Vegetable packaging quality inspection method and system based on image recognition

Abstract

The present invention provides a method and system for inspecting the quality of packaged vegetables based on image recognition. The method comprises: acquiring first image data, and determining first attribute data of a packer based on the first image data; acquiring second image data, and determining second attribute data of the packaged vegetables corresponding to the packer based on the second image data; determining a packaging quality inspection analysis model based on the first and second attribute data; and performing a packaging quality inspection on the packaged vegetables using the packaging quality inspection analysis model. The present invention performs a targeted analysis of the packaging quality of the packaged vegetables based on the optimal quality inspection analysis model, thereby avoiding the drawback of a "comprehensive" analysis during each quality inspection and significantly improving the efficiency of quality inspection and analysis.

Description

Technical Field

[0001] The present invention relates to the field of intelligent agricultural technology, and in particular to a vegetable packaging quality inspection method, system, electronic equipment and computer storage medium based on image recognition. Background Art

[0002] With the continuous development of smart agriculture, more and more smart agriculture operators have begun to process and pack vegetables at the production site, especially for those high-quality vegetables. Operators can pack the high-quality vegetables and directly connect with relevant merchants, which improves the efficiency of vegetable packaging.

[0003] Existing vegetable packaging is generally done manually, that is, workers select and sort the vegetables, and then use paper or plastic packaging materials to pack the high-quality vegetables. In order to avoid damage to the vegetables during transportation due to unqualified packaging, packaging quality inspection equipment is also required to inspect the packaging quality.

[0004] Existing techniques for vegetable packaging quality inspection typically rely on a single quality inspection and analysis model. This model is typically pre-trained using images of various different vegetables with various substandard packaging patterns, enabling it to identify a wide range of substandard packaging. However, to improve the accuracy and comprehensiveness of substandard identification, the trained quality inspection and analysis model incorporates too many functional relationships, requiring a comprehensive analysis during each packaging inspection. This results in low analysis efficiency and is particularly unsuitable for scenarios requiring large volumes and short inspection times. Summary of the Invention

[0005] In order to at least solve the technical problems existing in the above-mentioned background technology, the present invention provides a vegetable packaging quality inspection method, system, electronic device and computer storage medium based on image recognition.

[0006] A first aspect of the present invention provides a vegetable packaging quality inspection method based on image recognition, comprising the following steps:

[0007] Acquire first image data, and determine first attribute data of a packaging person based on the first image data;

[0008] acquiring second image data, and determining second attribute data of the packaged vegetables corresponding to the packaging person based on the second image data;

[0009] Determining a packaging quality inspection analysis model based on the first attribute data and the second attribute data;

[0010] The packaged vegetables are subjected to a packaged quality inspection using the packaged quality inspection analysis model.

[0011] Optionally, the first image data is the same as or different from the second image data.

[0012] Optionally, the number of the first attribute data is determined. If the number is one, the first image data is set to be different from the second image data; if the number is not one, the first image data is set to be the same as the second image data.

[0013] Optionally, determining a packaging quality inspection analysis model according to the first attribute data and the second attribute data includes:

[0014] determining a plurality of analysis models according to the second attribute data;

[0015] The third attribute data of the packaging personnel is determined based on the first attribute data, and a matching calculation is performed based on the third attribute data to screen and obtain the packaging quality inspection analysis model from the plurality of analysis models.

[0016] Optionally, the third attribute data is the first historical packaging data corresponding to the packaging person;

[0017] Then, performing a matching calculation based on the third attribute data to screen out the packaging quality inspection analysis model from the plurality of analysis models includes:

[0018] Determine historical unqualified data of the packaging personnel based on the first historical packaging data, and determine fourth attribute data and fifth attribute data based on the historical unqualified data;

[0019] Perform matching calculations on the fourth attribute data and the fifth attribute data with the plurality of analysis models one by one to obtain a first analysis model;

[0020] The packaging quality inspection analysis model is determined based on the first analysis model.

[0021] Optionally, the method further includes:

[0022] acquiring third image data, and determining a proficiency value of the packaging personnel based on the third image data;

[0023] Then, the matching calculation is performed one by one with the fourth attribute data and the fifth attribute data and the plurality of the first analysis models to screen and obtain the packaging quality inspection analysis model, further comprising:

[0024] Acquire second historical data of other packaging personnel according to the proficiency value, and determine sixth attribute data and seventh attribute data according to the second historical data;

[0025] Perform matching calculations on the sixth attribute data and the seventh attribute data with a plurality of the first analysis models one by one to obtain a second analysis model;

[0026] The packaging quality inspection analysis model is determined based on the second analysis model.

[0027] Optionally, the proficiency value is negatively correlated with the number / range of the other packaging personnel, that is, the larger the proficiency value, the smaller / smaller the number / range, that is, the smaller the amount of the second historical data, and conversely, the larger / larger the number / range, that is, the larger the amount of the second historical data.

[0028] The second aspect of the present invention provides a vegetable packaging quality inspection system based on image recognition, comprising a processing module, a storage module, and an acquisition module, wherein the processing module is connected to the storage module and the acquisition module;

[0029] The storage module is used to store executable computer program code;

[0030] The acquisition module is used to acquire image data of the vegetable packaging site and transmit it to the processing module;

[0031] The processing module is configured to execute the method as described in any of the preceding items by calling the executable computer program code in the storage module.

[0032] The third aspect of the present invention provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calling the executable program code stored in the memory to execute any of the methods described in the preceding items.

[0033] A fourth aspect of the present invention provides a computer storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method described in any one of the above items is executed.

[0034] Compared with the method described in the background art of using a single quality inspection and analysis model for "comprehensive" quality inspection and analysis, the solution of the present invention determines the optimal packaging quality inspection and analysis model based on the attribute data of the packaging personnel and the corresponding vegetables, and then conducts targeted analysis on the packaging quality of the corresponding packaged vegetables based on the optimal model, thereby avoiding the disadvantage of "comprehensive" analysis for each quality inspection and greatly improving the efficiency of quality inspection and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 This is a flow chart of a vegetable packaging quality inspection method based on image recognition disclosed in an embodiment of the present invention;

[0037] Figure 2 This is a structural diagram of a vegetable packaging quality inspection system based on image recognition disclosed in an embodiment of the present invention;

[0038] Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0040] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0041] In the description of the present invention, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the accompanying drawings, or is the orientation or position relationship in which the product of the invention is usually placed when in use. It is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the system or component referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0042] The terms "first," "second," "third," and "fourth" in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order of objects. For example, the terms "first input," "second input," "third input," and "fourth input" are used to distinguish different inputs rather than to describe a specific order of inputs.

[0043] In the embodiments of the present invention, words such as "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0044] In the description of the embodiments of the present invention, unless otherwise specified, “multiple” means two or more than two. For example, multiple processing units means two or more processing units; multiple elements means two or more elements, etc.

[0045] It should be noted that, in the absence of conflict, the features in the embodiments of the present invention may be combined with each other.

[0046] See also Figure 1 , Figure 1 This is a flow chart of a vegetable packaging quality inspection method based on image recognition disclosed in an embodiment of the present invention. Figure 1 As shown, a vegetable packaging quality inspection method based on image recognition according to an embodiment of the present invention includes the following steps:

[0047] Acquire first image data, and determine first attribute data of a packaging person based on the first image data;

[0048] acquiring second image data, and determining second attribute data of the packaged vegetables corresponding to the packaging person based on the second image data;

[0049] Determining a packaging quality inspection analysis model based on the first attribute data and the second attribute data;

[0050] The packaged vegetables are subjected to a packaged quality inspection using the packaged quality inspection analysis model.

[0051] In an embodiment of the present invention, compared with the method of using a single quality inspection and analysis model for "comprehensive" quality inspection and analysis as described in the background technology, the present invention determines the optimal packaging quality inspection and analysis model based on the attribute data of the packaging personnel and the corresponding vegetables, and then conducts targeted analysis on the packaging quality of the corresponding packaged vegetables based on the optimal model, thereby avoiding the disadvantages of "comprehensive" analysis for each quality inspection and greatly improving the efficiency of quality inspection and analysis.

[0052] The above and subsequent solutions of the present invention can be implemented by a dedicated processing device or by a remote server. The dedicated processing device can be a smart phone, tablet computer, laptop computer, PDA, personal computer, smart TV, smart watch, etc., but is not limited to this. The server involved can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. The terminal device and the server can be directly or indirectly connected through wired or wireless communication, and the present invention does not limit this. The number of servers and terminal devices is also not limited.

[0053] Optionally, the first image data is the same as or different from the second image data.

[0054] Optionally, the number of the first attribute data is determined. If the number is one, the first image data is set to be different from the second image data; if the number is not one, the first image data is set to be the same as the second image data.

[0055] In an embodiment of the present invention, an actual packaging scenario may involve multiple or just one packer. To address this situation, the present invention first determines the number of packers based on the first image data. If there is only one packer, the first image data and first attribute data only need to be acquired once within a single cycle, followed by subsequent acquisitions of different second image data and second attribute data at intervals. If there are more than one packer, the packer's position may vary, necessitating simultaneous extraction of both first and second attribute data from each captured image. Of course, in the case of only one packer, the first and second image data can be identical when the first / second attribute data is initially acquired.

[0056] Optionally, determining a packaging quality inspection analysis model according to the first attribute data and the second attribute data includes:

[0057] determining a plurality of analysis models according to the second attribute data;

[0058] The third attribute data of the packaging personnel is determined based on the first attribute data, and a matching calculation is performed based on the third attribute data to screen and obtain the packaging quality inspection analysis model from the plurality of analysis models.

[0059] In this embodiment of the present invention, multiple analysis models are pre-trained, primarily based on vegetable category and substandard category. Each vegetable corresponds to several analysis models, each of which corresponds to at least one substandard category. Consequently, the second attribute data of the packaged vegetables can be used to determine the specific major category of the analysis model. Furthermore, the third attribute data of the packer can be used to determine the specific subcategory of the analysis model, thereby enabling the selection of the optimal analysis model.

[0060] Optionally, the third attribute data is the first historical packaging data corresponding to the packaging person;

[0061] Then, performing a matching calculation based on the third attribute data to screen out the packaging quality inspection analysis model from the plurality of analysis models includes:

[0062] Determine historical unqualified data of the packaging personnel based on the first historical packaging data, and determine fourth attribute data and fifth attribute data based on the historical unqualified data;

[0063] Perform matching calculations on the fourth attribute data and the fifth attribute data with the plurality of analysis models one by one to obtain a first analysis model;

[0064] The packaging quality inspection analysis model is determined based on the first analysis model.

[0065] In this embodiment of the present invention, historical data for each packer is retrieved and analyzed to derive the packer's fourth and fifth attribute data. The fourth attribute data corresponds to the second attribute data, specifically the broad category of vegetables, while the fifth attribute data corresponds to the first attribute data, specifically the specific subcategory of unqualified products. Simultaneously, each analysis model also includes the corresponding fourth and fifth attribute data. Thus, through matching calculations, a matching analysis model is determined to serve as the packaging quality inspection analysis model.

[0066] Optionally, the method further includes:

[0067] acquiring third image data, and determining a proficiency value of the packaging personnel based on the third image data;

[0068] Then, the matching calculation is performed one by one with the fourth attribute data and the fifth attribute data and the plurality of the first analysis models to screen and obtain the packaging quality inspection analysis model, further comprising:

[0069] Acquire second historical data of other packaging personnel according to the proficiency value, and determine sixth attribute data and seventh attribute data according to the second historical data;

[0070] Perform matching calculations on the sixth attribute data and the seventh attribute data with a plurality of the first analysis models one by one to obtain a second analysis model;

[0071] The packaging quality inspection analysis model is determined based on the second analysis model.

[0072] In an embodiment of the present invention, the aforementioned embodiment may match multiple first analysis models. To address this situation, the present invention further obtains sixth and seventh attribute data for other packers (using the same method as used to determine the fourth and fifth attribute data for the aforementioned specific packer). These data reflect the most common types of failures for other packers, i.e., the types of failures frequently encountered by workers working with the same equipment or in the same workshop. This allows for further screening of the first analysis model, resulting in a second analysis model that encompasses the union of the specific packer's failure types and the other packers' most common failure types, enabling better quality inspection of packaging.

[0073] Optionally, the proficiency value is negatively correlated with the number / range of the other packaging personnel, that is, the larger the proficiency value, the smaller / smaller the number / range, that is, the smaller the amount of the second historical data, and conversely, the larger / larger the number / range, that is, the larger the amount of the second historical data.

[0074] In this embodiment of the present invention, the scope of second historical data acquisition for other packers is determined based on the proficiency of a specific packer's packing operations. This is because the more proficient a packer is, the less likely they are to produce unqualified packs. Furthermore, their error types are more consistent, meaning they rarely make the same mistakes as others. Therefore, the amount and scope of the second historical data can be appropriately narrowed to obtain a more accurate second analysis model. The scope of other packers involved can be expanded physical areas such as the same machine, the same area, or the same workshop.

[0075] Among them, the proficiency value of the packer can be determined based on factors such as the time required to pack a single bundle of vegetables and the number of packages completed per unit time. The details will not be elaborated here.

[0076] See also Figure 2 , Figure 2 This is a structural diagram of a vegetable packaging quality inspection system based on image recognition disclosed in an embodiment of the present invention. Figure 2 As shown, a vegetable packaging quality inspection system based on image recognition according to an embodiment of the present invention comprises a processing module (101), a storage module (102), and an acquisition module (103), wherein the processing module (101) is connected to the storage module (102) and the acquisition module (103); wherein,

[0077] The storage module (102) is used to store executable computer program code;

[0078] The acquisition module (103) is used to acquire image data of the vegetable packaging site and transmit it to the processing module (101);

[0079] The processing module (101) is configured to execute the method according to the first embodiment by calling the executable computer program code in the storage module (102).

[0080] The specific functions of the vegetable packaging quality inspection system based on image recognition in this embodiment refer to the above embodiments. Since the system in this embodiment adopts all the technical solutions of the above embodiments, it at least has all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0081] See also Figure 3 , Figure 3 An electronic device disclosed in an embodiment of the present invention includes: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the method as described in the above embodiment.

[0082] An embodiment of the present invention further discloses a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in the above embodiment is executed.

[0083] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input system, and at least one output system, and transmit data and instructions to the storage system, the at least one input system, and the at least one output system.

[0084] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing system so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0085] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, system, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0086] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display system (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing system (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of systems can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0087] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0088] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0089] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0090] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A vegetable packaging quality inspection method based on image recognition, characterized by: The steps include: Acquire first image data, and determine first attribute data of a packaging person based on the first image data; acquiring second image data, and determining second attribute data of the packaged vegetables corresponding to the packaging person based on the second image data; Determining a packaging quality inspection analysis model based on the first attribute data and the second attribute data; Performing packaging quality inspection on the packaged vegetables using the packaging quality inspection analysis model; The determining of the packaging quality inspection analysis model according to the first attribute data and the second attribute data includes: determining a plurality of analysis models according to the second attribute data; Determining third attribute data of the packaging personnel based on the first attribute data, and performing matching calculation based on the third attribute data to screen and obtain the packaging quality inspection analysis model from the plurality of analysis models; The third attribute data is the first historical packaging data corresponding to the packaging person; Then, performing a matching calculation based on the third attribute data to screen out the packaging quality inspection analysis model from the plurality of analysis models includes: Determine historical unqualified data of the packaging personnel based on the first historical packaging data, and determine fourth attribute data and fifth attribute data based on the historical unqualified data; Perform matching calculations on the fourth attribute data and the fifth attribute data with the plurality of analysis models one by one to obtain a first analysis model; The packaging quality inspection analysis model is determined based on the first analysis model.

2. The vegetable packaging quality inspection method based on image recognition according to claim 1, characterized in that: The first image data is the same as or different from the second image data.

3. The vegetable packaging quality inspection method based on image recognition according to claim 2, characterized in that: The number of the first attribute data is determined. If the number is one, the first image data is set to be different from the second image data; if the number is not one, the first image data is set to be the same as the second image data.

4. The vegetable packaging quality inspection method based on image recognition according to claim 1, characterized in that: The method further comprises: acquiring third image data, and determining a proficiency value of the packaging personnel based on the third image data; Then, determining the packaging quality inspection analysis model based on the first analysis model further includes: Acquire second historical data of other packaging personnel according to the proficiency value, and determine sixth attribute data and seventh attribute data according to the second historical data; Perform matching calculations on the sixth attribute data and the seventh attribute data with a plurality of the first analysis models one by one to obtain a second analysis model; The packaging quality inspection analysis model is determined based on the second analysis model.

5. The vegetable packaging quality inspection method based on image recognition according to claim 4, characterized in that: The proficiency value is negatively correlated with the number / range of the other packaging personnel, that is, the larger the proficiency value, the smaller the number or the smaller the range, that is, the smaller the amount of the second historical data, and conversely, the larger the number or the larger the range, the larger the amount of the second historical data.

6. A vegetable packaging quality inspection system based on image recognition, characterized by: It includes a processing module, a storage module, and an acquisition module, wherein the processing module is connected to the storage module and the acquisition module; wherein, The storage module is used to store executable computer program code; The acquisition module is used to acquire image data of the vegetable packaging site and transmit it to the processing module; The processing module is configured to execute the method according to any one of claims 1 to 5 by calling the executable computer program code in the storage module.

7. An electronic device comprising: a memory storing executable program code; A processor coupled to the memory; characterized in that: the processor calls the executable program code stored in the memory to execute the method according to any one of claims 1-5.

8. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is executed.

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