Method and apparatus for measuring meat taste and tenderness by using artificial intelligence model

Abstract

According to an embodiment of the present disclosure, provided is a method for measuring meat taste and tenderness by using an artificial intelligence model. The present method comprises the steps of: generating a learning data set including one or more meat images for learning and a sensory evaluation table matching the one or more learning meat images; and generating, on the basis of the learning data set, an artificial intelligence model trained to measure the taste and tenderness of meat included in input meat images. The present invention can provide consistent and highly accurate measurements for meat taste and tenderness by using artificial intelligence that has been trained on meat images classified for each taste and tenderness. In addition, because the present invention is performed by using artificial intelligence, costs can be reduced, and evaluation speed can be improved. Also, criteria for evaluating meat grades can be clarified, thereby providing the basis upon which factors played a decisive role in evaluating the grades.

Description

Method and device for measuring meat flavor and tenderness using an artificial intelligence model

[0001] The present invention relates to a method and device for measuring the taste and tenderness of meat using an artificial intelligence model, and more particularly, to a technology for analyzing a meat image using an artificial intelligence model and measuring the taste and tenderness of meat included in the image.

[0002] Meat consumption continues to rise globally. As of 2021, chicken ranked first in global meat consumption, followed by pork and beef.

[0003] In 2021, pork consumption reached 32.3 kg, while domestic beef consumption reached 12.4 kg per capita. Today's consumers have access to information about meat through various media outlets, and they use this information to purchase higher-quality meat. This is because there are significant differences in taste and price depending on the grade of meat.

[0004] When consumers purchase meat, taste is one of the factors they consider, along with price, origin, and grade. Factors that determine meat taste include marbling, color, juiciness, and texture.

[0005] Traditionally, experts measured the taste and tenderness of meat, and consumers purchased meat based on the taste and tenderness measured by the experts.

[0006] However, experts can differ in their assessments of flavor and tenderness. This is because the key criteria for measuring flavor and tenderness—marbling, color, texture, juiciness, and palatability—vary among experts. This has led to a lack of consistency in meat flavor and tenderness ratings.

[0007] Accordingly, there is an increasing need for a method and device for measuring the taste and age of meat using artificial intelligence that can quickly and consistently measure the taste and age by analyzing a single photo of meat based on data held by artificial intelligence that has learned a large number of photos of meat by taste and age.

[0008] The present invention is intended to solve the problems of the prior art described above, and relates to a method and device for judging meat grade using artificial intelligence, which analyzes meat included in an image using pre-trained artificial intelligence and measures taste and tenderness.

[0009] However, the technical task that this embodiment seeks to achieve is not limited to the technical task described above, and other technical tasks may exist.

[0010] As a technical means for achieving the above-described technical task, an embodiment according to the first aspect of the present disclosure provides a method for measuring the taste and tenderness of meat using an artificial intelligence model. The method includes the steps of generating a learning data set including at least one learning meat image and a sensory evaluation table matching the learning meat image, and the step of generating an artificial intelligence model trained to measure the taste and tenderness of meat included in an input meat image based on the learning data set.

[0011] In addition, an embodiment according to a second aspect of the present disclosure provides a device for measuring meat taste and tenderness. The device includes a communication module, at least one processor, and a memory electrically connected to the processor and storing at least one code executed by the processor, wherein the memory stores a code that, when executed through the processor, causes the processor to generate a learning data set including at least one learning meat image and a sensory evaluation table matching the learning meat image, and generate an artificial intelligence model trained to measure the taste and tenderness of meat included in an input meat image based on the learning data set.

[0012] The present invention provides consistent and highly accurate measurements of meat flavor and age using artificial intelligence trained on images of meat classified by flavor and age. Furthermore, the AI-powered approach reduces costs and improves measurement speed.

[0013] Additionally, by clearly defining the measurement criteria, it can provide visual data to show which items played a decisive role in evaluating the taste and tenderness of meat.

[0014] FIG. 1 is a drawing showing a meat taste and tenderness measuring device and a terminal connected to the device in communication with the device according to one embodiment of the present invention.

[0015] Figure 2 is a detailed drawing of the meat taste and tenderness measuring device in Figure 1.

[0016] FIG. 3a and FIG. 3b are diagrams illustrating a process for adjusting the size of a meat image and obtaining a sensory evaluation according to one embodiment of the present invention.

[0017] Figure 4 is a drawing showing the results of a sensory evaluation according to one embodiment of the present invention.

[0018] FIG. 5a and FIG. 5b are diagrams showing a process for obtaining a sensory evaluation according to one embodiment of the present invention.

[0019] FIG. 6a and FIG. 6b are drawings showing an interface provided to a terminal according to one embodiment of the present invention.

[0020] Figure 7 is a flowchart illustrating the sequence of a method for measuring meat taste and tenderness according to another embodiment of the present invention.

[0021] Hereinafter, the present disclosure will be described in detail with reference to the attached drawings. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. In addition, the attached drawings are only intended to facilitate understanding of the embodiments disclosed in the present specification, and the technical concepts disclosed in the present specification are not limited by the attached drawings. All terms, including technical and scientific terms, used herein should be interpreted as having meanings generally understood by a person of ordinary skill in the technical field to which the present disclosure pertains. Terms defined in the dictionary should be interpreted as having additional meanings consistent with the relevant technical literature and the present disclosure, and shall not be interpreted in an extremely ideal or restrictive sense unless otherwise defined.

[0022] In order to clearly explain the present invention in the drawings, parts irrelevant to the description have been omitted, and the size, shape, and appearance of each component shown in the drawings may be modified in various ways. Identical / similar parts throughout the specification are given identical / similar drawing reference numerals.

[0023] Throughout the specification, when a part is said to be "connected (connected, in contact with, or coupled)" to another part, this includes not only cases where it is "directly connected (connected, in contact with, or coupled)" but also cases where it is "indirectly connected (connected, in contact with, or coupled)" with another member in between. Furthermore, when a part is said to "include (have or provide)" a certain component, this does not mean that it excludes other components, but rather that it may "include (have or provide)" other components, unless otherwise specifically stated.

[0024] In this specification, the term 'unit' includes a unit realized by hardware, a unit realized by software, and a unit realized using both. In addition, one unit may be realized by using two or more pieces of hardware, and two or more units may be realized by one piece of hardware. Meanwhile, the '~ unit' is not limited to software or hardware, and the '~ unit' may be configured to be in an addressable storage medium or may be configured to reproduce one or more processors. Therefore, as an example, the '~ unit' includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within the components and '~sub-units' may be combined into a smaller number of components and '~sub-units' or further separated into additional components and '~sub-units'. Furthermore, the components and '~sub-units' may be implemented to activate one or more CPUs within the device or secure multimedia card.

[0025] The suffixes "module" and "part" used in the following description for components are assigned or used interchangeably solely for the convenience of writing the specification, and do not in themselves have distinct meanings or roles. Furthermore, in describing the embodiments disclosed herein, detailed descriptions of related known technologies have been omitted if they are deemed to obscure the gist of the embodiments disclosed herein.

[0026] As used herein, ordinal terms such as "first," "second," etc., are used solely to distinguish one component from another and do not limit the order or relationship of the components. For example, the first component of the present disclosure may be referred to as the "second component," and similarly, the second component may also be referred to as the "first component." As used herein, singular forms should be construed to include plural forms, unless explicitly stated otherwise.

[0027] The "user terminal" mentioned below may be implemented as a computer or portable terminal that can access a server or other terminal via a network. Here, the computer may include, for example, a notebook, desktop, laptop, VR HMD (e.g., HTC VIVE, Oculus Rift, GearVR, DayDream, PSVR, etc.) equipped with a web browser. Here, the VR HMD includes all of the stand-alone models implemented independently for PC (e.g., HTC VIVE, Oculus Rift, FOVE, Deepon, etc.), mobile (e.g., GearVR, DayDream, Storm Magic, Google Cardboard, etc.), and console (PSVR) (e.g., Deepon, PICO, etc.). A portable terminal is, for example, a wireless communication device that ensures portability and mobility, and may include not only a smart phone, a tablet PC, and a wearable device, but also various devices equipped with communication modules such as Bluetooth (BLE, Bluetooth Low Energy), NFC, RFID, ultrasonic, infrared, WiFi, and LiFi. In addition, a "network" refers to a connection structure that enables information exchange between each node, such as terminals and servers, and includes a local area network (LAN), a wide area network (WAN), the Internet (WWW: World Wide Web), wired and wireless data communication networks, telephone networks, and wired and wireless television communication networks.Examples of wireless data communication networks include, but are not limited to, 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, Bluetooth, infrared, ultrasonic, visible light communication (VLC), and LiFi.

[0028] FIG. 1 is a drawing showing a meat taste and tenderness measuring device and a terminal connected to the same according to one embodiment of the present invention.

[0029] Referring to FIG. 1, a meat taste and tenderness measuring device (100) and a terminal (200) can be connected to each other through a communication network. Hereinafter, the meat taste and tenderness measuring device (100) may be referred to as a server. The server may be implemented as a cloud computing server such as SaaS (Software as a Service), PaaS (Platform as a Service), or IaaS (Infrastructure as a Service). In addition, the server may be implemented in the form of a private cloud, public cloud, or hybrid cloud system, but the scope of the present invention is not limited thereto. In addition, the meat taste and tenderness measuring device (100) may be operated using an artificial intelligence model.

[0030] The meat taste and tenderness measuring device (100) generates a learning data set including at least one learning meat image and a sensory evaluation table matching the learning meat image.

[0031] The meat taste and tenderness measuring device (100) generates an artificial intelligence model trained to measure the taste and tenderness of meat included in an input meat image based on a learning data set. Here, the taste can be determined based on quantitative marbling values, meat color values, moisture values, tissue density values, etc., which are calculated based on evaluation values ​​of multiple experts. In addition, tenderness refers to the degree of tenderness and softness of the meat, and like the taste, it can be determined based on marbling values, meat color values, moisture values, tissue density values, etc. In addition, according to an embodiment of the present invention, the marbling values, meat color values, moisture values, tissue density values, etc. of processed meat and aged meat can be determined to measure the aging age, consumption date, post-processing date, etc. of the meat.

[0032] The terminal (200) can transmit an image to the meat taste and tenderness measuring device (100). In addition, the terminal can receive the meat taste and tenderness in the form of visual data from the meat taste and tenderness measuring device (100).

[0033] Figure 2 is a detailed drawing of the meat taste and tenderness measuring device in Figure 1.

[0034] Referring to FIG. 2, the meat taste and year measuring device (100) may include a communication module (110), a processor (120), and a memory (130).

[0035] The communication module (110) may include a device including hardware and software necessary to transmit and receive signals such as control signals or data signals through wired or wireless connections with other network devices.

[0036] The communication module (110) can receive images of meat from the terminal (200). Furthermore, the communication module (110) can provide the terminal (200) with information on the taste and age of the meat in the form of visual data including images, graphs, and tables. However, this is not limited thereto, and, if necessary, the communication module (110) can receive images or videos from external devices and databases other than the terminal (200).

[0037] The processor (120) may include various types of devices that control and process data. The processor (120) may refer to a data processing device built into hardware that has a physically structured circuit to perform a function expressed in code or instructions included in a program.

[0038] In one example, the processor (120) may be implemented in the form of a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), etc., but the scope of the present invention is not limited thereto.

[0039] The processor (120) performs operations according to the code stored in the memory (130).

[0040] The memory (130) can store at least one of information and data input to the communication module (110), information and data required for functions performed by the processor (120), and data generated according to the execution of the processor (120).

[0041] Memory (130) should be interpreted as a general term for a non-volatile storage device that maintains stored information even when no power is supplied and a volatile storage device that requires power to maintain stored information. Memory (130) may include magnetic storage media or flash storage media in addition to a volatile storage device that requires power to maintain stored information, but the scope of the present invention is not limited thereto.

[0042] The memory (130) is electrically connected to the processor (120) and stores at least one code to be executed by the processor (120). The memory (130) stores code that, when executed by the processor (120), causes the processor (120) to perform the following functions and procedures.

[0043] The memory (130) stores code that causes a learning data set to be generated, which includes at least one learning meat image and a sensory evaluation table matching the learning meat image. For example, the sensory evaluation table includes a marbling evaluation value, a meat color evaluation value, a texture evaluation value, a surface juice evaluation value, and a preference evaluation value for meat included in the meat image, and may be set based on criteria for judging the taste and tenderness of beef or the taste and tenderness of pork from a plurality of countries that have been set in advance.

[0044] At this time, the marbling evaluation value is calculated based on the fat distribution pattern of the meat included in the meat image, the meat color evaluation value is calculated based on the saturation of the meat, the texture evaluation value is calculated based on the density, moisture, and deformability of the meat, the meat juice evaluation value is calculated based on the fat content of the meat, and the preference evaluation value can be calculated based on a value preset by the subject of creating the sensory evaluation table.

[0045] The memory (130) stores a code that causes an artificial intelligence model trained to measure the taste and age of meat included in an input meat image based on a learning data set.

[0046] The memory (130) receives a meat image to be measured for taste and tenderness, and stores a code that causes the taste and tenderness of the meat image to be measured using an artificial intelligence model.

[0047] The memory (130) stores code that causes data to be generated that visualizes a region of a meat image as a basis for measured taste and tenderness. For example, the region may be a meat region within the meat image that contains a marbling distribution pattern and meat color exceeding a preset numerical value.

[0048] The memory (130) stores a code that causes the terminal (200) to provide an interface including a meat image, visualization data, marbling value, meat color value, texture value, meat juice value, total, and QR code.

[0049] FIG. 3a and FIG. 3b are diagrams illustrating a process for adjusting the size of a meat image and obtaining a sensory evaluation according to one embodiment of the present invention.

[0050] Referring to Fig. 3a, a meat taste and tenderness measuring device (100) can receive a meat image from a communication-connected terminal (200), cut out a portion surrounding the meat from the meat image by dividing it into sections, and then resize (301) the image. Then, the process shown in Fig. 3b can be performed. More specifically, a resized meat image (302) can be latently vectorized (304) using an artificial intelligence model (303), and taste and tenderness can be measured (305) based on previously learned grade-specific marbling, meat color, texture, meat juice, and preference evaluation values, and a sensory evaluation table can be generated (306).

[0051] At this time, the artificial intelligence model can use CNN (Convolutional Neural Network) and ViT (Vision Transformer) as models for learning.

[0052] CNN is a deep learning model that detects image features through convolution operations and uses these to classify input data or extract meaningful information and learn.

[0053] ViT is a deep learning model that uses an attention mechanism for image processing, and is effective in extracting and classifying global information by converting images into tokens.

[0054] More specifically, ViT implements image processing within a Transformer-structured model, leveraging the Transformer's multi-head attention structure rather than using a CNN for vision tasks. This allows ViT to achieve superior performance compared to other CNN-based models and consumes less computational resources during the training process.

[0055] At this time, large values ​​are emphasized based on the Q (Query), K (Key), and V (Value) values, which are the key element representations for learning.

[0056] Figure 4 is a drawing showing the results of a sensory evaluation according to one embodiment of the present invention.

[0057] Referring to FIG. 4, a meat taste and tenderness measuring device (100) can receive a meat image from a communication-connected terminal (200), and display a color distribution graph of the corresponding grade by combining it (401). At this time, the meat image and the color distribution graph can be arranged to overlap each other, or the meat image and the color distribution graph can be arranged side by side. In addition, a sensory evaluation result (403) can be obtained by utilizing an artificial intelligence model (402). At this time, the sensory evaluation result (403) can include marbling, meat color, texture, surface juice, and preference.

[0058] FIG. 5a and FIG. 5b are diagrams showing a process for obtaining a sensory evaluation according to one embodiment of the present invention.

[0059] Referring to FIG. 5a, as described in FIG. 4, the meat taste and tenderness measuring device (100) receives (501) a meat image from a communication-connected terminal (200), and can display a color distribution meat image (503) by combining a color distribution graph (502) of the corresponding grade. In addition, as shown in FIG. 5b, the taste and tenderness of the meat can be distinguished, and the Grad-CAM technology can be applied to each taste and tenderness of the meat to visualize which part has contributed the most, and the closer it is to red in the meat image, the more it has contributed to the model's prediction. For example, in FIG. 5b, the taste and tenderness of the meat are determined based on the part marked in red in each of the meat images of the taste and tenderness distribution images (504 to 508).

[0060] At this time, Grad-CAM (Gradient-weighted Class Activation Mapping) is a technique for visualizing the prediction results of a deep learning model, and can highlight how much a specific part of the image contributed to the model's decision. This allows for a visual understanding of which part the model is mainly utilizing and for interpreting the model's predictions. An attention map can be used together with Grad-CAM, and the attention map is a technique that visualizes the latent vector in a ViT (Vision Transformer)-based model, which is an artificial intelligence model, by making it square and then resizing it to the same size as the original image.

[0061] FIG. 6a and FIG. 6b are drawings showing an interface provided to a terminal according to one embodiment of the present invention.

[0062] Referring to Fig. 6a, the taste and tenderness evaluation results for raw meat (fresh meat) may provide marbling (601), meat color (602), texture (603), meat juice (604), and total (605) based on the original image (612), and may provide marbling (606), meat color (607), texture (608), meat juice (609), total (610), and an artificial intelligence grade number (611) based on the image (613) visualized by the artificial intelligence model. In addition, a QR code (614), management number, registrant email, and storage time may be provided together.

[0063] Referring to FIG. 6b, the taste and year evaluation results for processed meat may provide marbling (701), meat color (702), texture (703), meat juice (704), total (705), sequence number (707), and time (707) based on the original image (716), and may provide marbling (708), meat color (709), texture (710), meat juice (711), total (712), sequence number (713), time (714), and artificial intelligence grade number (715) based on the image (717) visualized by the artificial intelligence model. In addition, a QR code (718), management number, registrant email, and storage time may be provided together.

[0064] Figure 7 is a flowchart illustrating the sequence of a method for measuring meat taste and tenderness according to another embodiment of the present invention.

[0065] The elevator safety door control method described below can be performed by the meat flavor and tenderness measuring device (100) described above with reference to FIGS. 1 to 6B. Accordingly, the content of the embodiments of the present disclosure described above with reference to FIGS. 1 to 6B can be equally applied to the embodiments described below, and any content overlapping with the above description will be omitted. The steps described below do not necessarily have to be performed in order, and the order of the steps can be set in various ways, and the steps can be performed almost simultaneously.

[0066] Referring to FIG. 7, the method for measuring meat taste and tenderness includes a learning data set generation step (S100), an artificial intelligence model generation step (S200), a taste and tenderness measurement step (S300), a visualization data generation step (S400), and an interface provision step (S500).

[0067] The learning data set generation step (S100) is a step of generating a learning data set including at least one learning meat image and a sensory evaluation table matching the learning meat image.

[0068] The artificial intelligence model creation step (S200) is a step of creating an artificial intelligence model trained to measure the taste and age of meat included in an input meat image based on the above learning data set.

[0069] The taste and tenderness measurement step (S300) is a step of receiving a meat image to be measured for taste and tenderness, and measuring the taste and tenderness of the meat image using the artificial intelligence model.

[0070] The visualization data generation step (S400) is a step of generating data that visualizes a region of the meat image as a basis for the measured taste and year.

[0071] The interface provision step (S500) is a step of providing an interface including a meat image, visualization data, marbling value, meat color value, texture value, meat juice value, total, and QR code to the terminal (200).

[0072] An embodiment of the present invention may also be implemented in the form of a recording medium containing computer-executable instructions, such as program modules, executed by a computer. Computer-readable media may be any available media that can be accessed by a computer, and includes both volatile and nonvolatile media, removable and non-removable media. Furthermore, computer-readable media may include all computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data.

[0073] Although the methods and systems of the present invention have been described with respect to specific embodiments, some or all of their components or operations may be implemented using a computer system having a general-purpose hardware architecture.

[0074] Those skilled in the art will appreciate that the present disclosure can be easily modified into other specific forms based on the above description without changing the technical spirit or essential characteristics of the present disclosure. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The scope of the present disclosure is indicated by the following claims, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present disclosure. The scope of the present application is indicated by the following claims rather than the above detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present application.

Claims

1. A method for measuring the taste and tenderness of meat using an artificial intelligence model performed by a processor. a) generating a learning data set including at least one learning meat image and a sensory evaluation table matching the learning meat image; and b) A method for measuring the taste and tenderness of meat using an artificial intelligence model, comprising a step of creating an artificial intelligence model trained to measure the taste and tenderness of meat included in an input meat image based on the above learning data set.

2. In paragraph 1, c) A method for measuring the taste and tenderness of meat using an artificial intelligence model, further comprising the step of receiving a meat image to be measured for taste and tenderness, and measuring the taste and tenderness of the meat image using the artificial intelligence model.

3. In paragraph 1, The above sensory evaluation table is, Includes marbling evaluation values, meat color evaluation values, texture evaluation values, surface juice evaluation values, and preference evaluation values ​​for meat included in the above meat image, A method for measuring the taste and age of meat using an artificial intelligence model, which is established based on criteria for judging the taste and age of beef or pork from multiple countries.

4. In paragraph 3, The above marbling evaluation value is calculated based on the fat distribution pattern of the meat included in the meat image. The meat color evaluation value is calculated based on the saturation of the meat. The texture evaluation value is calculated based on the density, moisture and deformability of the meat. The juiciness evaluation value is calculated based on the fat content of the meat. A method for measuring the taste and tenderness of meat using an artificial intelligence model, wherein the preference evaluation value is a value preset by the creator of the above sensory evaluation table.

5. In paragraph 1, d) A method for measuring the taste and tenderness of meat using an artificial intelligence model, comprising a step of generating data visualizing a region of the meat image as a basis for the taste and tenderness measured according to the step c).

6. In paragraph 5, A method for measuring the taste and tenderness of meat using an artificial intelligence model, wherein the above-mentioned area is a meat area in which the marbling distribution shape, meat color, texture, and meat juice contained in the meat image are greater than preset numerical values.

7. In paragraph 1, e) A method for measuring the taste and tenderness of meat using an artificial intelligence model, comprising a step of providing an interface including a meat image, visualization data, marbling value, meat color value, texture value, meat juice value, total, and QR code to a terminal.

8. Communication module; at least one processor; and A memory electrically connected to the processor and storing at least one code to be executed by the processor, The above memory, when executed through the processor, causes the processor to: A meat taste and tenderness measuring device, which stores code that causes a learning data set including at least one learning meat image and a sensory evaluation table matching the learning meat image, to be created, and to create an artificial intelligence model trained to measure the taste and tenderness of meat included in an input meat image based on the learning data set.

9. In paragraph 8, The above memory, when executed through the processor, causes the processor to: A meat taste and tenderness measuring device, which receives a meat image to be measured for taste and tenderness, measures the taste and tenderness of the meat image using the artificial intelligence model, generates data visualizing a region of the meat image as a basis for the measured taste and tenderness, and stores a code causing the terminal to provide an interface including the meat image, visualization data, marbling value, meat color value, texture value, meat juiciness value, total, and QR code.

10. In paragraph 8, The above sensory evaluation table is, Includes marbling evaluation values, meat color evaluation values, texture evaluation values, surface juice evaluation values, and preference evaluation values ​​for meat included in the above meat image, A meat taste and age measuring device, which is set based on criteria for judging the taste and age of beef or pork from multiple countries.

11. In paragraph 10, A meat taste and tenderness measuring device, wherein the marbling evaluation value is calculated based on the fat distribution pattern of the meat included in the meat image, the meat color evaluation value is calculated based on the saturation of the meat, the texture evaluation value is calculated based on the density, moisture, and deformability of the meat, the meat juice evaluation value is calculated based on the fat content of the meat, and the preference evaluation value is a value preset by the subject of creating the sensory evaluation table.

12. In paragraph 9, A meat taste and tenderness measuring device, wherein the above-mentioned area is a meat area in which the marbling distribution shape, meat color, texture, and meat juice contained within the meat image are greater than preset numerical values.

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