referred to as body temperature data), etc. The dimension data may be data representing the dimensions of any part of the livestock 50. For example, the dimension data may be the body length, waist circumference, waist width, etc. of the livestock 50. The blood data may be data representing the amount, ratio, etc. of any component in the blood of the livestock 50. For example, the blood data is data representing the amount, ratio, etc. of blood cells, blood glucose, cholesterol, protein, inorganic salts, oxygen, carbon dioxide, enzymes, etc. The movement amount data may be data representing acceleration generated by installing an accelerometer or the like as a measurement device 200 on the livestock 50.

[0022] The installation position of the measuring device 200 is not particularly limited. For example, the installation position of the measuring device 200 is on the surface of the body of the livestock 50. For example, the installation position of the measuring device 200 is the head, torso, legs, and buttocks of the livestock 50. For example, the installation position of the measuring device 200 is the ears, nose, back, abdomen, groin, hooves, and tail. The installation position of the measuring device 200 does not have to be a position that comes into contact with the livestock 50, and may be a position that does not come into contact with the livestock 50.

[0023] The installation position of the measuring device 200 may be selected depending on the nature of the measurement by the measuring device 200. For example, if the measuring device 200 applies electricity to the livestock 50 to generate electrical measurement data, a portion of the body surface of the livestock 50 where the hair is thin may be selected as the installation position of the measuring device 200. For example, if the measuring device 200 irradiates the livestock 50 with elastic waves to generate elastic wave measurement data, a relatively hard part of the livestock 50 may be selected as the installation position of the measuring device 200. For example, a part of the livestock 50 where the skin and subcutaneous tissue are thin and where the bones are close may be selected as the installation position of the measuring device 200. For example, the horns, skull, spine, or hooves may be selected as the installation position of the measuring device 200. A process may be performed to improve the accuracy of the measurement of the livestock 50 by the measuring device 200. For example, a process may be performed such as removing hair from the body surface of the livestock 50 or embedding a part of the measuring device 200 into the outermost surface of the skin of the livestock 50.

[0024] The measuring device 200 may be selected according to a target body part of the livestock 50. For example, the installation position of the measuring device 200 is selected so that the target body part of the livestock 50 is sandwiched between the measuring device 200 that emits electricity or the like and the measuring device 200 that receives electricity or the like. For example, the installation position of the measuring device 200 is selected according to a target internal body component of the livestock 50. The internal body component is, for example, muscle, fat, bone, and internal organs. For example, when focusing on internal organs, the installation position of the measuring device 200 is selected so that a part of the abdominal organs of the livestock 50 is sandwiched between the measuring device 200. For example, the installation position of the measuring device 200 is selected so that a part of the livestock 50 used to determine the grade of the livestock 50 is sandwiched between the measuring device 200. For example, if the livestock 50 is a cow and the destination for the cow's carcass is Japan, the installation position of the measuring device 200 may be selected so that it is positioned to sandwich the area between the sixth and seventh ribs, which is used in determining the grade of the carcass in Japan.

[0025] Carcass refers to the flesh of a livestock50 after the head, tail, extremities, etc. have been cut off and the skin and internal organs have been removed. The ratio of carcass to live weight is called carcass yield.

[0026] The measuring device 200 may be selected so that the data volume of the measurement data 20 is small. The measuring device 200 may be selected so that the calibration frequency of the measuring device 200 is reduced. As a specific example, the measuring device 200 is a measuring device 200 that does not use video data. Video data has a large data volume, so communication resources for data transmission are large and computational resources for data analysis are also large. By not using video data, communication resources and computational resources can be reduced. Video data requires calibration to identify individual livestock 50, and calibration is required every time the livestock 50 is moved to a different livestock barn. By not using video data, the labor required for calibration can be reduced.

[0027] There is no particular limitation on the number of measuring devices 200 included in the estimation system 10. The number of measuring devices 200 may be one or more.

[0028] The timing at which the estimation system 10 measures the livestock 50 using the measuring device 200, and the measurement posture of the livestock 50 when the estimation system 10 measures the livestock 50 using the measuring device 200, may be measurement timing and measurement posture that provide high repeatability of the measurement data 20. For example, the measurement timing is when the livestock 50 is hungry, just before the livestock 50 eats food. Because the livestock 50 moves little and has little content in its digestive organs, it is expected that the measurement data 20 will have high repeatability. For example, the measurement posture is an upright posture rather than a lying posture. For example, because electricity, etc. emitted from the measuring device 200 is less likely to escape into the ground, it is expected that the measurement data 20 will have high repeatability.

[0029] The estimation apparatus 100 may be connected to the network 90. ​​The estimation apparatus 100 may be wirelessly connected to the network 90. ​​The estimation apparatus 100 may be connected to the network 90 via a wireless base station. The estimation apparatus 100 may be connected to the network 90 via a Wi-Fi (registered trademark) access point. The estimation apparatus 100 may be wired to the network 90.

[0030] The measurement device 200 may be wirelessly connected to the network 90. ​​The measurement device 200 may be connected to the network 90 via a wireless base station. The measurement device 200 may be connected to the network 90 via a Wi-Fi (registered trademark) access point.

[0031] The network 90 may include a mobile communication network. The mobile communication network may be compliant with 5G (5th Generation). The mobile communication network may be compliant with LTE (Long Term Evolution). The mobile communication network may be compliant with 6G (6th Generation) or later communication systems. The network 90 may include the Internet. The network 90 may include the cloud. The network 90 may include a LAN (Local Area Network).

[0032] The measurement data acquiring unit 110 acquires the measurement data 20. The measurement data acquiring unit 110 may acquire the measurement data 20 via the network 90. ​​The measurement data acquiring unit 110 may acquire the measurement data 20 from the measurement device 200 by short-range wireless communication or the like, without using the network 90.

[0033] The learning model storage unit 120 stores a learning model generated by machine learning using learning data including the measurement data 20 and the internal components of the livestock 50. The learning model storage unit 120 may store a learning model that receives as input the measurement data 20 obtained by measuring the inside of the livestock 50 that is the estimation target, and outputs the internal components of the livestock 50.

[0034] The estimation unit 130 estimates the internal body components of the livestock 50 to be estimated using measurement data 20 obtained by measuring the inside of the livestock 50 to be estimated and a learning model. The internal body components of the livestock 50 are the types, weights, volumes, and / or proportions of the components that make up the body of the livestock 50. For example, the internal body components are muscle, fat, bone, and internal organs. For example, the internal body components are the weights, volumes, and / or proportions of muscle, fat, bone, and internal organs. For example, the internal body components are the body weight of the livestock 50. For example, the internal body components are the weights of each part of the livestock 50. For example, the internal body components are the weight of a carcass obtained by slaughtering the livestock 50. For example, the internal body components are the weights of each part of the carcass obtained by butchering the carcass. For example, the internal body components are the weights of the dressed meat obtained by butchering each part of the meat.

[0035] The estimation unit 130 may input the measurement data 20 obtained by measuring the inside of the livestock 50 to be estimated, acquired by the measurement data acquisition unit 110, into the learning model stored in the learning model storage unit 120, and output the internal components of the livestock 50. The estimation unit 130 may use the output internal components as estimation results. For example, the estimation unit 130 may derive the internal components to be used as estimation results based on the output internal components. For example, the estimation unit 130 may derive the fat ratio based on the output amounts of muscle, fat, bone, and viscera. For example, the estimation unit 130 may derive the carcass weight based on the output body weight, head weight, tail weight, weight of the tips of the four legs, and weight of the viscera.

[0036] The estimation unit 130 may estimate the internal components of the livestock 50 by statistical analysis of a data set including the measurement data 20 and the internal components of the livestock 50. In this case, the estimation device 100 does not need to include the learning model storage unit 120. The analysis method used by the estimation unit 130 is not particularly limited. For example, the analysis method may be multiple regression analysis, principal component analysis, principal component regression, etc. The estimation unit 130 may select the type of measurement data 20 to use depending on the type of internal component to be estimated, rather than using the entire data set. For example, when estimating muscle or fat as the internal component, electrical measurement data may be selected as the measurement data 20. For example, when estimating bone as the internal component, elastic wave measurement data may be selected as the measurement data 20.

[0037] The estimation unit 130 may estimate the fat content in the muscle of the livestock 50. For example, the measurement data 20 may include electrical measurement data measured around a muscular part of the livestock 50, and learning data including the fat content in the muscle of the livestock 50 as a body component may be used to estimate the fat content in the muscle of the livestock 50 by inputting the electrical measurement data measured around the same part of the livestock 50 to be estimated as the measurement data 20 into a learning model generated by machine learning. The estimation unit 130 may estimate the fat content in the muscle of each part of the livestock 50. For example, the part of the livestock 50 is a part for each butchering method according to the type of livestock 50. For example, if the livestock 50 is a cow, the part of the livestock 50 may be the forepart, loin, ham, rib, etc. For example, if the livestock 50 is a cow, the parts of the livestock 50 include neck, shoulder, shank, butt, shoulder, sirloin, rib roast, outer thigh, ribeye, inner thigh, ball, and fillet.

[0038] The estimation unit 130 may estimate at least one of the weight of the carcass of the livestock 50 and the grade of the meat. For example, the estimation unit 130 may estimate the weight of the carcass of the livestock 50 to be estimated by inputting the measurement data 20 of the livestock 50 to be estimated into a learning model generated by machine learning using the measurement data 20 of the livestock 50 and learning data including the weight, head weight, tail weight, tail weight, etc. of the livestock 50 to be estimated into a learning model generated by machine learning using the measurement data 20 of the livestock 50 and learning data including the weight, head weight, tail weight, tail weight, etc. of the livestock 50 to estimate the weight, head weight, tail weight, weight of the tips of the four legs, weight of the internal organs, etc. of the livestock 50 to be estimated, and derive the weight of the carcass by performing calculations on these.

[0039] For example, the meat grade of the livestock 50 can be estimated by inputting the measurement data 20 of the livestock 50 to a learning model generated by machine learning using measurement data 20 of the livestock 50 and learning data containing the meat grade of the livestock 50 as internal components. For example, the estimation unit 130 may derive the meat grade based on the output information indicating the grade of the livestock 50 to be estimated. This allows, for example, estimation of the meat grade of the livestock 50 at each growth stage and the weight of the carcass that can actually be sold, enabling fattening farmers to devise feeding methods based on this information. Ultimately, it becomes possible to more effectively produce meat with higher commercial value.

[0040] The estimation unit 130 may estimate the internal components of the livestock 50 in a form that corresponds to the distribution stage of the livestock 50. The form that corresponds to the distribution stage of the livestock 50 is, for example, a carcass, a cut of meat, or dressed meat.

[0041] The estimation unit 130 may output the estimated internal components. For example, the estimation unit 130 displays the estimated internal components on a display provided in the estimation device 100. The estimation unit 130 may transmit the estimated internal components to a device other than the estimation device 100. For example, the other device is a smartphone, tablet terminal, PC (Personal Computer), etc. owned by the fattening farmer. The estimation unit 130 may print out the estimated internal components from a printer or the like provided in the estimation device 100.

[0042] FIG. 2 schematically illustrates an example of training data. The training data includes measurement data 20 and corresponding internal components. The measurement data 20 included in the training data may include various types of measurement data 20. For example, the measurement data 20 includes at least one of electrical measurement data, elastic wave measurement data, magnetic field measurement data, and electromagnetic wave measurement data. The measurement data 20 may include a plurality of electrical measurement data, elastic wave measurement data, magnetic field measurement data, and electromagnetic wave measurement data. In the example illustrated in FIG. 2, the measurement data 20 includes electrical resistance values ​​as electrical measurement data and bone conduction vibration values ​​as elastic wave measurement data. The measurement data 20 may include a plurality of measurement data 20 measured at different installation positions of the measuring device 200. For example, the measurement data 20 may include electrical measurement data measured by placing the measuring device 200 on both ears of the livestock 50 and electrical measurement data measured by placing the measuring device 200 on one ear and the buttocks of the livestock 50.

[0043] The internal body components included in the learning data may include various internal body components of the livestock 50. For example, the internal body components include at least one of muscle, fat, bone, and visceral elements. The internal body components may include a plurality of muscle, fat, bone, and visceral elements. The example shown in FIG. 2 shows internal body components of a cow at the time of slaughter. In the example shown in FIG. 2, the internal body components include body weight immediately before slaughter, carcass weight, carcass ratio, muscle weight, muscle ratio, fat weight, fat ratio, bone weight, bone ratio, visceral weight, and visceral ratio. The internal body components may also include information indicating the grade of the livestock 50 as meat. In the example shown in FIG. 2, the internal body components include carcass grade, yield grade, yield standard value, longissimus thoracis muscle area, meat quality grade, fat marbling, meat color and luster, meat firmness and texture, and fat color and luster and quality.

[0044] FIG. 3 schematically shows an example of training data. In the example shown in FIG. 3, the measurement data 20 included in the training data is time-series measurement data 20. The measurement intervals of the time series of the measurement data 20 may be constant. The measurement intervals do not have to be constant. The measurement times of the measurement data 20 may be determined according to the growth stage of the livestock. In the example shown in FIG. 3, the measurement times of the measurement data 20 are the rearing period at the breeding farm, the start of fattening period at the fattening farm, the fattening period at the fattening farm, the slaughter decision period, and the slaughter period. Each measurement period may include multiple measurements.

[0045] The measurement data acquiring unit 110 may acquire time-series measurement data 20 of the estimation target livestock 50. The estimation unit 130 may estimate the internal body components of the estimation target livestock 50 in time series by inputting the time-series measurement data 20 acquired by the measurement data acquiring unit 110 into a learning model stored in the learning model storage unit 120. This allows the internal body components to be estimated based on multiple pieces of measurement data 20 measured at different times, which is expected to improve the accuracy of estimating the internal body components.

[0046] Fig. 4 schematically illustrates an example of the estimation system 10. In the example illustrated in Fig. 4, differences from Fig. 1 will be mainly described. In the example illustrated in Fig. 4, the estimation system 10 includes a determination unit 140.

[0047] The determination unit 140 may determine whether or not a change in the feeding method for the estimation target livestock 50 is necessary, based on the internal components of the estimation target livestock 50 estimated by the estimation unit 130. For example, the determination unit 140 may determine that a change in the feeding method is unnecessary if the internal components satisfy a predetermined criterion, and may determine that a change in the feeding method is necessary if the internal components do not satisfy the predetermined criterion. The predetermined criterion may be a combination of criteria set for multiple internal components. The determination unit 140 may determine that a change in the feeding method is unnecessary if the fat content in the muscle of the livestock 50 is equal to or greater than a predetermined threshold, and may determine that a change in the feeding method is necessary if the fat content is less than the predetermined threshold. The threshold may be set according to the growth stage of the livestock 50.

[0048] The determination unit 140 may output the determination result of whether or not a change in the feeding method is necessary. The determination unit 140 may output the reason for the determination together with the feeding method determination result. If the determination unit 140 determines that a change in the feeding method is necessary, it may also output information on a recommended feeding method. For example, the determination unit 140 outputs the determination result that a change in the feeding method is necessary, the reason for the determination that the fat content in the muscle of the livestock 50 is lower than a predetermined threshold, and a feeding method that increases the feed amount and / or changes the feed type to a more nutritious feed. This allows fattening farmers to rationally and efficiently change the feeding method at an appropriate time depending on the fattening status of the livestock 50. This ultimately enables more effective production of meat with higher commercial value. If the determination unit 140 determines that a change in the feeding method is not necessary, it may output a determination result indicating that a change in the feeding method is not necessary. If the determination unit 140 determines that a change in the feeding method is not necessary, it does not have to output the determination result.

[0049] The determination unit 140 may determine whether the estimation target livestock 50 should be slaughtered based on the internal components of the estimation target livestock 50 estimated by the estimation unit 130. For example, the determination unit 140 may determine that the livestock 50 may be slaughtered if the internal components meet a predetermined standard, and may determine that the livestock 50 should not be slaughtered and should continue to be fattened if the internal components do not meet the predetermined standard. The predetermined standard may be a combination of standards set for a plurality of internal components. For example, the determination unit 140 may determine that the livestock 50 may be slaughtered if the weight of the livestock 50 meets a predetermined standard and the grade of the meat of the livestock 50 is a predetermined grade or higher, and may determine that the livestock 50 should not be slaughtered and should continue to be fattened if the internal components do not meet the predetermined standard.

[0050] The determination unit 140 may output a determination result as to whether or not the livestock should be slaughtered. The determination unit 140 may also output the estimated internal components together with the determination result as to whether or not the livestock should be slaughtered. This allows, for example, a fattening farmer to know the carcass weight and meat grade of the livestock 50 before shipping the livestock 50. Ultimately, it allows the expected sales to be estimated in advance.

[0051] 4, the determination unit 140 may be located outside the estimation device 100. For example, a device other than the estimation device 100 may include the determination unit 140, and the device may communicate with the estimation device 100 via the network 90. ​​Alternatively, the estimation device 100 may include the determination unit 140.

[0052] When the determination unit 140 is located outside the estimation device 100, the determination unit 140 may transmit and output the determination result, etc. to the estimation device 100. The determination unit 140 may transmit and output the determination result, etc. to another device other than the estimation device 100. For example, the other device is a smartphone, tablet terminal, PC, etc. owned by the fattening farmer. When the estimation device 100 includes the determination unit 140, the determination unit 140 displays and outputs the determination result on a display included in the estimation device 100, for example. The determination unit 140 may transmit and output the determination result to another device other than the estimation device 100. The determination unit 140 may print out the determination result from a printer, etc. included in the estimation device 100.

[0053] In the example shown in FIG. 4 , the estimation system 10 includes a learning model generation unit 150. The learning model generation unit 150 generates a learning model. The learning model generation unit 150 may generate the learning model by machine learning using learning data including measurement data 20 obtained by measuring the inside of the livestock 50 and the internal components of the livestock 50. The learning model may be a learning model that receives the measurement data 20 of the livestock 50 to be estimated as input and outputs the internal components of the livestock 50. The measurement data 20 of the learning data may include electrical measurement data, elastic wave measurement data, magnetic field measurement data, and electromagnetic wave measurement data. The internal components of the learning data may include muscle, fat, bone, and internal organs. The internal components of the learning data may include the carcass weight of the livestock 50 and the meat grade of the livestock 50. According to the learning model, the internal components of the livestock 50 can be estimated from the measurement data 20 of the livestock 50 without actually slaughtering the livestock 50. This allows, for example, estimation of the grade of meat at each stage of growth of livestock 50, enabling fattening farmers to devise feeding methods, which in turn allows for more efficient production of meat with higher commercial value.

[0054] 4, the learning model generation unit 150 may be located outside the estimation device 100. For example, a device other than the estimation device 100 may include the learning model generation unit 150, and the device may communicate with the estimation device 100 via a network 90. ​​Alternatively, the estimation device 100 may include the learning model generation unit 150.

[0055] The learning data may include attribute data of the livestock 50. The attribute data may include, for example, at least one of the breed, pedigree, sex, breeding farm, date of birth, age at slaughter, and medical history of the livestock 50. For example, the attribute data includes the breed of the livestock 50. For example, the attribute data includes the pedigree of the livestock 50. For example, the attribute data includes the sex of the livestock 50. For example, the attribute data includes the breeding farm of the livestock 50. For example, the attribute data includes the date of birth of the livestock 50. For example, the attribute data includes the age at slaughter of the livestock 50. For example, the attribute data includes the medical history of the livestock 50. The learning model storage unit 120 may store a learning model generated by machine learning using learning data including the measurement data 20, internal components, and attribute data of the livestock 50.

[0056] 4, the estimation system 10 includes an attribute data acquisition unit 160. The attribute data acquisition unit 160 may acquire attribute data of the estimation target livestock 50. The estimation unit 130 may estimate the internal components of the estimation target livestock 50 by inputting the measurement data 20 acquired by the measurement data acquisition unit 110 and the attribute data acquired by the attribute data acquisition unit 160 into a learning model stored in the learning model storage unit 120.

[0057] 5 is an explanatory diagram illustrating an example of the processing flow by the estimation system 10. Here, the process will be described in which the estimation system 10 estimates the internal components of the estimation target livestock 50 using a learning model, and determines whether or not the feeding method needs to be changed based on the estimation results. The description will be given assuming that the start state is one in which a learning model has already been prepared and the learning model storage unit 120 has finished storing the learning model.

[0058] In step (sometimes abbreviated as S) 102, the measurement data acquisition unit 110 acquires the measurement data 20 of the livestock 50 that is the subject of estimation. The measurement data acquisition unit 110 receives the measurement data 20 from one or more measurement devices 200.

[0059] In S104, the estimation unit 130 estimates the internal components of the livestock 50 to be estimated using the measurement data 20 acquired by the measurement data acquisition unit 110 in S102 and the learning model stored in the learning model storage unit 120.

[0060] In S106, the determination unit 140 determines whether or not a change in the feeding method for the estimation target livestock 50 is necessary, based on the internal components estimated by the estimation unit 130 in S104. If it is determined that a change in the feeding method is necessary, the process proceeds to S108, and if it is determined that a change in the feeding method is not necessary, the process proceeds to S110.

[0061] In S108, the estimation unit 130 outputs a determination result indicating that a change in the feeding method is necessary.

[0062] In S110, the estimation unit 130 does not output the determination result. The estimation unit 130 may output the determination result indicating that there is no need to change the feeding method.

[0063] 6 schematically shows an example of the hardware configuration of a computer 1200 that functions as the estimation system 10 or the estimation device 100. A program installed on the computer 1200 can cause the computer 1200 to function as one or more "parts" of the device according to the present embodiment, or can cause the computer 1200 to perform operations associated with the device according to the present embodiment or one or more "parts" thereof, and / or can cause the computer 1200 to perform a process according to the present embodiment or steps of the process. Such a program can be executed by the CPU 1212 to cause the computer 1200 to perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0064] The computer 1200 according to this embodiment includes a CPU 1212, a RAM 1214, and a graphics controller 1216, which are interconnected by a host controller 1210. The computer 1200 also includes input / output units such as a communications interface 1222, a storage device 1224, a DVD drive, and an IC card drive, which are connected to the host controller 1210 via an input / output controller 1220. The DVD drive may be a DVD-ROM drive, a DVD-RAM drive, or the like. The storage device 1224 may be a hard disk drive, a solid-state drive, or the like. The computer 1200 also includes a ROM 1230 and legacy input / output units such as a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0065] The CPU 1212 operates according to programs stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphics controller 1216 acquires image data generated by the CPU 1212 into a frame buffer or the like provided in the RAM 1214 or into the graphics controller itself, and causes the image data to be displayed on the display device 1218.

[0066] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD drive reads programs or data from a DVD-ROM or the like and provides them to the storage device 1224. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0067] The ROM 1230 stores therein a boot program or the like that is executed by the computer 1200 upon activation, and / or programs that depend on the hardware of the computer 1200. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via a USB port, a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0068] The programs are provided by a computer-readable storage medium such as a DVD-ROM or an IC card. The programs are read from the computer-readable storage medium, installed in the storage device 1224, RAM 1214, or ROM 1230, which are also examples of computer-readable storage media, and executed by the CPU 1212. Information processing described in these programs is read by the computer 1200, and causes cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be configured by implementing operations or processing of information in accordance with the use of the computer 1200.

[0069] For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 may execute a communication program loaded into the RAM 1214 and instruct the communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 1212, the communication interface 1222 reads transmission data stored in a transmission buffer area provided in the RAM 1214, the storage device 1224, a DVD-ROM, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer area or the like provided on the recording medium.

[0070] Furthermore, the CPU 1212 may cause all or a necessary portion of a file or database stored in an external recording medium such as the storage device 1224, a DVD drive (DVD-ROM), an IC card, etc. to be read into the RAM 1214, and may perform various types of processing on the data on the RAM 1214. The CPU 1212 may then write back the processed data to the external recording medium.

[0071] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and may undergo information processing. The CPU 1212 may perform various types of processing on data read from the RAM 1214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequences of the programs, and write the results back to the RAM 1214. The CPU 1212 may also search for information in a file, database, etc. on the recording medium. For example, if multiple entries, each having an attribute value of a first attribute associated with an attribute value of a second attribute, are stored on the recording medium, the CPU 1212 may search for an entry whose attribute value of the first attribute matches a specified condition from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0072] The above-described programs or software modules may be stored in a computer-readable storage medium on or near the computer 1200. A recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can also be used as a computer-readable storage medium, thereby providing the programs to the computer 1200 via the network.

[0073] The blocks in the flowcharts and block diagrams in the present embodiments may represent stages of a process in which an operation is performed or "parts" of an apparatus responsible for performing the operation. Particular stages and "parts" may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable storage medium, and / or a processor provided with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuitry may include digital and / or analog hardware circuits, including integrated circuits (ICs) and / or discrete circuits. The programmable circuitry may include reconfigurable hardware circuits, such as field programmable gate arrays (FPGAs) and programmable logic arrays (PLAs), including AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, and memory elements.

[0074] A computer-readable storage medium may include any tangible device capable of storing instructions that are executed by an appropriate device, such that a computer-readable storage medium having instructions stored thereon comprises an article of manufacture, including instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray disc, memory stick, integrated circuit card, etc.

[0075] The computer readable instructions may include either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0076] Computer-readable instructions may be provided to a general-purpose computer, a special-purpose computer, or another programmable data processing device, or a programmable circuit, either locally or via a local area network (LAN) or a wide area network (WAN) such as the Internet, so that the processor of the programmable data processing device, such as a computer, or the programmable circuit executes the computer-readable instructions to generate means for performing the operations specified in the flowcharts or block diagrams. Here, the computer may be a personal computer (PC), a tablet computer, a smartphone, a workstation, a server computer, a general-purpose computer, a special-purpose computer, or the like, or may be a computer system in which multiple computers are connected. Such a computer system in which multiple computers are connected is also called a distributed computing system, and is a broad definition of computers. In a distributed computing system, multiple computers collectively execute a program by each executing a portion of the program and passing data between the computers as needed during program execution.

[0077] Examples of processors include computer processors, central processing units, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc. A computer may have one or more processors. In a multiprocessor system with multiple processors, each processor executes a portion of a program and passes data between processors as needed during program execution, allowing the multiple processors to collectively execute the program. For example, in multitasking, each of the multiple processors may execute a portion of each task in small chunks by switching tasks at time slice intervals. In this case, which portion of a program each processor executes changes dynamically. Which portion of a program each of the multiple processors executes may also be statically determined by multiprocessor-aware programming.

[0078] Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

[0079] It should be noted that the execution order of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and that the processes can be performed in any order unless the output of a previous process is used in a later process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that the processes must be performed in this order. [Explanation of symbols]

[0080] 10 estimation system, 20 measurement data, 50 livestock, 90 network, 100 estimation device, 110 measurement data acquisition unit, 120 learning model memory unit, 130 estimation unit, 140 judgment unit, 150 learning model generation unit, 160 attribute data acquisition unit, 200 measurement device, 1200 computer, 1210 host controller, 1212 CPU, 1214 RAM, 1216 graphics controller, 1218 display device, 1220 input / output controller, 1222 communication interface, 1224 storage device, 1230 ROM, 1240 input / output chip

Claims

1. a learning model storage unit that stores a learning model generated by machine learning using learning data including measurement data obtained by measuring the inside of the livestock and internal components of the livestock; a measurement data acquisition unit that acquires measurement data obtained by measuring the inside of the livestock that is the subject of estimation; an estimation unit that estimates the internal components of the estimation target livestock using the measurement data acquired by the measurement data acquisition unit and the learning model; Equipped with An estimation system, wherein the measurement data includes at least one of electrical measurement data generated by applying electricity to the body of the livestock and electromagnetic wave measurement data generated by irradiating the body of the livestock with electromagnetic waves.

2. The estimation system of claim 1, wherein the measurement data includes a plurality of the electrical measurement data, magnetic field measurement data generated by applying a magnetic field inside the livestock's body, the electromagnetic wave measurement data, and elastic wave measurement data generated by irradiating elastic waves inside the livestock's body.

3. The estimation system according to claim 1 , wherein the internal body components include at least one of muscle, fat, bone, and visceral elements.

4. The estimation system according to claim 1 , wherein the estimation unit estimates a fat component in a muscle of the livestock.

5. The estimation system according to claim 1 , wherein the estimation unit estimates at least one of a weight of a carcass of the livestock subject to estimation and a grade of meat of the livestock subject to estimation.

6. The estimation system according to any one of claims 1 to 5, further comprising a learning model generation unit that generates a learning model by machine learning using learning data including the measurement data obtained by measuring the inside of the livestock and the internal components of the livestock.

7. a learning model storage unit that stores a learning model generated by machine learning using learning data including measurement data obtained by measuring the inside of the livestock and internal components of the livestock; a measurement data acquisition unit that acquires measurement data obtained by measuring the inside of the livestock that is the subject of estimation; an estimation unit that estimates the internal components of the livestock to be estimated using the measurement data acquired by the measurement data acquisition unit and the learning model; a determination unit that determines whether or not the livestock to be estimated should be slaughtered based on the internal components of the livestock to be estimated; An estimation system comprising:

8. a learning model storage unit that stores a learning model generated by machine learning using learning data including measurement data obtained by measuring the inside of the livestock and internal components of the livestock; a measurement data acquisition unit that acquires measurement data obtained by measuring the inside of the livestock that is the subject of estimation; an estimation unit that estimates the internal components of the estimation target livestock using the measurement data acquired by the measurement data acquisition unit and the learning model; Equipped with the measurement data acquisition unit acquires the measurement data in time series, the estimation unit estimates the internal components of the estimation target livestock in a time series by inputting the time series measurement data into the learning model; The estimation system further comprises a determination unit that determines whether or not a change in a feeding method for the estimation target livestock is necessary based on the internal body components of the time series estimated by the estimation unit.

9. a learning model storage unit that stores a learning model generated by machine learning using learning data including measurement data obtained by measuring the inside of the livestock and internal components of the livestock; a measurement data acquisition unit that acquires measurement data obtained by measuring the inside of the livestock that is the subject of estimation; an attribute data acquisition unit that acquires attribute data of the livestock to be estimated; an estimation unit that estimates the internal components of the estimation target livestock using the measurement data acquired by the measurement data acquisition unit and the learning model; Equipped with the learning model storage unit stores the learning model generated by machine learning using learning data including the measurement data and the internal components of the livestock and attribute data of the livestock; The estimation unit estimates the internal components of the livestock to be estimated by inputting the measurement data acquired by the measurement data acquisition unit and the attribute data acquired by the attribute data acquisition unit into the learning model.

10. A learning data storage unit that stores learning data including measurement data obtained by measuring the inside of the livestock body and internal components of the livestock body; a learning model generation unit that generates a learning model by machine learning using the learning data stored in the learning data storage unit, using measurement data that measures the inside of the livestock as an input and outputting the internal components of the livestock; Equipped with A generation system in which the measurement data includes at least one of electrical measurement data generated by applying electricity to the body of the livestock and electromagnetic wave measurement data generated by irradiating electromagnetic waves into the body of the livestock.

11. 1. A computer-implemented estimation method comprising: a measurement data acquisition stage for acquiring measurement data obtained by measuring the inside of the livestock subject to estimation; an estimation step of estimating the internal components of the estimation target livestock using a learning model generated by machine learning using learning data including measurement data measuring the inside of the livestock and the internal components of the livestock, and the measurement data acquired in the measurement data acquisition step; Equipped with An estimation method in which the measurement data includes at least one of electrical measurement data generated by applying electricity to the body of the livestock and electromagnetic wave measurement data generated by irradiating the body of the livestock with electromagnetic waves.

12. 1. A computer-implemented estimation method comprising: a measurement data acquisition stage for acquiring measurement data obtained by measuring the inside of the livestock subject to estimation; an estimation step of estimating the internal components of the estimation target livestock using a learning model generated by machine learning using learning data including measurement data obtained by measuring the inside of the livestock and the internal components of the livestock, and the measurement data acquired in the measurement data acquisition step; a determination step of determining whether or not the livestock to be estimated should be slaughtered based on the internal components of the livestock to be estimated; The estimation method comprises:

13. 1. A computer-implemented estimation method comprising: a measurement data acquisition stage for acquiring measurement data obtained by measuring the inside of the livestock subject to estimation; an estimation step of estimating the internal components of the estimation target livestock using a learning model generated by machine learning using learning data including measurement data measuring the inside of the livestock and the internal components of the livestock, and the measurement data acquired in the measurement data acquisition step; Equipped with the measurement data acquisition step includes a step of acquiring the measurement data in time series, The estimation step includes a step of estimating the internal components of the estimation target livestock over time by inputting the time-series measurement data into the learning model, The estimation method further comprises a determination step of determining whether or not a change in the feeding method for the livestock being estimated is necessary based on the internal components of the time series estimated in the estimation step.

14. 1. A computer-implemented estimation method comprising: a measurement data acquisition stage for acquiring measurement data obtained by measuring the inside of the livestock subject to estimation; an attribute data acquisition step of acquiring attribute data of the livestock to be estimated; an estimation step of estimating the internal components of the estimation target livestock using a learning model generated by machine learning using learning data including measurement data measuring the inside of the livestock and the internal components of the livestock, and the measurement data acquired in the measurement data acquisition step; Equipped with The estimation step includes a step of estimating the internal components of the livestock to be estimated by inputting the measurement data acquired in the measurement data acquisition step and the attribute data acquired in the attribute data acquisition step into the learning model generated by machine learning using learning data including the measurement data and the internal components of the livestock and attribute data of the livestock.

15. A program for causing a computer to execute the estimation method according to any one of claims 11 to 14.

16. A learning model generation step is provided in which a learning model is generated by machine learning using the learning data stored in a learning data storage unit that stores measurement data measuring the inside of the livestock's body and learning data including internal components of the livestock, and the learning model takes the measurement data measuring the inside of the livestock as input and outputs the internal components of the livestock, A generation method in which the measurement data includes at least one of electrical measurement data generated by applying electricity to the body of the livestock and electromagnetic wave measurement data generated by irradiating electromagnetic waves into the body of the livestock.

17. A program for causing a computer to execute the generation method described in claim 16.

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