Estimation system, estimation method, and program

Abstract

Provided is an estimation system comprising: a learning model storage unit that stores a learning model generated by machine learning using training data including measurement data obtained by taking internal measurements of the bodies of farm animals and internal elements of the farm animals; a measurement data acquisition unit that acquires measurement data obtained by taking internal measurements of the body of a farm animal that is the target of estimation; and an estimation unit that estimates internal elements of the farm animal that is the target of estimation using the learning model and the measurement data acquired by the measurement data acquisition unit. The measurement data may include at least one among electrical measurement data generated by internally applying electricity to the body of the farm animal, elastic wave measurement data generated by internally irradiating the body of the farm animal with elastic waves, magnetic field measurement data generated by internally applying a magnetic field to the body of the farm animal, and electromagnetic wave measurement data generated by internally irradiating the body of the farm animal with electromagnetic waves.

Description

Estimation system, estimation method, and program

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

[0002] Patent Document 1 describes an "estimation system for estimating the health state 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 instrument 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 documents] [Patent documents] [Patent document 1] JP 2021-136868 A [Patent document 2] JP 2011-103793 A [Patent document 3] JP 2002-253523 A [Patent document 4] JP 2021-016376 A General disclosure

[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 obtained by measuring the inside of the livestock and the internal components of the livestock. The estimation system may include a measurement data acquisition unit that acquires measurement data obtained by measuring the inside of the livestock as an estimation target. The estimation system may include an estimation unit that estimates the internal components of the livestock as an estimation target using the measurement data acquired by the measurement data acquisition unit and the learning model. In the estimation system, the estimation unit may estimate the internal components of the livestock by statistical analysis of a dataset including the measurement data and the internal components of the livestock.

[0004] In any of the estimation systems, 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 a plurality 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 internal organ elements. In any of the estimation systems, the measurement data may be data measured using measurement devices having measurement signal transmission and reception functions distributed and arranged at different parts of the livestock. In any of the estimation systems, the measurement data may be data measured using measurement devices having measurement signal transmission and reception functions arranged so as to sandwich a part of the livestock used to determine the livestock's grade. In any of the estimation systems, the measurement data may not include video data. In any of the estimation systems, the measurement data may include at least one of dimension data, weight data, blood data, saliva data, locomotion data, and body temperature data of the livestock. In any of the estimation systems, the internal components of the livestock may include the type, weight, volume, and / or ratio of components that make up the body of the livestock. In any of the estimation systems, the internal components of the livestock may include the weight of each portion of meat obtained by butchering a carcass. In any of the estimation systems, the internal components of the livestock may include the weight of each portion of meat obtained by butchering a carcass.

[0005] In any of the estimation systems, the estimation unit may estimate fat components in the muscle of the livestock. In the estimation system, the estimation unit may estimate at least one of the weight of a carcass of the livestock to be estimated and the grade of meat of the livestock to be estimated. In any of the estimation systems, the estimation unit may estimate the internal constituent elements in a form corresponding to the distribution stage of the livestock. In any of the estimation systems, the estimation unit may estimate the internal constituent elements in a form corresponding to the distribution stage of the livestock, including at least one of a carcass, a primal meat, and dressed meat.

[0006] In any of the estimation systems, the measurement data acquisition unit may acquire the measurement data in time series, and the estimation unit may estimate the internal body components of the estimation target livestock in time series by inputting the time series measurement data into the learning model. Any of the estimation systems may further include a determination unit that determines whether a change in a feeding method for the estimation target livestock is necessary based on the time series of internal body components estimated by the estimation unit. In any of the estimation systems, the determination unit may output a determination result as to whether a change in the feeding method is necessary. In any of the estimation systems, the determination unit may output a reason for the determination together with the determination result of the feeding method. In any of the estimation systems, if the determination unit determines that a change in the feeding method is necessary, the determination unit may also output information on a recommended feeding method. In any of the estimation systems, if the determination unit determines that a change in the feeding method is not necessary, the determination unit may output the determination result indicating that a change in the feeding method is not necessary. In any of the estimation systems, the determination unit may determine whether the livestock to be estimated should be slaughtered based on the internal components of the livestock to be estimated by the estimation unit. In any of the estimation systems, the determination unit may output the determination result of whether the livestock should be slaughtered. In any of the estimation systems, the determination unit may output the estimated internal components together with the determination result of whether the livestock should be slaughtered.

[0007] In any of the 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. Any of the estimation systems may further include an attribute data acquisition unit that acquires attribute data of the livestock to be estimated. In any of the estimation systems, 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.

[0012] FIG. 1 schematically illustrates an example of an estimation system 10. FIG. 2 schematically illustrates an example of training data. FIG. 3 schematically illustrates an example of training data. FIG. 4 schematically illustrates an example of an estimation system 10. FIG. 5 is an explanatory diagram for explaining an example of a processing flow by the estimation system 10. FIG. 6 schematically illustrates an example of a hardware configuration of a computer 1200 that functions as the estimation system 10 or the estimation device 100.

[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 as claimed. 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, a 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 sensors, to estimate the health status (abnormalities such as infectious diseases) of individual livestock using AI (artificial intelligence).

[0015] Although it has been possible to estimate the health condition of livestock based on 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, using a learning model generated by machine learning using training data including measurement data obtained by measuring the inside of the livestock and the internal components of the livestock.

[0016] 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 the 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 is, for example, electrical conductivity, electrical resistance, etc. The elastic wave measurement data is, for example, the conductivity of ultrasound, sound waves, and low-frequency vibrations, and the frequency dependence of conductivity, etc. The magnetic field measurement data is, for example, the conductivity of an irradiated magnetic field, etc. The magnetic field measurement data may be data measuring weak changes in magnetic flux density caused by blood flow, etc. 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, etc. 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 measurement of weight (sometimes referred to as weight data), data on the measurement of blood (sometimes referred to as blood data), data on the measurement of saliva (sometimes referred to as saliva data), data on the measurement of locomotion (sometimes referred to as locomotion data), data on the measurement of 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 locomotion 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 to improve the accuracy of measurement of the livestock 50 by the measuring device 200 may be performed. For example, a process such as hair removal 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 may be performed.

[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 portion of the abdominal organs of the livestock 50 is sandwiched between the measuring device 200 and the measuring device 200. For example, the installation position of the measuring device 200 is selected so that a portion of the livestock 50 used to determine the grade of the livestock 50 is sandwiched between the measuring device 200 and 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] The carcass refers to the flesh of a livestock 50 after the head, tail, extremities, etc. have been cut off and the skin and internal organs have been removed. The ratio of the carcass to the live weight is called the 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 each time the livestock 50 is moved to another 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, or just before the livestock 50 is about to eat. Because the livestock 50 moves little and has little content in its digestive organs, it can be 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 can be expected that the measurement data 20 will have high repeatability.

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

[0030] The measuring device 200 may be wirelessly connected to the network 90. ​​The measuring device 200 may be connected to the network 90 via a wireless base station. The measuring 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 a 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. For example, the internal body components are the weights of the 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, without 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 component 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 component in the muscle of the livestock 50 as an internal component may be used to input, into a learning model generated by machine learning, electrical measurement data measured around the same part of the livestock 50 to be estimated as the measurement data 20, thereby estimating the fat component in the muscle of the livestock 50 to be estimated. The estimation unit 130 may estimate the fat component 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 loin, shank, shoulder, shoulder belly, sirloin, rib loin, 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 carcass weight of the estimation target livestock 50 by inputting the measurement data 20 of the estimation target livestock 50 into a learning model generated by machine learning using the measurement data 20 of the livestock 50 and learning data including the carcass weight of the livestock 50 as an internal body component. For example, the estimation unit 130 may input the measurement data 20 of the estimation target livestock 50 into a learning model generated by machine learning using the measurement data 20 of the livestock 50 and learning data including the body weight, head weight, tail weight, tail weight, etc. of the livestock 50, thereby estimating the body weight, head weight, tail weight, weight of the tips of the four legs, weight of the internal organs, etc. of the estimation target livestock 50, and deriving the carcass weight by performing calculation processing 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 the 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, allowing fattening farmers to devise feeding methods based on this information. Ultimately, this enables more effective production of 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 also 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 component 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 component 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 a determination result as to whether a change in the feeding method is necessary. The determination unit 140 may also output the reason for the determination along 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 may output 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 or not 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 predetermined standards, 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 standards. The predetermined standards 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 predetermined standards and the grade of the meat of the livestock 50 is equal to or higher than a predetermined grade, 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 standards.

[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 is possible to estimate expected sales 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. ​​The estimation device 100 may also 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 meat grade at each stage of growth of the livestock 50, enabling fattening farmers to devise feeding methods, which in turn allows for more effective 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 the network 90. ​​The estimation device 100 may also include the learning model generation unit 150.

[0055] The training 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 training model generated by machine learning using training 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, a 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 a change in the feeding method is necessary 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 memory 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 a legacy input / output unit 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 1216 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 brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or a 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 computer 1200 and an external device, CPU 1212 may execute a communication program loaded into RAM 1214 and instruct communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of CPU 1212, communication interface 1222 reads transmission data stored in a transmission buffer area provided in RAM 1214, storage device 1224, a DVD-ROM, or a recording medium such as an IC card, and transmits the read transmission data to a network, or writes received 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 a device 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, and may include 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, magnetic, optical, electromagnetic, and semiconductor storage media. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), electrically erasable programmable read-only memories (EEPROMs), static random access memories (SRAMs), compact disc read-only memories (CD-ROMs), digital versatile discs (DVDs), Blu-ray discs, memory sticks, integrated circuit cards, and the like.

[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] The computer-readable instructions may be provided to a general-purpose computer, a special-purpose computer, or another programmable data processing device processor or programmable circuit, either locally or via a local area network (LAN), a wide area network (WAN) such as the Internet, so that the processor or programmable circuit of the programmable data processing device, such as a computer, 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 computer. 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 processor or multiple 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 each time slice. 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 subsequent 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.

[0080] 10 Estimation system, 20 Measurement data, 50 Livestock, 90 Network, 100 Estimation device, 110 Measurement data acquisition unit, 120 Learning model storage unit, 130 Estimation unit, 140 Determination 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. An estimation system comprising: a learning model memory unit that stores a learning model generated by machine learning using learning data including measurement data obtained by measuring the inside of the body of a livestock and the internal components of the livestock; a measurement data acquisition unit that acquires measurement data obtained by measuring the inside of the body of the livestock to be estimated; and 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.

2. The estimation system of claim 1, wherein the measurement data includes at least one 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.

3. The estimation system of claim 2, wherein the measurement data includes a plurality of the electrical measurement data, the elastic wave measurement data, the magnetic field measurement data, and the electromagnetic wave measurement data.

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

5. An estimation system according to any one of claims 1 to 4, wherein the estimation unit estimates the fat content in the muscle of the livestock.

6. An estimation system according to any one of claims 1 to 5, wherein the estimation unit estimates at least one of the weight of the carcass of the livestock being estimated and the grade of the meat of the livestock being estimated.

7. An estimation system as described in any one of claims 1 to 6, wherein the measurement data acquisition unit acquires the measurement data in a time series, the estimation unit estimates the internal body components of the estimation target livestock in a time series by inputting the time series measurement data into the learning model, and the estimation system further comprises a judgment unit that determines whether or not a change in the feeding method for the estimation target livestock is necessary based on the internal body components in the time series estimated by the estimation unit.

8. An estimation system as described in any one of claims 1 to 7, wherein the learning model memory 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, and the estimation system further comprises an attribute data acquisition unit that acquires attribute data of the livestock to be estimated, and 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.

9. An estimation system described in any one of claims 1 to 8, further comprising a learning model generation unit that generates a learning model by machine learning using learning data including the measurement data measured inside the livestock's body and the internal components of the livestock.

10. An estimation method executed by a computer, comprising: a measurement data acquisition step of acquiring measurement data obtained by measuring the inside of the body of a livestock to be estimated; and an estimation step of estimating the internal components of the livestock to be estimated using a learning model generated by machine learning using learning data including the measurement data obtained by measuring the inside of the body of the livestock and the internal components of the livestock, and the measurement data acquired in the measurement data acquisition step.

11. A program for causing a computer to execute the estimation method according to claim 10.

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