Grain inspection system

Abstract

A grain inspection system 100 in which a measurement unit 1 and a server 7, and furthermore the server 7 and a terminal 6, are connected to each other via a communication line comprises an imaging means 11 for imaging grain, a feature amount calculation means 12 for calculating a feature amount from a captured image of the grain captured by the imaging means 11, and a pseudo-image generation means 72 for generating a pseudo-image of the grain on the basis of the feature amount. The imaging means 11 is provided to the measurement unit 1. The feature amount calculation means 12 is provided to the measurement unit 1 and / or the server 7. The pseudo-image generation means 72 is provided to the server 7. The pseudo-image of the grain generated by the pseudo-image generation unit is transmitted to the terminal 6 via the communication line by an information transmission means 73 of the server 7.

Description

Grain Inspection System

[0001] The present invention relates to a grain inspection system in which a measurement unit, a server, and a terminal are connected via a communication line.

[0002] As described in Patent Document 1, a system has been known which is configured by connecting an imaging means for capturing images of grains, a cloud server for processing the images of the grains captured by the imaging means to determine the quality of the grains, and a terminal for displaying the results of the quality determination of the grains via an internet line.

[0003] In the "grain discrimination system" described in Patent Document 1, by connecting an imaging means and a terminal prepared by a user to a cloud server via an internet line, it is possible to discriminate the quality of grains photographed by the imaging means. The cloud server is configured to store the discrimination results and photographed images of each grain, so that the user can check these discrimination results and photographed images of each grain on the terminal.

[0004] JP 2024-065866 A

[0005] However, conventional "grain discrimination systems" require image data of grains captured by an imaging means to be transmitted to a cloud server via an internet connection. Therefore, when a large number of captured images of grains with large file sizes are transmitted in real time (simultaneously), there is a risk of delays in transmission. If such a transmission delay occurs, it may become difficult to simultaneously discriminate a large number of grains, as with, for example, an inline sensor (such as the "inline NIR measurement" described in JP-A-5-508225).

[0006] In view of the above problems, the present invention aims to provide a grain inspection system that can inspect and confirm grains more accurately and efficiently than conventional systems.

[0007] In order to solve the above problems, the present invention provides a grain inspection system comprising a measurement unit and a server, and further wherein the server and a terminal are connected to each other via a communication line, the grain inspection system comprising: an imaging means for imaging grains; a feature calculation means for calculating feature quantities from the captured image of the grains captured by the imaging means; and a pseudo image generation means for generating a pseudo image of the grains based on the feature quantities, wherein the imaging means is provided in the measurement unit, the feature calculation means is provided in the measurement unit and / or the server, the pseudo image generation means is provided in the server, and the pseudo image of the grain generated by the pseudo image generation means is transmitted to the terminal via the communication line by an information transmission means of the server.

[0008] Furthermore, in the present invention, the pseudo image generating means generates the pseudo image of the kernel by artificial intelligence using a pseudo image generation model.

[0009] Furthermore, the present invention provides a grain inspection system, wherein the feature amount is information relating to the shape and color of the grain.

[0010] Furthermore, there is provided a grain inspection system characterized by using the inline sensor of the present invention.

[0011] According to the present invention, since not only is large-capacity image file data transmitted to a server via a communication line as in the past, but also feature quantities calculated from a captured image can be transmitted from a measurement unit to a server according to the communication environment, it is possible to significantly reduce the transmission load and avoid delays in data transmission. In addition, since it is not necessary to store large-capacity image file data on a server, there is also the effect of reducing the load on the server for storing the image file data.

[0012] Furthermore, by providing a pseudo image generating means for generating a pseudo image of the grain based on the feature amount, even if the captured image of the grain is not stored in the server, the pseudo image of the grain captured can be sent to a terminal carried by the user, allowing the state of the grain to be viewed and confirmed on the display device of the terminal.

[0013] Furthermore, if the pseudo image generating means generates pseudo images of kernels by artificial intelligence using a pseudo image generation model, it becomes possible to generate pseudo images of kernels with extremely high reproducibility.

[0014] Furthermore, by using an in-line sensor as the measuring unit of the present invention, in-line inspection (mass inspection) can be realized with high accuracy in various processes, not just in the receiving inspection process, in rice milling factories and the like.

[0015] 1 is a schematic configuration diagram of a grain inspection system according to this embodiment; FIG. 2 is a schematic block diagram of a measurement unit according to this embodiment; FIG. 3 is a schematic block diagram of a server according to this embodiment; FIG. 4 is a diagram explaining an example of a feature amount calculation configuration according to this embodiment; FIG. 5 is a diagram explaining a method for generating a pseudo image of a grain according to this embodiment; FIG. 6 is a table showing an example of feature amount calculation according to this embodiment; FIG. 7 is a diagram showing an example of a pseudo image generation model according to this embodiment; and FIG. 8 is a diagram showing an example of a pseudo image generated according to this embodiment.

[0016] Hereinafter, an embodiment of the grain inspection system of the present invention will be described with reference to the drawings. However, the present invention is not limited to the embodiment shown below.

[0017] 1 shows a schematic configuration diagram of a grain inspection system 100 according to this embodiment. As shown in the figure, the grain inspection system 100 includes at least a measurement unit 1, a server 7, and a terminal 6.

[0018] The measurement unit 1 of this embodiment is an inline sensor for rice milling facilities or grain processing facilities, and is applied to devices that can continuously capture images of grains transported by conveying means such as chutes or belts, such as known grain discrimination devices and color sorters equipped with imaging equipment such as optical cameras.

[0019] Of course, the measurement unit 1 of the present invention is not limited to an inline sensor, but can also be applied as an online sensor to a grain inspection device for online analysis of grains collected via a sampling device.

[0020] Furthermore, the measuring unit 1 of the present invention can also be applied as an offline sensor to a tabletop testing device or the like for offline analysis of collected grains in a laboratory.

[0021] The measurement unit 1 is connected to the server 7 via a communication line, and is capable of transmitting grain feature quantities, quality determination results, and the like, which will be described later, to the server 7 in response to various commands from the server 7. The configuration of the communication line and the server 7 is not particularly limited, but an internet line can be suitably used as the communication line. Furthermore, it is also possible to use, for example, a regular server installed in the business premises where the terminal 6 is used or in a related company as the server 7. Of these, a cloud server can be suitably used as the server 7.

[0022] The server 7 and the terminal 6 are connected by a communication line, and in response to various commands from the terminal 6, it is possible to send pseudo images of grains and quality determination results, which will be described later, from the server 7 to the terminal 6. The communication line is not particularly limited, but an internet line can be suitably used as the communication line.

[0023] As the terminal 6, in addition to a smartphone owned by the user, a PDA, a tablet terminal, a notebook PC, a desktop PC, etc. can be used. In particular, devices with excellent portability are preferably used.

[0024] Furthermore, as an additional function, the measuring unit 1 may be configured to be linked to the terminal 6. That is, the measuring unit 1 has unique identification information, and by inputting the unique identification information of the measuring unit 1 into the terminal 6, the measuring unit 1 can be linked to the terminal 6. This allows information such as the discrimination results and pseudo images of the kernels corresponding to the measuring unit 1 to be efficiently viewed and confirmed on the terminal 6.

[0025] The unique identification information may be input to the terminal 6 by reading a two-dimensional code displayed on the measuring unit 1 using a camera mounted on the terminal 6, or by keying in an ID individually assigned to the measuring unit 1 into the terminal 6. In addition, a selection may be made from a plurality of pre-registered measuring units 1 displayed on the terminal 6.

[0026] 2 is a schematic block diagram of the measurement unit 1 in this embodiment. As shown in the figure, the measurement unit 1 includes at least an imaging means 11 that captures an image of a grain to be inspected, a feature amount calculation means 12 that calculates feature amounts from the image of the grain captured by the imaging means 11, and a transmission means 14 that transmits the feature amounts calculated by the feature amount calculation means 12 to the server 7 via a communication line.

[0027] In this embodiment, a quality discrimination means 13 is further provided that can discriminate the quality of the grain, and it is possible to discriminate the quality of the grain based on the image of the grain captured by the imaging means 11 and / or the feature calculated by the feature calculation means 12.

[0028] In this embodiment, the imaging means 11 is equipped with an optical camera (not shown) that captures images of the grains flowing down. Additionally, the imaging means 11 is equipped with a plurality of light sources that emit light toward the grains, and white LEDs are provided. Note that the light sources are not limited to white LEDs, and it is also possible to use single-color LEDs of R (red), G (green), and B (blue).

[0029] Furthermore, the feature quantities in this embodiment are information relating to the shape and color of the grains to be inspected. Explaining in more detail, as shown in Fig. 4, feature quantity calculation means 12 calculates the feature quantities of the grains from the captured image of a grain 2, such as grains 1 to 3, captured by imaging means 11. For example, as shown in the table in Fig. 4, it is possible to calculate the "kernel length," "kernel width," and "area" of each grain (whole grain, broken grain, partially colored grain) as feature quantities.

[0030] Note that the feature quantities are not limited to "grain length," "grain width," and "area." For example, as shown in the table of FIG. 6, in addition to the "length," "width," and "area" of the grain, "perimeter" and "center of gravity" can also be calculated as feature quantities as shape characteristics of the entire grain. Furthermore, it is possible to calculate color characteristics of the entire grain, shape characteristics of the colored region, and color characteristics of the colored region as feature quantities. These feature quantities are then transmitted to the server 7 via a communication line. This configuration eliminates the need to transmit large amounts of image file data to the server 7 as in the past, significantly reducing the volume of transmitted data and making it possible to significantly reduce the transmission load compared to the past.

[0031] As shown in Figure 3, the server 7 of this embodiment is equipped with at least a pseudo image generation means 72 that generates a pseudo image of the grain based on the features transmitted by the transmission means 14 of the measurement unit 1, and an information transmission means 73 that transmits the pseudo image of the grain generated by the pseudo image generation means 72 to the terminal 6 via a communication line.

[0032] The server 7 of this embodiment further includes a storage means 71, which is capable of storing the feature values ​​transmitted by the transmission means 14 of the measurement unit 1, the results of kernel quality determination by the quality determination means 13, etc. It is also possible to configure the storage means 71 to store the pseudo image of the kernel generated by the pseudo image generation means 72.

[0033] 5 , when the user operates the terminal 6 to request the server 7 to display an image of the grain to be inspected, the server 7 reads out the feature quantities of the grain corresponding to a predetermined inspection date and lot number from the storage means 71. Then, based on the read-out feature quantities, a pseudo image of the corresponding grain is generated by the pseudo image generation model of the pseudo image generation means 72. The generated pseudo image of the grain is transmitted from the information transmission means 73 to the terminal 6 via a communication line, and the user can view and confirm the pseudo image of the grain via the display device of the terminal 6.

[0034] In this embodiment, the pseudo-image generating means 72 generates pseudo-images of grains using artificial intelligence with a pseudo-image generation model. For example, as shown in FIG. 7 , a large number of sets of images of whole grains and their feature values, images of broken grains and their feature values, and images of partially colored grains and their feature values ​​can be prepared as training data to construct a pseudo-image generation model. It is also possible to generate highly accurate pseudo-images of grains other than whole grains, broken grains, and partially colored grains by training the feature values ​​shown in the table of FIG. 6 and increasing the number of grain types. For example, for brown rice, pseudo-images of milky white grains, immature grains at the base, white grains at the back and ventral sides, immature green grains, dead green grains, dead white grains, fully colored grains, partially colored grains, and cracked grains can be generated. For polished rice (white rice), pseudo-images of cracked grains, fully colored grains, partially colored grains, damaged grains, fully chalky grains, semi-chalky grains, and white grains at the back and ventral sides can be generated.

[0035] Furthermore, as shown in FIG. 8, it is possible to generate highly accurate pseudo images of various grains such as "short grain," "medium grain," and "long grain."

[0036] (Other Modifications) Although one embodiment of the grain inspection system of the present invention has been described above, the present invention is not limited to the above embodiment, and also includes the following.

[0037] The grains that can be inspected by the grain inspection system include granular materials such as rice, wheat, beans, and corn.

[0038] Furthermore, it is possible to use a generative AI consisting of an autoencoder as a pseudo-image generation model, and it is also possible to add a configuration that automatically finds feature values ​​through learning, which makes it possible to generate pseudo-images of kernels with even higher accuracy.

[0039] In the above-described embodiment, a case has been described in which the feature calculation means 12 and the quality discrimination means 13 are provided on the measuring unit 1 side under conditions in which communication delays are not acceptable. However, for example, in cases in which there is sufficient capacity for data transmission via a communication line, it is also possible to provide the feature calculation means 12 and the quality discrimination means 13 on the server 7, and to have the server 7 calculate the feature values ​​of the kernels.

[0040] Specifically, after the image of the kernel photographed by the measurement unit 1 is received by the server 7, feature amounts are calculated from the photographed image and the feature amounts are stored in the server 7. Therefore, once the feature amounts have been stored in the server 7, there is no need to store the photographed image in the server 7. Therefore, there is no need to provide the server 7 with a large capacity memory. Furthermore, there is no need to provide the measurement unit 1 with advanced and expensive computing capabilities, which is expected to result in an effect of allowing the measurement unit 1 to be configured inexpensively.

[0041] In yet another embodiment, the feature calculation means 12 and / or the quality discrimination means 13 can be provided on both the measurement unit 1 side and the server 7 side. With this configuration, it becomes possible to select whether to send the image of the kernel photographed by the measurement unit 1 to the server 7 and calculate the feature on the server 7 side, or to calculate the feature on the measurement unit 1 side without sending the image of the kernel photographed by the measurement unit 1 to the server 7, depending on the communication environment (presence or absence of communication delay and its degree).

[0042] In addition, in the above-described embodiment, the measurement unit 1 is configured to be equipped with a quality discrimination means 13 capable of discriminating the quality of grains, but this is not necessarily limited to this, and it is also possible to provide the quality discrimination means 13 in the server 7.

[0043] Although several embodiments of the present invention have been described above, these are intended to facilitate understanding of the present invention and are not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and the present invention includes equivalents thereof. Furthermore, the components described in the claims and specification may be combined or omitted to the extent that at least part of the above-mentioned problems can be solved or at least part of the effects can be achieved.

[0044] REFERENCE SIGNS LIST 1 Measuring unit 11 Imaging means 12 Feature amount calculation means 13 Quality discrimination means 14 Transmission means 2 Grain 6 Terminal 7 Server 71 Storage means 72 Pseudo image generation means 73 Information transmission means 100 Grain inspection system

Claims

1. A grain inspection system comprising a measurement unit, a server, and a terminal, the server and the terminal being connected to each other via a communication line, further comprising: an imaging means for imaging grains; a feature calculation means for calculating feature quantities from the image of the grain captured by the imaging means; and a pseudo image generation means for generating a pseudo image of the grain based on the feature quantities, wherein the imaging means is provided in the measurement unit, the feature calculation means is provided in the measurement unit and / or the server, the pseudo image generation means is provided in the server, and the pseudo image of the grain generated by the pseudo image generation means is transmitted to the terminal via the communication line by an information transmission means of the server.

2. A grain inspection system according to claim 1, wherein the pseudo-image generating means generates the pseudo-image of the grain by artificial intelligence using a pseudo-image generation model.

3. A grain inspection system according to claim 1 or 2, characterized in that the feature amount is information relating to the shape and color of the grain.

4. A grain inspection system according to claim 1 or 2, characterized in that the measuring unit is an in-line sensor.

5. The grain inspection system according to claim 3, wherein the measuring unit is an in-line sensor.

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