Method and apparatus for distributing fruits

Abstract

The embodiments relate to a system for controlling marking of fruit and vegetable objects, wherein the system performs the following operations: measuring the surface characteristics of a fruit and vegetable object by a surface characteristic measuring unit to obtain surface characteristic information including color contrast information and surface stability information; determining a primary marking condition based on the color contrast information and the surface stability information by a marking condition calculation unit; irradiating a laser onto the surface of a fruit and vegetable object by applying the primary marking condition by a laser marking unit to implement a primary marking; photographing the fruit and vegetable object by a recognition test module to decode the primary marking; determining an additional marking condition based on the result of the decoding by a correction unit; and irradiating a laser onto the surface of a fruit and vegetable object by applying the additional marking condition by a laser marking unit to implement a secondary marking.

Description

Technology Field

[0001] The embodiments of the present invention relate to a technology for distributing fruits and vegetables, and more specifically, to a technology for identifying fruits and vegetables in a more environmentally friendly manner during the distribution process. Background Technology

[0002] For fresh foods such as fruits and vegetables, the method of attaching stickers or packaging materials printed with barcodes or QR codes has generally been used to provide consumers with information such as the place of production, harvest date, variety, and distribution channel during the distribution and sales process. While this method has the advantages of being simple and inexpensive, problems frequently arise where identification information cannot be recognized or information transmission fails due to sticker detachment or damage, poor print quality, or contamination by moisture or foreign substances. Furthermore, adhesives or packaging materials are often unhygienic or difficult to separate for disposal, causing environmental pollution; in particular, when they come into direct contact with the surface of fruits and vegetables, they can impair the visual marketability of the product or cause consumer aversion. The problem to be solved

[0003] The embodiments allow for precise and stable marking of identification information on the surface of fruits and vegetables without the need for stickers or printed materials, and are evaluated as an effective technology capable of simultaneously ensuring food hygiene, environmental friendliness, and consumer trust. Furthermore, by automatically adjusting marking conditions based on feedback to guarantee recognition rates while minimizing surface damage, it provides versatility and practicality applicable to various types of fruits and vegetables.

[0004] The technical problems to be solved in the embodiments are not limited to those mentioned above, and other unmentioned technical problems may be considered by those skilled in the art from the various embodiments described below. means of solving the problem

[0005] According to one embodiment, a fruit and vegetable object marking control method comprises: an operation of obtaining surface characteristic information including color contrast information and surface stability information by measuring the surface characteristics of a fruit and vegetable object by a surface characteristic measuring unit; an operation of determining a primary marking condition based on the color contrast information and the surface stability information by a marking condition calculation unit; an operation of implementing a primary marking by irradiating a laser onto the surface of a fruit and vegetable object by applying the primary marking condition by a laser marking unit; an operation of decoding the primary marking by photographing the fruit and vegetable object by a recognition test module; an operation of determining an additional marking condition based on the result of the decoding by a correction unit; and an operation of implementing a secondary marking by irradiating a laser onto the surface of a fruit and vegetable object by applying the additional marking condition by the laser marking unit.

[0006] According to one embodiment, the decoding operation may include: an operation of generating multi-angle image data by photographing the surface of the object on which the first marking has been performed from the front and from a plurality of different angles; and an operation of calculating the success rate of decoding using the multi-angle image data.

[0007] According to one embodiment, the operation of determining the additional marking condition can calculate an additional marking condition in which the marking depth in the first marking condition is amplified by a preset ratio when the decoding success rate from the decoding result is less than a threshold value.

[0008] According to one embodiment, the operation of determining the additional marking condition may include: an operation of identifying a failure location or error block of the decoding; and an operation of calculating an additional marking condition that amplifies the marking depth or adjusts the marking intensity only for the portion corresponding to the identified location.

[0009] According to one embodiment, the operation of determining the additional marking condition may include: an operation of identifying a failure location or error block of the decoding; and an operation of calculating an additional marking condition that amplifies the marking depth or adjusts the marking intensity only for the portion corresponding to the identified location. Effects of the invention

[0010] According to the embodiments, QR code marking optimized for each fruit is made possible by quantitatively analyzing the surface characteristics of fruits, selecting a suitable laser marking method based on the analysis results, and correcting the marking conditions through recognition rate feedback on the initial marking results. This allows for the stable and direct assignment of identification information to the fruit surface without attaching stickers or printed materials, thereby achieving excellent results in terms of hygiene, environmental friendliness, and marketability.

[0011] The laser marking method can fundamentally prevent issues such as detachment, contamination, recognition failure, and adhesive residue that occurred with conventional sticker methods, and enables the stable recognition of precise identification information, such as QR codes, under various environmental conditions. In particular, by including a function that measures the actual recognition rate after marking and automatically corrects the marking depth, output, and number of repetitions based on the results, the present invention can flexibly respond to the diversity of fruit and vegetable surface conditions or external changes during distribution and storage.

[0012] Furthermore, it is designed to initially perform marking at a minimum depth to minimize surface damage, and to re-mark only within the necessary range if the recognition rate is insufficient, thereby ensuring recognition performance while maintaining the external quality of the fruit. Accordingly, it can be utilized as an identification method with diverse potential applications, such as traceability management, anti-counterfeiting, and integration with smart distribution systems, without compromising the marketability of the fruit or consumer satisfaction.

[0013] According to the embodiments, the marking method is automatically selected based on the type of fruit and surface characteristics, and the marking quality is optimized through depth control and feedback correction functions based on quantitative judgment, making it universally applicable to various fruits and vegetables. Furthermore, by automating the entire process, it provides technical effects of high commercial and industrial value.

[0014] The effects obtainable from the embodiments are not limited to those mentioned above, and other unmentioned effects can be clearly derived and understood by a person skilled in the art based on the detailed description below. Brief explanation of the drawing

[0016] The accompanying drawings, included as part of the detailed description to aid in understanding the embodiments, provide various embodiments and explain the technical features of the various embodiments together with the detailed description. FIG. 1 is a diagram showing the configuration of an electronic device according to one embodiment. FIG. 2 is a diagram showing the configuration of a program according to one embodiment. FIG. 3 is a diagram illustrating the overall configuration of a system according to one embodiment. FIG. 4 is a flowchart illustrating the operation of a system according to one embodiment. FIG. 5 is an example of a flowchart of the operation of a system according to one embodiment. FIG. 6 is an example of a marking implemented in a different way according to one embodiment. Specific details for implementing the invention

[0017] The following embodiments are combinations of the components and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless otherwise explicitly stated. Each component or feature may be implemented in a form not combined with other components or features. Additionally, various embodiments may be constructed by combining some components and / or features. The order of operations described in various embodiments may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment.

[0018] In the description of the drawings, procedures or steps that could obscure the essence of the various embodiments were not described, nor were procedures or steps that can be understood by a person of ordinary knowledge in the relevant technical field described.

[0019] Throughout the specification, when a part is described as "comprising" or "including" a component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components. Furthermore, terms such as "...part," "...unit," and "module" as used in the specification refer to a unit that performs at least one function or operation, and this may be implemented in hardware, software, or a combination of hardware and software. Additionally, "one (a or an)," "one," "the," and similar related terms may be used in the context describing various embodiments (particularly in the context of the following claims) in both singular and plural forms, unless otherwise indicated in the specification or clearly contradicted by the context.

[0020] Hereinafter, embodiments according to various examples will be described in detail with reference to the accompanying drawings. The detailed description disclosed below, together with the accompanying drawings, is intended to describe exemplary embodiments of various examples and is not intended to represent the only embodiment.

[0021] In addition, specific terms used in various embodiments are provided to aid in understanding the various embodiments, and the use of such specific terms may be modified in other forms within the scope of not departing from the technical concept of the various embodiments.

[0023] FIG. 1 is a diagram showing the configuration of an electronic device according to one embodiment.

[0024] FIG. 1 is a block diagram of an electronic device (101) in a network environment (100) according to various embodiments. Referring to FIG. 1, in the network environment (100), the electronic device (101) may communicate with an electronic device (102) through a first network (198) (e.g., a short-range wireless communication network) or may communicate with at least one of an electronic device (104) or a server (108) through a second network (199) (e.g., a long-range wireless communication network). According to one embodiment, the electronic device (101) may communicate with the electronic device (104) through a server (108). According to one embodiment, the electronic device (101) may include a processor (120), memory (130), input module (150), sound output module (155), display module (160), audio module (170), sensor module (176), interface (177), connection terminal (178), haptic module (179), camera module (180), power management module (188), battery (189), communication module (190), subscriber identification module (196), or antenna module (197). In some embodiments, at least one of these components (e.g., connection terminal (178)) may be omitted from the electronic device (101), or one or more other components may be added. In some embodiments, some of these components (e.g., sensor module (176), camera module (180), or antenna module (197)) may be integrated into a single component (e.g., display module (160)). The electronic device (101) may be referred to as a client, terminal, or peer.

[0025] The processor (120) can control at least one other component (e.g., hardware or software component) of the electronic device (101) connected to the processor (120) by executing software (e.g., program (140)), for example, and can perform various data processing or operations. According to one embodiment, as at least part of the data processing or operations, the processor (120) can store commands or data received from other components (e.g., sensor module (176) or communication module (190)) in volatile memory (132), process the commands or data stored in volatile memory (132), and store the resulting data in non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., central processing unit or application processor) or an auxiliary processor (123) that can operate independently or together with it (e.g., graphics processing unit, neural processing unit (NPU), image signal processor, sensor hub processor, or communication processor). For example, if the electronic device (101) includes a main processor (121) and an auxiliary processor (123), the auxiliary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a designated function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as part thereof.

[0026] The auxiliary processor (123) may control at least some of the functions or states associated with at least one component of the electronic device (101) (e.g., display module (160), sensor module (176), or communication module (190)) on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. According to one embodiment, the auxiliary processor (123) (e.g., image signal processor or communication processor) may be implemented as part of another functionally related component (e.g., camera module (180) or communication module (190)). According to one embodiment, the auxiliary processor (123) (e.g., neural network processing unit) may include a hardware structure specialized for processing an artificial intelligence model. The artificial intelligence model may be generated through machine learning. Such learning may be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or through a separate server (e.g., server (108)). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model may include a plurality of artificial neural network layers.An artificial neural network may be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to the hardware structure, the artificial intelligence model may include a software structure, either additionally or substantially.

[0027] The memory (130) can store various data used by at least one component of the electronic device (101) (e.g., processor (120) or sensor module (176)). The data may include, for example, input data or output data for software (e.g., program (140)) and related commands. The memory (130) may include volatile memory (132) or non-volatile memory (134).

[0028] The program (140) may be stored as software in memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0029] The input module (150) can receive commands or data to be used for a component of the electronic device (101) (e.g., processor (120)) from outside the electronic device (101) (e.g., user). The input module (150) may include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0030] The sound output module (155) can output a sound signal to the outside of the electronic device (101). The sound output module (155) may include, for example, a speaker or a receiver. The speaker may be used for general purposes, such as multimedia playback or recording playback. The receiver may be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part thereof.

[0031] The display module (160) can visually provide information to an external (e.g., user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling said device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of the force generated by said touch.

[0032] The audio module (170) can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150) or output sound through the sound output module (155) or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphones) connected directly or wirelessly to the electronic device (101).

[0033] The sensor module (176) can detect the operating state of the electronic device (101) (e.g., power or temperature) or the external environmental state (e.g., user state) and generate an electrical signal or data value corresponding to the detected state. According to one embodiment, the sensor module (176) may include, for example, a gesture sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an accelerometer sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biosensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0034] The interface (177) may support one or more specified protocols that can be used for the electronic device (101) to be connected directly or wirelessly to an external electronic device (e.g., electronic device (102)). According to one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0035] The connection terminal (178) may include a connector through which the electronic device (101) can be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0036] The haptic module (179) can convert an electrical signal into a mechanical stimulus (e.g., vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module (179) may include, for example, a motor, a piezoelectric element, or an electric stimulation device.

[0037] The camera module (180) can capture still images and video. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0038] The power management module (188) can manage the power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented, for example, as at least part of a power management integrated circuit (PMIC).

[0039] The battery (189) can supply power to at least one component of the electronic device (101). According to one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0040] The communication module (190) can support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between an electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may include one or more communication processors that operate independently of the processor (120) (e.g., application processor) and support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., cellular communication module, short-range wireless communication module, or GNSS (global navigation satellite system) communication module) or a wired communication module (194) (e.g., LAN (local area network) communication module, or power line communication module). The corresponding communication module among these communication modules can communicate with an external electronic device (104) through a first network (198) (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network (199) (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules may be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can identify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) using subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module (196).

[0041] The wireless communication module (192) can support 5G networks and next-generation communication technologies following 4G networks, for example, new radio access technology. NR access technology can support high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and connection of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low-latency communications (URLLC)). The wireless communication module (192) can support a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate, for example. The wireless communication module (192) can support various technologies for securing performance in the high-frequency band, such as beamforming, massive MIMO (multiple-input and multiple-output), full-dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large-scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), external electronic device (e.g., electronic device (104)), or network system (e.g., second network (199)). According to one embodiment, the wireless communication module (192) can support a Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mMTC, or U-plane latency (e.g., downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) for realizing URLLC.

[0042] An antenna module (197) can transmit a signal or power to or from an external source (e.g., an external electronic device). According to one embodiment, the antenna module (197) may include an antenna comprising a radiator made of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as a first network (198) or a second network (199), may be selected from the plurality of antennas, for example, by a communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device through the selected at least one antenna. According to some embodiments, in addition to the radiator, other components (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as part of the antenna module (197).

[0043] According to various embodiments, the antenna module (197) may form a mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface (e.g., bottom surface) of the printed circuit board and capable of supporting a specified high frequency band (e.g., mmWave band), and a plurality of antennas (e.g., array antennas) disposed on or adjacent to a second surface (e.g., top surface or side surface) of the printed circuit board and capable of transmitting or receiving a signal of the specified high frequency band.

[0044] At least some of the above components can be connected to each other via a communication method between peripheral devices (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)) and exchange signals (e.g., commands or data) with each other.

[0045] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) through a server (108) connected to a second network (199). Each of the external electronic devices (102, or 104) may be the same or different type of device as the electronic device (101). According to one embodiment, all or part of the operations performed on the electronic device (101) may be performed on one or more of the external electronic devices (102, 104, or 108). For example, if the electronic device (101) needs to perform a function or service automatically or in response to a request from a user or another device, the electronic device (101) may request one or more external electronic devices to perform at least part of the function or service instead of performing the function or service itself or additionally. One or more external electronic devices that receive the above request may execute at least part of the requested function or service, or additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may provide the result as is or additionally processed as at least part of the response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device (101) may provide ultra-low latency services using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server using machine learning and / or neural networks. According to one embodiment, the external electronic device (104) or the server (108) may be included within a second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0046] A server (108) is connected to an electronic device (101) and can provide services to the connected electronic device (101). Additionally, the server (108) may proceed with a membership registration process, store and manage various information of users who have registered as members accordingly, and provide various purchasing and payment functions related to the service. Furthermore, the server (108) may share execution data of service applications running on each of multiple electronic devices (101) in real time so that services can be shared among users. In terms of hardware, this server (108) may have the same configuration as a conventional web server or WAP server. However, in terms of software, it may include program modules that perform various functions and are implemented through any language such as C, C++, Java, Visual Basic, Visual C, etc. Additionally, the server (108) generally refers to a computer system that is connected to an unspecified number of clients and / or other servers through an open computer network such as the Internet, receives requests for task execution from clients or other servers, and derives and provides the results of the task, as well as computer software (server program) installed for this purpose. Furthermore, the server (108) should be understood as a broad concept that includes, in addition to the aforementioned server program, a series of application programs running on the server (108) and, in some cases, various databases (DB: Database, hereinafter referred to as "DB") built internally or externally. Accordingly, the server (108) classifies membership registration information and various information and data regarding games, stores them in the DB, and manages them; such DB can be implemented internally or externally of the server (108).Additionally, the server (108) can be implemented using server programs provided for various operating systems such as DOS, Windows, Linux, UNIX, and Macintosh on general server hardware. Representative examples include Website and IIS (Internet Information Server) used in Windows environments, and CERN, NCSA, and APPACH used in UNIX environments. Additionally, the server (108) may be linked with an authentication system and a payment system for user authentication of the service or purchase payment related to the service.

[0047] The first network (198) and the second network (199) refer to a connection structure capable of exchanging information between each node, such as terminals and servers, or a network connecting a server (108) and electronic devices (101, 104). The first network (198) and the second network (199) include, but are not limited to, the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), 3G, 4G, LTE, 5G, Wi-Fi, etc. The first network (198) and the second network (199) may be closed types such as LAN, WAN, etc., but it is preferable that they be open types such as the Internet. The Internet refers to a global open computer first network (198) and second network (199) structure that provides the TCP / IP protocol and various services existing in the upper layer, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP (Simple Network Management Protocol), NFS (Network File Service), and NIS (Network Information Service).

[0048] A database may have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). A database may have a data storage form that allows for the free retrieval (extraction), deletion, editing, and addition of data. A database may be implemented to suit the purpose of an embodiment of the present disclosure using a relational database management system (RDBMS) such as Oracle, Informix, Sybase, and DB2, an object-oriented database management system (OODBMS) such as Gemston, Orion, and O2, and an XML native database such as Excelon, Tamino, and Sekaiju, and may have appropriate fields or elements to achieve its functions.

[0050] FIG. 2 is a diagram showing the configuration of a program according to one embodiment.

[0051] FIG. 2 is a block diagram (200) illustrating a program (140) according to various embodiments. According to one embodiment, the program (140) may include an operating system (142), middleware (144), or an application (146) executable on the operating system (142) for controlling one or more resources of an electronic device (101). The operating system (142) may include, for example, Android™, iOS™, Windows™, Symbian™, Tizen™, or Bada™. At least some of the programs (140) may be preloaded to the electronic device (101) at manufacturing time, for example, or downloaded or updated from an external electronic device (e.g., electronic device (102 or 104), or server (108)) when used by a user. All or part of the program (140) may include a neural network.

[0052] The operating system (142) can control the management (e.g., allocation or reclamation) of one or more system resources (e.g., processes, memory, or power) of the electronic device (101). The operating system (142) may additionally or substantially include one or more driver programs for driving other hardware devices of the electronic device (101), e.g., an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197).

[0053] Middleware (144) may provide various functions to an application (146) so that functions or information provided from one or more resources of an electronic device (101) can be used by the application (146). Middleware (144) may include, for example, an application manager (201), a window manager (203), a multimedia manager (205), a resource manager (207), a power manager (209), a database manager (211), a package manager (213), a connectivity manager (215), a notification manager (217), a location manager (219), a graphics manager (221), a security manager (223), a call manager (225), or a voice recognition manager (227).

[0054] The application manager (201) can, for example, manage the life cycle of the application (146). The window manager (203) can, for example, manage one or more GUI resources used on the screen. The multimedia manager (205) can, for example, identify one or more formats required for the playback of media files and perform encoding or decoding of the corresponding media files using a codec that matches the selected format. The resource manager (207) can, for example, manage the source code of the application (146) or the memory space of the memory (130). The power manager (209) can, for example, manage the capacity, temperature, or power of the battery (189) and, using the relevant information, determine or provide relevant information required for the operation of the electronic device (101). According to one embodiment, the power manager (209) can interact with the BIOS (basic input / output system) (not shown) of the electronic device (101).

[0055] The database manager (211) can, for example, create, search, or modify a database to be used by the application (146). The package manager (213) can, for example, manage the installation or update of the application distributed in the form of a package file. The connectivity manager (215) can, for example, manage a wireless or direct connection between the electronic device (101) and an external electronic device. The notification manager (217) can, for example, provide a function to notify the user of the occurrence of a specified event (e.g., an incoming call, a message, or an alarm). The location manager (219) can, for example, manage location information of the electronic device (101). The graphics manager (221) can, for example, manage one or more graphic effects or related user interfaces to be provided to the user.

[0056] The security manager (223) may, for example, provide system security or user authentication. The telephony manager (225) may, for example, manage voice call functions or video call functions provided by the electronic device (101). The voice recognition manager (227) may, for example, transmit user voice data to the server (108) and receive from the server (108) a command corresponding to a function to be performed on the electronic device (101) based on at least part of the voice data, or text data converted based on at least part of the voice data. According to one embodiment, the middleware (244) may dynamically delete some existing components or add new components. According to one embodiment, at least part of the middleware (144) may be included as part of the operating system (142) or implemented as separate software different from the operating system (142).

[0057] The application (146) may include, for example, a home (251), a dialer (253), an SMS / MMS (255), an IM (instant message) (257), a browser (259), a camera (261), an alarm (263), a contact (265), a voice recognition (267), an email (269), a calendar (271), a media player (273), an album (275), a watch (277), a health (279) (e.g., measuring biometric information such as exercise volume or blood sugar), or an environmental information (281) (e.g., measuring atmospheric pressure, humidity, or temperature information). According to one embodiment, the application (146) may further include an information exchange application (not shown) capable of supporting information exchange between the electronic device (101) and an external electronic device. The information exchange application may include, for example, a notification relay application configured to transmit information (e.g., a call, a message, or an alarm) designated to an external electronic device, or a device management application configured to manage the external electronic device. The notification relay application may transmit notification information corresponding to a designated event (e.g., receiving mail) generated in another application of the electronic device (101) (e.g., an email application (269)) to the external electronic device. Additionally or alternatively, the notification relay application may receive notification information from the external electronic device and provide it to the user of the electronic device (101).

[0058] A device management application can control the power (e.g., turn-on or turn-off) or function (e.g., brightness, resolution, or focus) of an external electronic device or a part of its components (e.g., a display module or camera module of the external electronic device) that communicates with the electronic device (101). The device management application can additionally or substantially support the installation, deletion, or updating of applications running on the external electronic device.

[0059] Throughout this specification, the terms neural network, neural network, and network function may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may generally be referred to as "nodes." These "nodes" may also be referred to as "neurons." A neural network is composed of at least two nodes. The nodes (or neurons) constituting neural networks may be interconnected by one or more "links."

[0060] In a neural network, two or more nodes connected via links can form a relative relationship between an input node and an output node. The concepts of input and output nodes are relative; any node in an output node relationship with respect to one node may be in an input node relationship with respect to another node, and vice versa. As previously mentioned, the input node versus output node relationship can be generated based on links. One or more output nodes may be connected to a single input node via links, and vice versa.

[0061] In a relationship between an input node and an output node connected through a single link, the value of the output node can be determined based on data input to the input node. Here, the nodes interconnecting the input and output nodes may have weights. These weights can be variable and may be varied by the user or an algorithm to enable the neural network to perform the desired function. For example, if one or more input nodes are interconnected to a single output node via respective links, the output node value can be determined based on the values ​​input to the input nodes connected to the output node and the weights set on the links corresponding to each input node.

[0062] As described above, a neural network is formed in which two or more nodes are interconnected through one or more links to create input-output node relationships within the network. The characteristics of a neural network can be determined by the number of nodes and links within the network, the relationships between the nodes and links, and the weight values ​​assigned to each link. For example, if two neural networks exist with the same number of nodes and links but different weight values ​​between the links, the two neural networks can be recognized as being different from each other.

[0064] FIG. 3 is a diagram illustrating the overall configuration of a system according to one embodiment.

[0065] A system according to one embodiment relates to a technology for identifying fruits and vegetables in a more eco-friendly way during the distribution process.

[0066] For fresh foods such as fruits and vegetables, the method of attaching stickers or packaging printed with barcodes or QR codes has generally been used to provide consumers with information such as production location, harvest date, variety, and distribution channels during the distribution and sales process. While this method has the advantages of being simple and inexpensive, problems frequently arise where identification information cannot be recognized or fails to be conveyed due to sticker detachment or damage, poor print quality, or contamination by moisture or foreign substances. Furthermore, adhesives or packaging materials are often unhygienic or difficult to separate for disposal, causing environmental pollution; in particular, when they come into direct contact with the surface of fruits and vegetables, they can impair the visual marketability of the product or cause consumer aversion.

[0067] A system according to one embodiment can precisely and reliably mark identification information on the surface of fruits and vegetables without the need for stickers or printed materials, and this is an effective technology that can simultaneously ensure food hygiene, environmental friendliness, and consumer trust. In addition, by automatically adjusting marking conditions based on feedback to guarantee recognition rates while minimizing surface damage, it provides versatility and practicality applicable to various types of fruits and vegetables.

[0068] According to the embodiments, QR code marking optimized for each fruit is made possible by quantitatively analyzing the surface characteristics of fruits, selecting a suitable laser marking method based on the analysis results, and correcting the marking conditions through recognition rate feedback on the initial marking results. This allows for the stable and direct assignment of identification information to the fruit surface without attaching stickers or printed materials, thereby achieving excellent results in terms of hygiene, environmental friendliness, and marketability.

[0069] The laser marking method can fundamentally prevent issues such as detachment, contamination, recognition failure, and adhesive residue that occurred with conventional sticker methods, and enables the stable recognition of precise identification information, such as QR codes, under various environmental conditions. In particular, by including a function that measures the actual recognition rate after marking and automatically corrects the marking depth, output, and number of repetitions based on the results, the present invention can flexibly respond to the diversity of fruit and vegetable surface conditions or external changes during distribution and storage.

[0070] Furthermore, it is designed to initially perform marking at a minimum depth to minimize surface damage, and to re-mark only within the necessary range if the recognition rate is insufficient, thereby ensuring recognition performance while maintaining the external quality of the fruit. Accordingly, it can be utilized as an identification method with diverse potential applications, such as traceability management, anti-counterfeiting, and integration with smart distribution systems, without compromising the marketability of the fruit or consumer satisfaction.

[0071] According to the embodiments, the marking method is automatically selected based on the type of fruit and surface characteristics, and the marking quality is optimized through depth control and feedback correction functions based on quantitative judgment, making it universally applicable to various fruits and vegetables. Furthermore, by automating the entire process, it provides technical effects of high commercial and industrial value.

[0072] According to one embodiment, the system includes a server (310), a surface characteristic measuring unit (301), a marking condition calculation unit (302) (not shown), a laser marking unit (303), a recognition test module (305), and a correction unit (304). The server (310) may include the marking condition calculation unit (302) and the correction unit (304). The server (310) may further include means for automatically transporting fruit, for example, a conveyor belt (307) and a related transport system.

[0073] The marking condition calculation unit (302) and the correction unit (304) may be included as components of the server (310), and may be physically implemented in hardware by one or more independent processors or computing units. For example, the marking condition calculation unit (302) may be configured as an independent computing device that analyzes the surface characteristics of the fruit in real time and calculates the marking conditions through a dedicated chipset or microcontroller, and the correction unit (304) may also be configured as a separate computing block or control circuit that receives recognition test results and executes an algorithm to correct the marking conditions.

[0074] Meanwhile, the marking condition calculation unit (302) and the correction unit (304) do not necessarily need to be implemented as physically separated hardware devices, and may be implemented in software by one or more general-purpose processors (e.g., CPU, GPU, or NPU) embedded in the server (310). In this case, the marking condition calculation and correction functions are performed in an execution environment within the server (e.g., operating system, virtual machine, microservice, etc.) in the form of program code, instruction set, or deep learning-based judgment logic, and may share the same processor or memory area in terms of hardware.

[0075] That is, in this embodiment, the marking condition calculation unit (302) and the correction unit (304) may be classified as independent hardware devices depending on the purpose of performing their functions, or they may be implemented as logically separated software modules or algorithm units within a single server (310). This means that various modifications are possible depending on the implementation environment or purpose, and the technical concept of the present invention can be applied equally without being constrained by such differences in implementation forms.

[0076] The server (310) is a central control unit that controls the entire system and oversees computation processing, data transmission and reception, and algorithm execution between each component. The server (310) may include the logical functions of the marking condition calculation unit (302) and the correction unit (304), and may be composed of a hardware device equipped with one or more computation modules (CPU, GPU, memory, etc.), or may be implemented as a cloud-based server or an edge computing unit.

[0077] The server (310) receives data from the surface characteristic measurement unit in real time, executes marking condition calculation and correction logic, and then transmits the result to the laser marking unit (303) and the recognition test module (305) to implement the automated cyclic operation of the system. In addition, the server (310) is responsible for speed control, stop timing control, and synchronization between devices of the transport system (including the conveyor belt 307), and may also include functions for storing and externally transmitting production history data.

[0078] The marking condition calculation unit (302) automatically determines an appropriate marking method (skin decolorization method or fine irregularity marking method) based on the surface information of the fruit transmitted from the surface characteristic measurement unit (301), and performs the function of calculating the primary marking condition accordingly.

[0079] Specifically, the marking condition calculation unit (302) calculates a color contrast index, a surface stability index, etc., to determine whether it corresponds to a first condition or a second condition, and sets marking parameters including a marking method, marking depth, laser output, scanning speed, and repetition count according to the result. This configuration may be implemented with one or more physical processors (e.g., CPU, GPU) or software modules, and may also have a machine learning-based prediction model embedded.

[0080] The correction unit (304) analyzes the results of the recognition test module (305) and, if the recognition rate of the QR code is less than a preset threshold (e.g., 95%), performs the function of correcting the existing marking conditions to create additional marking conditions.

[0081] The correction method is divided into amplification of the total marking depth (e.g., +10~20%) or partial re-marking limited to a portion of the block area, and the corrected condition is transmitted back to the laser marking unit (303) to perform secondary or final marking. The correction unit (304) can be implemented in software on the processor of the server (310) and may have an AI algorithm embedded to learn the area of ​​recognition failure.

[0082] The surface characteristic measuring unit (301) is a device that quantitatively analyzes the external surface condition of the fruit and vegetable to be marked, and provides various data necessary for selecting a laser marking method and calculating marking conditions. The measurement items include color difference ( E), RGB average value, pigment density, absorbance, surface moisture content, oxidation sensitivity, surface roughness, cuticle thickness, etc. may be included.

[0083] These measurements can be performed by a combination of one or more machine vision cameras, infrared sensors, hyperspectral sensors, near-infrared absorption sensors, spectroscopic sensors using optical filters, humidity analyzers, etc., and the measured values ​​are transmitted in real time to a server (310) for reference by a marking condition calculation unit (302). The measurement unit is designed to rapidly scan moving fruit on a conveyor belt in a non-contact manner so as not to reduce the processing speed of the system.

[0084] The laser marking unit (303) is configured to mark a QR code or identification information on the surface of a fruit according to calculated marking conditions, for example, in the form of a multi-joint robot arm, and is equipped with a laser marking head at its end.

[0085] The marking head is precisely controlled to perform marking at appropriate locations by considering the curvature, reflectivity, and material properties of the fruit surface, and types of lasers that may be used include fiber lasers (e.g., 1064 nm), DPSS lasers (532 nm), and CO2 lasers. Marking is primarily performed in the first stage at a minimum reference depth (e.g., 15–25 μm) and is accurately executed on designated areas, such as the bottom, sides, or logo locations of the fruit.

[0086] The recognition test module (305) is a device that evaluates whether a marked QR code can actually be correctly recognized through a smartphone camera or a dedicated decoder. The module photographs the marked fruit from the front and multiple angles (±15°, etc.) using a fixed-position shooting device (e.g., industrial camera, CMOS image sensor), and measures the recognition rate, distortion rate, contrast level, etc. of the QR code using its own decoding algorithm or an external library.

[0087] The measured result is transmitted to the server (310) and serves as the basis for the correction unit (304) to determine whether recognition is successful, and if necessary, leads to the creation of a re-marking condition.

[0088] The conveyor belt (307) serves as a primary means of transport for automating and continuously processing the entire process flow of the fruit and vegetable marking system according to the present invention, and performs the role of transporting a marking target object, such as a fruit, to a marking position and transporting the marked object to a subsequent process or packaging process.

[0089] The conveyor belt (307) is generally composed of an infinitely continuous rotating belt structure in which a rotating shaft and a power drive unit are connected, and the belt may include fixed slots, grooves, or guides to stably maintain the position of fruits and vegetables or to prevent rotation. In addition, the movement speed of the belt can be controlled by considering the system's throughput, laser marking time, recognition test time, etc., and can be linked with a precision controller (e.g., servo motor, inverter, etc.) capable of stopping and restarting as needed.

[0090] The conveyor belt (307) collects surface information of the target object by passing through the measurement area of ​​the surface characteristic measuring unit positioned in front of the marking position, and automatically aligns and stops at the marking position of the subsequent laser marking unit (303) to enable precise QR code marking. Afterward, the object that has completed marking moves again and enters the shooting range of the recognition test module (305), and depending on the recognition result, it is returned to the secondary marking unit or discharged to the final packaging process through the judgment of the correction unit (304).

[0091] Additionally, the conveyor belt (307) may be further equipped with a sub-guide device or a sensing sensor (e.g., a photo sensor, a position sensor, etc.) to correct or fix the position alignment according to the size or shape of the fruit, and may be expanded into a transport system that branches or integrates multiple transport lines. For example, a structure that branches and transports to different marking units according to the marking method (skin decolorization method vs. fine irregularity marking method) can also be implemented.

[0092] Accordingly, the conveyor belt (307) and the related transport system of the present invention are key components for realizing precision and automation of the fruit and vegetable marking process, and are organically linked with all functional modules within the system, contributing to improving overall marking quality and production efficiency through stable transport of fruit and accurate position control.

[0093] According to one embodiment, in order to provide identification information by marking a QR code on the surface of fruits with hard shells, the system can perform a series of processes including selecting an appropriate marking method considering the surface characteristics of the fruit and the distribution environment, evaluating the recognition rate in real time after marking, and automatically correcting it if necessary.

[0094] First, when the fruit (321, 323, 325) moves along the conveyor belt, the RGB brightness, pigment density, and color difference of the corresponding fruit are measured by the surface characteristic measuring unit. Information such as E), absorbance, moisture content, oxidation sensitivity, predicted surface deformation, and cuticle thickness is measured or estimated through a prior DB. The measured surface characteristic information is transmitted to the marking condition calculation unit (302), and the marking method is automatically determined according to the first condition (suitability of the epidermal decolorization method) or the manufacturing condition (suitability of the fine irregularity marking method), and at the same time, initial marking conditions such as basic output, scanning speed, and minimum marking depth are set according to the type of fruit.

[0095] Afterward, a laser marking unit (303), such as a marking head of a multi-joint robot arm, performs a primary QR marking with a minimum reference depth on a designated area, such as the bottom or side of the fruit. The fruit with the completed marking is moved to the shooting range of a recognition test module (305) (fixed camera or scanner), and the recognition rate is calculated through multi-angle shooting and decoding. If the decoding success rate is greater than or equal to a preset threshold (e.g., 95%), the marking is finalized and moves to the next process.

[0096] On the other hand, if the recognition rate is below the standard, the correction unit (304) operates to generate a correction marking condition by amplifying the marking depth by a certain ratio or partially re-marking only some blocks, and the laser marking unit (303) applies this again to perform a second marking. Afterward, if the standard is satisfied through a re-test of the recognition rate, the marking is confirmed, and if it is not satisfied, additional correction is possible within the maximum number of repetitions. This feedback-based control method guarantees the recognition rate of the QR code while minimizing surface damage.

[0097] Consequently, the system sequentially performs surface measurement, condition calculation and marking method selection, minimum marking execution, recognition testing, and confirmation when exceeding the standard or correction and re-marking when falling below the standard; since the entire process is automated, stable and eco-friendly QR marking can be realized for various fruits and vegetables.

[0098] Definitions of terms are described below.

[0099] Color difference information refers to information that quantifies the color characteristics of an object to determine color contrast information. Color difference information is a value that quantitatively measures the visual difference between two colors, and generally It is expressed as an E value. Color difference information is calculated based on RGB-based color measurements, for example. If E is 5 or higher, it can be assessed that there is a color difference perceptible to humans. Pigment density information refers to the concentration of pigments contained on the object's surface, used to determine color contrast. Pigment density information serves as a criterion for judging how much pigment can be decolorized upon laser irradiation or the degree of uniformity in the distribution of the pigment itself. Pigment density information is measured relatively high in materials with high pigment concentrations, such as grape skins and citrus peels, and this can be used to evaluate marking methods involving epidermal decolorization. RGB brightness information refers to information indicating the brightness level of the object's surface measured to determine color contrast. RGB brightness information is utilized to calculate total luminance by measuring the luminous intensity values ​​for each R, G, and B channel. For example, if R=200, G=180, and B=170, the brightness information is classified as high luminance, indicating a high likelihood of decolorization contrast being clearly visible. Absorbance refers to information indicating the degree to which light of a specific wavelength is absorbed by the surface, used to determine color contrast. Absorbance represents the optical properties of an object, and the potential or depth of surface color change can be predicted based on the light absorption rate when a laser is irradiated. Peels with high absorbance exhibit a stronger decolorization effect upon laser irradiation, and absorbance can be quantified using spectroscopic sensors. Moisture content information refers to the moisture concentration inside or on the surface of an object measured to assess surface stability. Moisture content serves as a criterion for determining how well the surface shape is maintained after marking; higher moisture levels result in a damper surface, which can lower the recognition rate of the QR code. For example, for peaches with a moisture content of over 90%, the marking area is at risk of spreading over time, making the micro-embossing marking method advantageous. Oxidation sensitivity refers to information indicating the sensitivity of the surface to changes in color or texture in response to oxygen in the air, used to assess surface stability.Oxidation sensitivity is a measure used to quantitatively predict the likelihood of surface changes during distribution and to predict whether color contrast may fade over time. For example, bananas, which have thin skins and oxidize quickly, have high oxidation sensitivity; therefore, the unevenness method is more suitable than the skin bleaching method. Skin thickness refers to numerical information regarding the thickness of an object's skin to assess surface stability. Skin thickness is utilized as a factor to determine surface damage, the range of heat diffusion, and the likelihood of residual unevenness during marking. For example, thick apple skins are easy to maintain unevenness, whereas thin cherry skins can easily crack, so the bleaching method is preferable. Deformation prediction refers to information predicting how the surface shape will change during distribution and storage to assess surface stability. Deformation prediction quantifies the likelihood that the surface will become concave or raised due to moisture loss, temperature changes, external impacts, etc. For example, fruits with a low deformation prediction have high shape retention and are suitable for unevenness marking, whereas conversely, if they are high, bleaching marking is recommended. The first operation method refers to the marking method performed when the first condition is satisfied. The first operation method is based on epidermal bleaching and forms visible markings through pigment changes. For example, the first operation method is implemented by bleaching a QR pattern on intensely pigmented fruits using a low-power laser. The second operation method refers to a marking method performed when the first condition is not satisfied and the second condition is satisfied. The second operation method is a micro-grooving marking method that creates visual contrast by modifying the physical surface structure of the marking target. For example, the second operation method forms QRs by engraving micro-grooves on citrus fruits, where surface color changes are difficult. Epidermal bleaching refers to a marking method that creates light-dark contrast by locally destroying pigments. The epidermal bleaching method focuses on changing the surface color to a lighter shade by utilizing the pigment decomposition effect caused by heat.For example, this method involves irradiating the deep red surface of an apple with a high-temperature laser to create bright brown dots. Micro-embossing marking refers to a method of creating visual patterns by finely carving or pressing the surface to form irregularities. Micro-embossing marking forms code patterns by utilizing differences in three-dimensional shape rather than variations in brightness. For instance, marking is performed on fruits with minimal surface color changes by forming grooves 0.3 mm deep. Recognition rate refers to the percentage of successful decodings when a marked pattern is captured and decoded. The recognition rate is calculated as the ratio of the number of successful decoding images to the total number of captured images. For instance, if a QR code is decoded 9 out of 10 times, the recognition rate is 90%. Contrast is numerical information representing the difference in color or structural brightness between the marked area and the background area. Contrast is a factor that directly affects the recognition rate and can vary depending on discoloration or the depth of the irregularities. For instance, a brightness difference of 30 or more can be evaluated as high contrast. Positional accuracy is numerical information indicating how precisely the QR marking has been implemented at the target location. Positional accuracy is calculated as the error distance between the reference coordinates and the actual marking coordinates on the image. For example, if the difference between the target coordinates and the actual coordinates is within 1 mm, it is judged to have high positional accuracy. Color contrast information refers to color-based visual recognition potential information, including color difference information, RGB brightness information, and pigment density information. Color contrast information is used as basic data for judging the first condition and determines QR recognition potential based on differences in visual brightness. For example, if the surface is deep red and changes to bright yellow upon decolorization, the color contrast information can be considered high. Surface stability information refers to information on the surface's deformation potential, including moisture content, oxidation sensitivity, epidermal thickness, and deformation prediction. Surface stability information is used to judge the second condition and evaluate the possibility of pattern maintenance during long-term preservation.For example, fruits with a high moisture retention rate during storage can be judged to have high surface stability. The primary marking condition refers to an initial set of marking parameters based on the first or second operation mode. The primary marking condition is a set value that includes the marking method, depth, laser output, scanning speed, etc. For example, if the first condition is satisfied, the primary marking condition is set to an output of 15% and a scanning speed of 1.5 mm / s. The secondary marking condition refers to a set of additional conditions set for correction when the recognition rate of the primary marking is low. The secondary marking condition includes values ​​that increase the depth or output for part or all of the marking area. For example, this applies to a condition where, in the event of a primary marking failure, the laser output is increased by 20% to re-mark the same location. Primary marking refers to the initial marking operation performed according to the primary marking condition. Primary marking is the act of initially generating a QR pattern by applying conditions set based on surface characteristics. For example, this applies to the process of engraving QR dot patterns at 1mm intervals on the surface of an apple using a skin bleaching method. Additional marking conditions refer to correction marking parameters set to compensate for decoding failures or reduced recognition rates. These conditions include information that corrects marking intensity, depth, location, and repetition counts. For instance, a condition to increase the marking depth by 0.1mm by selecting only the blocks where errors occurred is included here. Secondary marking refers to correction marking performed by applying additional marking conditions. Secondary marking is a process designed to improve recognition rates by enhancing marking intensity or reprocessing failed blocks. For instance, if a portion of a marked QR is blurry, the process of re-engraving only that specific block corresponds to secondary marking. The decoding success rate refers to the percentage of image decodings that are successfully completed after QR marking. The decoding success rate is measured based on the number of correctly interpreted QRs relative to the total number of images. For instance, if decoding is successful 18 times out of 20 tests, the success rate is 90%.The threshold is a reference value compared to the decoding success rate and is used as a criterion for determining whether re-marking is necessary. The threshold is generally set between 85% and 95%, and correction is required if it falls below this level. For example, if the threshold is 90% and the decoding success rate is 87%, the re-marking procedure is executed. The preset ratio refers to a reference magnification factor that amplifies the marking intensity or depth to calculate secondary marking conditions. The preset ratio is determined based on experimental values ​​or historical data and is set to values ​​such as 1.2x or 1.5x. For example, if the marking depth is increased from the existing 0.2mm to 0.3mm, the ratio is 1.5x. Decoding failure locations refer to coordinates or blocks that were not decoded or where errors occurred during QR marking. Decoding failure locations are identified through image analysis results, and correction marking is performed based on those locations. For example, the bottom-left three blocks out of 21 may be identified as failure locations. An error block refers to a QR area among decoding failure locations where errors occur repeatedly or are determined to be below a threshold. Error blocks are subject to secondary marking and may be areas caused by repetitive malfunctions or damage. For example, if two of the 5x5 blocks in the left center of the QR code are repeatedly not decoded, those blocks become error blocks. The first condition refers to a condition where color contrast information is above a standard. The first condition is primarily a criterion for determining whether decolorization marking is suitable. The second condition refers to a condition where surface stability information is above a standard. The second condition is primarily a criterion for determining whether embossing marking is suitable.

[0101] FIG. 4 is a flowchart illustrating the operation of a system according to one embodiment.

[0102] According to one embodiment, in operation (401), the surface characteristic measuring unit can obtain surface characteristic information including color contrast information and surface stability information by measuring the surface characteristics of a fruit and vegetable object. At this time, the surface characteristic measuring unit (301) can detect various items in real time, such as RGB values, pigment density, moisture content, and oxidation sensitivity, and this is used as key basic data for selecting a marking method and setting conditions in subsequent steps.

[0103] According to one embodiment, in operation (403), the marking condition calculation unit (302) can determine a primary marking condition based on color contrast information and surface stability information. The marking condition calculation unit (302) can determine a primary marking condition based on color contrast information and surface stability information transmitted from the surface characteristic measurement unit (301).

[0104] The marking condition calculation unit (302) can determine whether the first condition is satisfied based on color contrast information. The marking condition calculation unit (302) can first determine whether the fruit and vegetable object satisfies the first condition based on color contrast information.

[0105] For example, the marking condition calculation unit (302) can determine whether the fruit and vegetable object satisfies the first condition using mathematical formula 1.

[0106] [Mathematical Formula 1]

[0107]

[0108] In mathematical formula 1, S is the suitability of the first operation method, and is a numerical value of the condition satisfaction based on the color contrast information of the object, serving as a criterion for quantitatively determining whether the object is suitable for the epidermal decolorization method. E represents the color difference value before and after laser irradiation, and can have a normalized value of, for example, 0 to 100. P represents pigment density, and can have a range of, for example, 0 to 1, where a value closer to 1 indicates a darker pigment. B represents the RGB brightness normalized value, and can have a normalized relative value of, for example, 0 to 1, where a value closer to 0 indicates a darker color and a value closer to 1 indicates a brighter color. A represents the epidermal absorbance at a specific wavelength, and can have a normalized relative value of, for example, 0 to 1, where a value closer to 1 indicates better laser absorption. w1, w2, w3, and w4 represent the weights of each variable and can be determined experimentally.

[0109] First term As the color difference increases, it becomes increasingly saturated and can have a value between 0 and 1. The second term is a logarithmic function applied to be sensitive to changes in pigment density, and the third term It increases exponentially as RGB brightness increases. The fourth term It is modeled to contribute almost linearly as absorbance increases.

[0110] The first condition is It can be expressed as follows. Here, Sth is a threshold value, which can be experimentally set to 2.5, for example. If S>=2.5, the epidermal decolorization method (first condition) can be determined to be suitable.

[0111] For example, each parameter and variable of mathematical formula 1 can be set as follows.

[0112] ,

[0113] At this time, And, as a result of calculation, S is approximately 3.12, so if Sth is 2.5, it can be determined that the first condition is satisfied.

[0114] If the first condition is satisfied, the marking condition calculation unit (302) can determine the first marking condition using the first operation method. If it is determined that the first condition is satisfied, the marking condition calculation unit (302) may determine the first marking condition using the first operation method corresponding to the finding that it is appropriate to apply a surface decolorization method to the surface of the object. This condition is, for example, a color difference ( E) This applies when the score function reflecting pigment density, brightness, absorbance, etc., is greater than or equal to the reference value.

[0115] If the first condition is not satisfied, the marking condition calculation unit (302) can determine whether the second condition is satisfied based on surface stability information. On the other hand, if the first condition is not satisfied, the marking condition calculation unit (302) can additionally determine whether the second condition is satisfied based on surface stability information, which is mainly based on moisture content, oxidation sensitivity, predicted surface deformation value, cuticle thickness, etc.

[0116] For example, the marking condition calculation unit (302) can determine whether the fruit and vegetable object satisfies the second condition using mathematical formula 2.

[0117] [Mathematical Formula 2]

[0118]

[0119] In Equation 2, Y represents the suitability of the second operating mode, a value that numerically evaluates whether the object is suitable for the micro-irregularity marking method based on the object's surface stability information. This is the result of scoring the modeled likelihood that the marking shape will be maintained during distribution and storage after marking. M represents the moisture content (%), which can have a relatively normalized value ranging from 0 to 100, for example; the higher the value, the higher the likelihood of surface deformation. O represents the oxidation sensitivity index, which can have a relatively normalized value ranging from 0 to 1, for example; the closer the value is to 1, the faster the oxidation proceeds. D represents the predicted surface deformation, which can have a relatively normalized value ranging from 0 to 1, for example; it indicates the degree to which deformation occurs easily due to dampness or wrinkles during distribution. T represents the cuticle layer thickness (μm), which ranges from 0 to 100+; a higher value indicates a thicker cuticle layer, signifying higher protective power. w1, w2, w3, and w4 represent the weights of each variable and can be determined experimentally.

[0120] The term simulates the saturation curve, and its value increases as moisture content increases, and Due to the term, oxidation sensitivity reacts sensitively based on 0.5, and Through the term, as the surface strain increases, the value of the term exhibits a pattern of non-linear increase, and According to the clause, the thinner the cuticle thickness, the larger the result value becomes, and the thicker the thickness, the smaller the result value becomes.

[0121] The second condition is It can be expressed as. is the threshold value of the second condition, and, for example, can experimentally range from 2.2 to 2.5. In the case where = 2.5, if Y≥2.5, it can be determined that the fine uneven marking method is suitable.

[0122] For example, each parameter and variable of mathematical formula 2 can be set as follows.

[0123] ,

[0124] At this time, and, calculation result Y It can be determined that the second condition is satisfied as it becomes 3.01.

[0125] If the second condition is satisfied, the marking condition calculation unit (302) can determine the first marking condition using the second operation method. If it is determined that the second condition is satisfied, the marking condition calculation unit (302) can determine the first marking condition using the second operation method of applying a fine unevenness marking method.

[0126] According to one embodiment, in operation (405), the laser marking unit (303) can implement a primary marking by applying primary marking conditions and irradiating a laser onto the surface of a fruit and vegetable object. The laser marking unit (303) can implement a primary marking by applying the primary marking conditions calculated above and irradiating a laser onto the surface of a fruit and vegetable object. The marking unit (303) precisely approaches the marking location and marks a QR code or identification pattern based on a preset output, scanning speed, depth, etc.

[0127] According to one embodiment, in operation (407), the recognition test module (305) can photograph a fruit object and decode a primary marking. The recognition test module (305) can operate to photograph a marked fruit object and decode the primary marking.

[0128] The recognition test module (305) can generate multi-angle image data by photographing the surface of an object that has undergone primary marking from the front and from multiple different angles. The recognition test module (305) can calculate the success rate of decoding using the multi-angle image data. The recognition test module (305) can generate multi-angle image data by taking photos from multiple different angles, such as left, right, up, and down, in addition to the front, and this is used as data to evaluate the actual recognition rate. Subsequently, the recognition test module (305) can quantitatively calculate the decoding success rate of the QR code using the generated multi-angle image data, and this success rate information is provided as an input value for the subsequent correction judgment step.

[0129] According to one embodiment, in operation (409), the correction unit (304) may determine additional marking conditions based on the results of decoding. If the decoding success rate from the decoding results is less than a threshold value, the correction unit (304) may calculate additional marking conditions that amplify the marking depth in the primary marking conditions by a preset ratio. The correction unit (304) may identify the failure location or error block of the decoding. The correction unit (304) may calculate additional marking conditions that amplify the marking depth or adjust the marking intensity only for the part corresponding to the identified location.

[0130] For example, if the decoding success rate is below a preset threshold (e.g., 95%), the correction unit (304) can generate a new condition in which the marking depth in the primary marking condition is amplified by a certain ratio. At this time, the amplification ratio is set experimentally and is automatically corrected to improve marking quality. Additionally, the correction unit (304) can identify locations where the decoding failed or blocks where errors occurred during the decoding process. In such cases, if the decoding failure area is limited to a specific block, the correction unit (304) can generate additional marking conditions to enable partial re-marking by amplifying the marking depth or adjusting the laser intensity only for the part corresponding to that location. This method can prevent excessive re-marking and minimize damage to the appearance of the fruit.

[0131] According to one embodiment, in operation (411), the laser marking unit (303) can implement a secondary marking by irradiating a laser onto the surface of a fruit and vegetable object by applying additional marking conditions. The laser marking unit (303) can implement a secondary marking by irradiating a laser again onto the same surface of the fruit and vegetable object by applying the additional marking conditions. The secondary marking may be a full re-marking or a partial marking, thereby improving the recognition rate of the QR code above a threshold value and finally confirming the marking.

[0133] FIG. 5 is an example of a flowchart of the operation of a system according to one embodiment.

[0134] According to one embodiment, in operation (501), the surface characteristic measuring unit (301) can obtain color contrast information by sensing each fruit or by receiving it from an external storage medium such as a database.

[0135] For example, the surface characteristic measuring unit (301) scans the surface of the fruit to collect data such as RGB values, brightness, and pigment density, and based on this, can quantify the likelihood that color contrast will be clearly visible after laser marking. This process may be performed in real time, or data for a specific variety and harvest time may be retrieved from a pre-established DB and applied.

[0136] According to one embodiment, in operation (503), the surface characteristic measuring unit (301) can obtain surface stability information by sensing each fruit or by receiving it from an external storage medium such as a database.

[0137] Surface stability information includes the moisture content of the fruit, oxidation sensitivity, surface roughness, and the likelihood of dampness occurring during distribution. For example, in cases where fruits like citrus fruits have high moisture content and thin skins and are prone to discoloration during storage, this serves as basic data for determining that embossed marking is more suitable than discoloration marking.

[0138] According to one embodiment, in operation (505), the surface characteristic measuring unit (301) can transmit surface characteristic information to the marking condition calculation unit (302).

[0139] The information transmitted at this stage is sent in real-time in the form of a digital signal and linked with each fruit's unique ID to be matched in the marking condition calculation logic of the next stage.

[0140] According to one embodiment, in operation (507), the marking condition calculation unit (302) can determine whether surface characteristic information satisfies the first condition.

[0141] The first condition primarily refers to cases where color contrast is distinct and the surface is stable, and it quantitatively determines whether a sufficient recognition rate can be secured even with the epidermal decolorization method. If the E value is high, pigment density is high, and RGB contrast is distinct, it is highly likely to meet the first condition.

[0142] According to one embodiment, when the first condition is satisfied, in operation (509), the marking condition calculation unit (302) can determine the first operation method as the first marking condition.

[0143] The first operating method is an epidermal bleaching method that uses laser heat to decolorize pigments on the fruit surface, creating contrast differences and forming a QR code. This method is fast and non-destructive, and is particularly advantageous for fruits such as apples and grapes, which have hard surfaces and are less prone to discoloration.

[0144] According to one embodiment, if the first condition is not satisfied, in operation (511), the marking condition calculation unit (302) can determine whether the surface characteristic information satisfies the second condition.

[0145] The second condition applies when the surface is unsuitable for discoloration or when contrast may fade over time; in this case, it evaluates whether recognition rates can be maintained by utilizing unevenness instead. For example, fruits with high moisture content and sensitivity to oxidation, such as citrus fruits or peaches, are likely to meet the second condition.

[0146] According to one embodiment, when the second condition is satisfied, in operation (513), the marking condition calculation unit (302) can determine the second operation method as the first marking condition.

[0147] The second operating method is a micro-grooving marking method, which physically engraves the QR code pattern by creating grooves on the fruit's surface through fine carving or pressing. This method has the advantage of maintaining the visual pattern even in situations where color changes are difficult or uncertain.

[0148] According to one embodiment, in operation (515), the marking condition calculation unit (302) can finally determine the first marking condition.

[0149] This condition consists of a quantitative set that includes not only the marking method but also the marking depth, laser output, scanning speed, pattern alignment position, and dot spacing.

[0150] According to one embodiment, in operation (517), the marking condition calculation unit (302) can transmit the primary marking condition to the laser marking unit (303).

[0151] The conditions are transmitted as electronic signals and input in real-time into the marking controller mounted on the robot arm for automatic control.

[0152] According to one embodiment, in operation (519), the laser marking unit (303) can apply the received primary marking conditions to implement a primary marking on the surface of the fruit and vegetable.

[0153] At this time, the marking is performed by aligning it on the bottom or flat surface of the fruit based on a designated QR code pattern, and a device to prevent rotation and fix the position of the fruit can be utilized.

[0154] According to one embodiment, in operation (521), the recognition test module (305) can take a picture of the primary marking and perform decoding.

[0155] This module captures marked QR code images from the front and at an oblique angle using a fixed camera or scanner, and produces decoding results through built-in decoder software.

[0156] According to one embodiment, in operation (523), the recognition test module (305) can transmit the decoding result to the correction unit (304).

[0157] This decoding result includes success status, error correction rate, and whether recognition failed for each block, and the correction unit performs additional judgments based on this.

[0158] According to one embodiment, in operation (525), the correction unit (304) can determine additional marking conditions based on the decoding result.

[0159] For example, if the recognition rate is 90%, a condition is set to amplify the marking depth by 15% or to perform local re-marking only in some error areas.

[0160] According to one embodiment, in operation (527), the correction unit (304) can transmit additional marking conditions to the laser marking unit (303).

[0161] The transmitted additional marking conditions are aligned with the existing marking coordinates and controlled to ensure precise overlapping marking.

[0162] According to one embodiment, in operation (529), the laser marking unit (303) can perform a secondary marking on the surface of the fruit and vegetable based on additional marking conditions.

[0163] Through this final correction marking, the brightness contrast or embossing depth of the QR code is enhanced, and the repeated marking can be terminated at the level of 1 to 2 times until the recognition rate exceeds the standard value.

[0164] As such, the system of the present invention organically links each operation, automatically optimizes marking conditions according to the surface characteristics of the fruit, and performs iterative correction by reflecting the actual recognition rate, thereby ultimately providing eco-friendly, hygienic, and highly reliable QR marking results.

[0166] FIG. 6 is an example of a marking implemented in a different way according to one embodiment.

[0167] Referring to FIG. 6, two types of fruits and vegetables to which a QR code marking system according to one embodiment is applied are shown as examples. In both fruits and vegetables, the QR code is implemented in the same location, specifically in a small area at the bottom of the fruit. This is part of a design that selects a location that is identifiable by an image sensor yet is less exposed to friction, impact, etc., during the distribution process. The fruit on the left is an example where the skin bleaching method is applied, and the fruit on the right is an example where the fine unevenness marking method is applied.

[0168] The fruit on the left (e.g., an orange) is an example applied in accordance with the first condition of the invention. Its surface is rich in pigment and possesses a color contrast exceeding a certain level, and its RGB brightness and absorbance exhibit characteristics that allow for localized decolorization upon laser irradiation. Accordingly, the first operating method—namely, the localized decolorization of the pigment layer by a low-power laser—generated a QR pattern, and the generated marking achieved a high recognition rate through color contrast with the surroundings. This method has the advantage of causing no physical damage to the fruit surface and minimizing the risk of external deformation during distribution.

[0169] The fruit on the right (e.g., an apple) is an example of dissatisfaction with Condition 1, which was determined to be unsuitable for the epidermal bleaching method. Since the surface pigment density is low and sufficient color contrast is not secured, the second operating method was applied based on a judgment made according to Condition 2 of the invention. This fruit has relatively stable moisture content, low oxidation sensitivity and deformation predictability, and possesses characteristics such as high retention of the physical irregular structure due to the epidermal thickness being above a certain level. Accordingly, the marking system formed the marking by directly creating fine irregularities on the surface using a laser of medium power or higher. In the figure, the surface structure of the QR code generated on the fruit on the right is more distinctly prominent, which is the result of adjustments made through the depth of the marking and the number of repeated irradiations.

[0170] Although QR codes of the same size and location were applied to both fruits, different marking methods were selected based on differences in color contrast information and surface stability information obtained by the surface characteristic measurement unit, and the marking condition calculation unit numerically calculated the suitability of the first operating method or the second operating method based on the data of each fruit and automatically determined the appropriate marking method.

[0171] In addition, after marking, the QR code is captured from various angles through a recognition test module, and the marking is confirmed only if the decoding success rate is above a set threshold. If the recognition rate is insufficient, additional marking conditions are set through a correction unit, and a second marking can be performed on the same location or error block.

[0173] The embodiments described above may be implemented as hardware components, software components, and / or combinations of hardware and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and one or more software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0174] Software may include computer programs, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or command the processing unit independently or collectively. Software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave so as to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed over networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0175] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiment, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

[0176] Although the embodiments have been described above with reference to the limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, suitable results may be achieved even if the described techniques are performed in a different order than described, and / or if the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0177] Therefore, other implementations, other embodiments, and equivalents to the claims also fall within the scope of the claims set forth below.

Claims

Claim 1 An operation of obtaining surface characteristic information including color contrast information and surface stability information by measuring the surface characteristics of a fruit and vegetable object by a surface characteristic measuring unit; an operation of determining a primary marking condition based on the color contrast information and the surface stability information by a marking condition calculation unit; an operation of implementing a primary marking by irradiating a laser onto the surface of the fruit and vegetable object by applying the primary marking condition by a laser marking unit; an operation of photographing the fruit and vegetable object and decoding the primary marking by a recognition test module; an operation of determining additional marking conditions based on the result of the decoding by a correction unit; The method includes an operation of implementing a secondary marking by applying the additional marking conditions to the surface of the fruit and vegetable object and irradiating a laser by the laser marking unit; the operation of determining the primary marking conditions includes: determining whether a first condition indicating whether a skin bleaching method is suitable for the fruit and vegetable object is satisfied based on the color difference value, pigment density, RGB brightness normalized value, and skin absorbance before and after laser irradiation of the fruit and vegetable object; and if the first condition is not satisfied, determining whether a second condition indicating whether a fine unevenness marking method is suitable for the fruit and vegetable object is satisfied based on the moisture content, oxidation sensitivity, predicted surface deformation, and cuticle layer thickness of the fruit and vegetable object; and the operation of implementing the primary marking includes, if the first condition is satisfied, irradiating a laser to the surface of the fruit and vegetable object to implement the primary marking using the skin bleaching method. A fruit and vegetable object marking control method comprising, when the above second condition is satisfied, an operation of implementing the first marking using the fine unevenness marking method. Claim 2 A method for controlling fruit and vegetable object marking, wherein the decoding operation comprises: an operation of generating multi-angle image data by photographing the surface of the object on which the first marking has been performed from the front and from a plurality of different angles; and an operation of calculating the success rate of decoding using the multi-angle image data. Claim 3 A fruit and vegetable object marking control method according to claim 1, wherein the operation of determining the additional marking condition calculates an additional marking condition in which the marking depth in the primary marking condition is amplified by a preset ratio when the decoding success rate from the decoding result is less than a threshold value. Claim 4 A method for controlling fruit and vegetable object marking, wherein the operation of determining the additional marking condition in claim 1 comprises: an operation of identifying a failure location or error block of the decoding; and an operation of calculating an additional marking condition that amplifies the marking depth or adjusts the marking intensity only for the portion corresponding to the identified location.

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