Information processing system, method for controlling information processing system, and program

Abstract

To provide a mechanism that more accurately determines the growth state of agricultural and livestock products.SOLUTION: An information processing system comprises: management means that manages a plurality of picked-up images of agricultural and livestock products at a first point; acquisition means that acquires a picked-up image of agricultural and livestock products at a second point; and display means that acquires, from the images managed by the management means, an image similar to the image acquired by the acquisition means, and displays the image side by side with the image acquired by the acquisition means.SELECTED DRAWING: Figure 8

Description

Technical Field

[0001] The present invention relates to an information processing system, a control method for an information processing system, and a program, and more particularly to a technique for determining the growth state of agricultural and livestock products.

Background Art

[0002] Conventionally, the growth status of agricultural crops has been managed using cameras. Patent Document 1 discloses a technique for measuring the dimensions of a plant stem using a camera.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Disclosure of the Invention

Problems to be Solved by the Invention

[0004] Patent Document 1 describes a technique for measuring dimensions based on marks serving as dimension references.

[0005] However, although the growth state of the agricultural and livestock products in front of one's eyes can be known, there remains a problem of wanting to know how that state compares to other environments.

[0006] Therefore, an object of the present invention is to provide a mechanism for more accurately determining the growth state of agricultural and livestock products.

Means for Solving the Problems

[0007] An information processing system comprising: a management means for managing a plurality of images of agricultural and livestock products imaged at a first location; an acquisition means for acquiring an image of agricultural and livestock products imaged at a second location; and a display means for acquiring an image similar to the image acquired by the acquisition means from the images managed by the management means and displaying it side by side with the image acquired by the acquisition means.

Advantages of the Invention

[0008] According to the present invention, a mechanism for more accurately determining the growth state of agricultural and livestock products can be provided.

Brief Description of the Drawings

[0009]

Figure 1

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Figure 10

Embodiments for Carrying Out the Invention

[0010] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. The embodiments described below show an example of implementing the present invention specifically and are one of the specific embodiments of the configuration described in the claims.

[0011] FIG. 1 is a system configuration diagram showing an example of the configuration of the information processing system 100 of the present invention.

[0012] The left side of FIG. 1 shows a control unit, and depicts a system that is communicably connected via a network such as a router and the Internet to a network camera 200, which is an image acquisition unit, and a mobile terminal 201.

[0013] The server device 101 acquires data managed by the database server 102 from the database server, analyzes the image data included in the acquired data, and identifies the growth status of the agricultural crops, for example, whether fruits or flowers are present, the ratio of the leaf area in the image, and the like.

[0014] The database server 102 is a server that manages, as a database, data for reading and writing data such as that shown in FIGS. 5, 6, 7, and 10, and transmits the data managed by the database server 102 to the server device 101 in accordance with a request from the server device 101.

[0015] The right side of FIG. 1 shows an image acquisition unit, and depicts a situation where ridges 300 of strawberries or the like growing in a field are photographed using a network camera 200 or a mobile terminal 201 fixedly installed on a ceiling of a greenhouse or a pillar installed in a field.

[0016] Note that this image acquisition unit exists for each field such as a farmland or a greenhouse, and there may be two or more image acquisition units for one farmer or agricultural management organization. Even for an enterprise engaged in agricultural operations, it may have image acquisition units of a plurality of fields (image acquisition units) nationwide, and a system in which those and the control unit are communicably connected via a network.

[0017] In this embodiment, strawberries are assumed as the agricultural crops for explanation, but the present invention is not limited to strawberries, and other agricultural crops such as bell peppers, tomatoes, cucumbers, cabbages, lettuces, radishes, apples, bananas, kiwis, rice, and wheat may also be used.

[0018] In addition, in this embodiment, although agricultural crops are used, the present invention is not limited to agricultural crops, and livestock products such as cattle, pigs, and chickens may also be used. In that case, the yield prediction will be to predict the number of shipments or the amount of eggs shipped, etc.

[0019] Then, the image data captured by the network camera 200 is transmitted to the database server 102 via the router and managed by the database server 102 in association with the preset ID that captured the image data (an example of the management means in the present invention for managing the image data captured by the imaging means for imaging agricultural crops with the set preset in association with the preset at the time when the image data was captured).

[0020] Also, the image data captured by the mobile terminal 201 is managed by the database server 102 in association with the preset ID linked to the marker (e.g., QR code (registered trademark)) shown in the image (an example of the management means in the present invention for managing the image data captured by the imaging means for imaging agricultural crops with the set preset in association with the preset at the time when the image data was captured).

[0021] Note that in this embodiment, the server device 101 and the database server 102 are separate devices, but as another embodiment, the server device 101 and the database server 102 may be one device.

[0022] Also, the network camera 200 and the mobile terminal 201 may be a digital camera, a wearable camera, an infrared camera, a camera attached to a vehicle, an aircraft, a drone, etc. Hereinafter, with reference to FIG. 2, the hardware configuration applicable to each information processing device constituting the information processing system 100 shown in FIG. 1 will be described.

[0023] FIG. 2 is a block diagram showing the hardware configuration applicable to each information processing device constituting the information processing system 100 shown in FIG. 1.

[0024] In FIG. 2, 201 is a CPU that comprehensively controls each device and controller connected to the system bus 204. Also, in the ROM 202 or the external memory 211, there are stored a BIOS (Basic Input / Output System) which is a control program of the CPU 201, an operating system program (hereinafter referred to as OS), and various programs described later that are necessary to realize the functions executed by each PC, etc.

[0025] 203 is a RAM that functions as the main memory, work area, etc. of the CPU 201. The CPU 201 loads programs and the like necessary for the execution of processing from the ROM 202 or the external memory 211 to the RAM 203, and realizes various operations by executing the loaded programs.

[0026] 205 is an input controller that controls inputs from a keyboard (KB) 210, a pointing device such as a mouse (not shown), etc.

[0027] 206 is a video controller that controls the display on a display device such as the display 210.

[0028] 207 is a memory controller that controls access to an external storage device (hard disk (HD)) that stores various data, a flexible disk (FD), or an external memory 211 such as a CompactFlash (registered trademark) memory connected via an adapter to a PCMCIA card slot. 207 controls access to an external memory 211 such as a CompactFlash (registered trademark) memory connected via an adapter to a PCMCIA card slot, an external storage device (hard disk (HD)) that stores various data, or a flexible disk (FD).

[0029] 208 is a communication I / F controller that controls the reception of image data from an external PC 213 via a network (TCP / IP). 209 is an image I / F controller that controls the reception of image data from a camera 214 via an image transfer cable (USB, Ethernet, Camera Link, etc.).

[0030] The various programs described below for implementing the present invention are recorded in the RAM 203 and executed by the CPU 201.

[0031] Also, the image data used when executing the above program is stored in the ROM 202, the external memory 211, the external PC 213, and the camera 214 according to the application, and is stored in the RAM 203 via various controllers when the program is executed.

[0032] FIG. 3 is an example of a block diagram showing the software configuration of an embodiment of the present invention. The information processing system 100 includes the following functional units.

[0033] The management unit 301 is a functional unit that manages the growth characteristic amounts for each variety of agricultural and livestock products.

[0034] The reception unit 302 is a functional unit that receives the designation of the variety of agricultural and livestock products.

[0035] The acquisition unit 303 is a functional unit that acquires the image of the agricultural and livestock products that have been imaged.

[0036] The prediction unit 304 is a functional unit that predicts the yield of the agricultural and livestock products based on a determination as to whether the characteristic amounts of the image of the agricultural and livestock products approximate the growth characteristic amounts corresponding to the variety received by the reception unit 302.

[0037] The prediction unit 304 is a functional unit that predicts the yield of the agricultural and livestock products based on a determination as to whether the characteristic amounts of the image of the agricultural and livestock products approximate the growth characteristic amounts corresponding to the variety received by the reception unit 302 when the reliability of the prediction using the learning model is less than a predetermined value.

[0038] The acquisition unit 303 is a functional unit that acquires the image of the marker that has been imaged.

[0039] The control unit 305 is a functional unit that controls to superimpose and output on the image acquired by the acquisition unit 303 a grid deformed based on the shape of the image of the marker acquired by the acquisition unit 303.

[0040] The control unit 305 is a functional unit that controls to superimpose and output on the image acquired by the acquisition unit 303 a grid subjected to at least one of inclination or deformation according to at least one of the orientation, inclination, or deformation of the image of the marker acquired by the acquisition unit 303.

[0041] The calculation unit 306 is a functional unit that calculates the size or length of an object in the image.

[0042] The calculation unit 306 is a functional unit that calculates the size or length of an object in the image based on the grid.

[0043] The management unit 301 is a functional unit that manages a plurality of images of agricultural and livestock products imaged at the first location.

[0044] The acquisition unit 303 is a functional unit that acquires an image of agricultural and livestock products imaged at the second location.

[0045] The display unit 307 is a functional unit that acquires an image similar to the image acquired by the acquisition unit 303 from the images managed by the management unit 301 and displays it side by side with the image acquired by the acquisition unit 303.

[0046] The display unit 307 is a functional unit that identifies and displays the differences between an image similar to the image acquired by the acquisition unit 303 and the image acquired by the acquisition unit 303.

[0047] The display unit 307 is a functional unit that displays images similar to the image acquired by the acquisition unit 303 in an order based on the similarity.

[0048] The display unit 307 is a functional unit that displays, in the order of similarity, images similar to the image acquired by the acquisition unit 303 based on the similarity criteria for which selection from the user has been received. This concludes the description of FIG. 3.

[0049] The flowchart of FIG. 4 will be described.

[0050] The following processing is assumed to be performed by the CPU 201 of any one of the server device 101, the network camera 200, or the mobile terminal 201 that constitutes the information processing system 100.

[0051] In S401, the mobile terminal 201 receives a selection of the variety of agricultural products to be photographed from the user, and transmits the identification information of the selected variety to the server device 101. Here, it is assumed that the selection of "Variety A" has been received.

[0052] That is, this step shows an example of a process for receiving the designation of the variety of agricultural and livestock products.

[0053] In S402, the server device 101 acquires an image of the agricultural products photographed by the network camera 200 or the mobile terminal 201. Here, it is assumed that photos such as 810 in FIG. 8, 910 and 930 in FIG. 9 have been acquired.

[0054] That is, this step shows an example of a process for acquiring an image of the agricultural and livestock products that has been imaged.

[0055] That is, this step shows an example of a process for acquiring an image of the agricultural and livestock products at the second location that has been imaged.

[0056] That is, this step shows an example of a process for acquiring an image of the marker that has been imaged.

[0057] In S403, the server device 101 calculates the feature amount of the acquired image.

[0058] In S404, the server device 101 acquires, from the database server 102, images of other farms that are similar to the calculated image feature amounts.

[0059] The database server 102 stores and manages images of agricultural and livestock products at other locations together with environmental information such as the date and time of shooting, variety, temperature, and humidity.

[0060] That is, the database server 102 shows an example of a means for managing a plurality of images of agricultural and livestock products imaged at the first location.

[0061] Here, similar images refer to images in which at least one of the variety, number of growth days, growth environment, number of leaves of agricultural crops, leaf length, leaf size, leaf density, leaf pattern, leaf shape, leaf color, stem length, stem thickness, stem density, stem pattern, stem shape, stem color, number of flowers, flower length, flower size, flower density, flower pattern, flower shape, flower color, flower blooming rate, number of fruits, fruit length, fruit size, fruit density, fruit pattern, fruit shape, fruit color, number of seeds, seed length, seed size, seed density, seed pattern, seed shape, and seed color of agricultural and livestock products included in the image; the length, size, body temperature, pulse rate, heart rate, stress level of all or part of the body of livestock products; and the soil color, soil particle size, soil particle shape, water color, water temperature, water components, temperature, humidity, image composition, hue, contrast, brightness, and shooting date and time are the same or similar.

[0062] In S405, the server device 101 transmits the image acquired in S404 to the mobile terminal 201, and the mobile terminal 201 displays the image acquired in S402 and the image acquired in S404 side by side.

[0063] That is, this step shows an example of a process of acquiring an image similar to the image acquired from the managed image and displaying it side by side with the acquired image.

[0064] Specifically, as shown in FIG. 8, the image 810 acquired in S402 and the images 821, 822, and 823 acquired in S404 are displayed side by side.

[0065] Also, at that time, the differences between the image 810 and the images 821, 822, and 823 may be surrounded by round or square frames, blinked, or identified and displayed.

[0066] That is, this step is a step showing an example of a process of identifying and displaying the differences between an image similar to the acquired image and the acquired image.

[0067] The images (821, 822, 823) similar to the captured image (810) are arranged and displayed in the order of similarity. However, since there are various criteria for determining similarity as described above, the user may be able to select such criteria (not shown). Specifically, the user can select criteria for arranging images such as "same variety", "color of the fruit", and "composition of the image".

[0068] That is, this step is a step showing an example of a process of displaying images similar to the acquired image in an order based on the degree of similarity.

[0069] That is, this step is a step showing an example of a process of displaying images similar to the acquired image in an order of similarity based on the similarity criteria accepted from the user.

[0070] As described above, it becomes easier to compare the growth states of a plurality of similar farms, and the growth state of agricultural and livestock products can be determined more accurately.

[0071] Also, as a result, the manager of a farm where growth has not progressed can improve the growth method, or the supervisor can give instructions to a farm where growth has not progressed.

[0072] In the present embodiment, the server device 101 transmits the image acquired in S404 to the mobile terminal 201. However, the method is not limited to this. For example, the server device 101 may generate a page as shown in FIG. 8 and transmit the page information (e.g., URL, HTML, etc.) to the mobile terminal 201. Alternatively, the mobile terminal 201 may directly acquire images of other farms from the database server 102 instead of the server device 101.

[0073] The following processing is assumed to be performed by the mobile terminal 201, but it may also be performed by the server device 101 or by the cooperation of the mobile terminal 201 and the server device 101.

[0074] In S406, the mobile terminal 201 detects a marker (e.g., QR code (registered trademark)) (911 or 931 in FIG. 9) included in the image acquired in S402.

[0075] In S407, the mobile terminal 201 analyzes the size, inclination, and deformation of the marker detected in S406.

[0076] Specifically, the size is calculated by detecting the four corners of the marker, and the degree of inclination from the horizontal is calculated. Also, the degree of deformation of the marks at the four corners (here, double rectangles) is calculated.

[0077] For example, assuming that the marker in FIG. 9 is actually a square with a side length of 8 cm, it can be determined from the analysis that the marker 911 in FIG. 9 is inclined but maintains the shape of a square and has no deformation.

[0078] Also, the marker 931 in FIG. 9 is inclined about 10 degrees to the left. By comparing the sizes of the marks at the four corners, it can be seen that the mark in the foreground is larger and the mark in the background is smaller, indicating that it is a perspective photo. This can be determined from the analysis.

[0079] Regarding these analysis methods, the calculation formulas for determination as described above are known techniques, and there are various methods, so detailed explanations are omitted.

[0080] In S408, based on the analysis in S407, the mobile terminal 201 superimposes and displays a grid on the image.

[0081] Specifically, when displaying a grid at 10 cm intervals, as shown by the dotted lines 920 and 940 in FIG. 9, the interval of the grid (1.25 times the size of the marker) is determined in proportion to the size of the marker (one side is 8 cm) by the analysis in S407, and the orientation of the grid is determined by aligning it with the inclination of the marker (parallel).

[0082] Also, based on the degree of deformation of the marker (the ratio and positional relationship of the sizes of the marks at the four corners), it is determined whether to display the grid in parallel or to express the depth using a perspective method in which the interval becomes narrower towards the back.

[0083] The calculation formula for this grid deformation process is a known technique, and since there are various methods, detailed description is omitted.

[0084] That is, this step shows an example of a process of controlling to superimpose and output a grid deformed based on the shape of the acquired marker image on the image acquired by the acquisition means.

[0085] That is, this step shows an example of a process of controlling to superimpose and output a grid that has been subjected to at least one of inclination or deformation on the image acquired by the acquisition means according to at least one of the orientation, inclination, or deformation of the acquired marker image.

[0086] As described above, since the grid is displayed in accordance with the inclination of the image, it becomes possible to more accurately determine the growth state of agricultural and livestock products.

[0087] Also, in S408, based on the displayed grid, the mobile terminal 201 displays the length of the stem (e.g., the display of "16.7 cm" at 920 and the display of "40.8 cm" at 940 in FIG. 9). Specifically, the detected stem is linearly deformed and compared with the length of the grid to calculate and display the length of the stem. At this time, the target stem is identified and displayed with a thick line.

[0088] The calculation formula for obtaining the length of this stem is a known technique, and there are various methods, so detailed explanations are omitted.

[0089] That is, this step is a step showing an example of a process for calculating the size or length of an object in an image.

[0090] That is, this step is a step showing an example of a process for calculating the size or length of an object in an image based on a grid.

[0091] In this embodiment, it is assumed that the length of the longest stem among the stems in the image is displayed, but it is not limited to this method. It is also possible to display the length and size of all stems, leaves, buds, flowers, fruits, etc., or to display the length and size of the stems, leaves, buds, flowers, fruits, etc. for which instructions have been received from the user. Other methods may also be used.

[0092] As described above, it becomes possible to more accurately determine the growth state of agricultural and livestock products.

[0093] In S409, the mobile terminal 201 identifies the stems, leaves, buds, flowers, and fruits of the agricultural products included in the image acquired in S402.

[0094] In S410, the mobile terminal 201 repeats the processes of S411 to S415 as many times as the number of stems, leaves, buds, flowers, and fruits identified in S402.

[0095] In S411, the mobile terminal 201 analyzes the growth characteristic quantities (size, length, color, density, shape, etc.) of the stems, leaves, buds, flowers, and fruits to be processed.

[0096] In S412, based on the growth feature amounts analyzed in S411, the mobile terminal 201 classifies the growth stage using an AI model generated in advance.

[0097] For example, in the case of strawberry fruits, based on the color and size, it is classified into which growth stage among {green ripening stage / white ripening stage / red ripening stage / harvest stage} it belongs to.

[0098] In S413, the mobile terminal 201 determines whether the reliability of the growth stage classified in S412 is equal to or higher than a predetermined value, that is, whether it is sufficiently reliable. If it is equal to or higher than the predetermined value, it returns to S410. If it is less than the predetermined value, it proceeds to S414.

[0099] In S414, the mobile terminal 201 determines the closest growth stage classification among the feature amounts of the variety selected in S401 in the variety master (Figure 10) stored in the database server 102 or the mobile terminal 201.

[0100] If the closest growth stage classification is the same as the growth stage by AI, it returns to S410. If it is different, it proceeds to S415.

[0101] Here, Figure 10 is a master of color and size for each variety, and statistical amounts of the feature amounts are registered in the master.

[0102] The statistical amounts are, in order from the left, "minimum value, average value, maximum value, standard deviation", and any of these values may be used in the determination in S414.

[0103] That is, Figure 10 is a diagram showing an example of a management means for managing the growth feature amounts for each variety of agricultural and livestock products.

[0104] Specifically, if the AI determines that the growth stage of the strawberry fruit of "variety A" included in the image is the "white ripening stage", but the reliability is lower than the predetermined value, and as a result of determining which growth stage color of "variety A" the color of the fruit approximates using the master in Figure 10, it is determined that it most approximates the "red ripening stage" rather than the "white ripening stage", since it is different from the growth stage by AI, it proceeds to S415.

[0105] In addition, in this embodiment, a master of color and size for each variety is used, but it is not limited to color and size, and a master such as length, density, shape, etc. may be used instead of color and size.

[0106] In S415, the mobile terminal 201 updates (overwrites) the growth stage classified by the AI in S412 with the growth stage determined in S414.

[0107] In S416, the mobile terminal 201 predicts the yield based on the growth stages of the stems, leaves, buds, flowers, and fruits classified in S412 or S415, and displays the predicted yield on the screen (707 in FIG. 7).

[0108] That is, this step shows an example of a process of predicting the yield of the agricultural and livestock product based on the determination of whether the feature amount of the image of the agricultural and livestock product approximates the growth feature amount corresponding to the received variety.

[0109] Thus, the description of FIG. 4 ends.

[0110] As described above, when the reliability of the growth stage determination by AI is low, by using the master for each variety, it becomes possible to perform a more accurate yield prediction without generating a learning model for each variety.

[0111] Generating a learning model for each variety is very costly. Therefore, farmers and agricultural corporations that grow various varieties can perform a low-cost and highly accurate yield prediction.

[0112] In addition, in this embodiment, an agricultural product such as strawberry is taken as an example, but it is not limited to agricultural products, and livestock products such as cows, pigs, and chickens may also be used. In that case, the variety in S401 is the variety of the livestock product, the part specified in S409 is the whole body or a part of the body such as the legs, head, and face, the growth stage in S412 is divided into "birth stage, juvenile stage, growth stage, adult stage", etc., and the yield prediction in S416 is to predict the number of shipments or the amount of eggs shipped.

[0113] Also, for reference, FIGS. 5 to 7 used in the yield prediction process will be described.

[0114] FIG. 5 is an example of a past yield performance table stored in the external memory 211 of the database server 102.

[0115] The past yield performance table is composed of a date 501 and a total field yield 502.

[0116] The total field yield 502 is estimated as the number of boxes filled with strawberries to be shipped. For example, considering 1 box = 2 Kg, the number of boxes filled with strawberries shipped daily is estimated.

[0117] FIG. 6 is an example of an image analysis result table stored in the external memory 211 of the database server 102.

[0118] The image analysis result table is composed of a preset ID 601, a date 602, a number of highly mature fruits 603, and an estimated yield performance 604. The preset ID 601 indicates the location of the ridge 300 photographed by the network camera 200 or the mobile terminal 201. Note that the image data used for the analysis is also managed in association with the preset ID 601.

[0119] The number of highly mature fruits 603 represents the number of fruits that have matured to a harvestable state. The number of highly mature fruits 603 can be obtained by detecting highly mature fruits from the target image using a known technique such as "Faster R-CNN". The estimated yield performance represents an estimated value of the number of fruits harvested on a certain day.

[0120] The estimated yield performance 604 can be obtained by using the difference from the previous day of the number of highly mature fruits 603.

[0121] For example, when obtaining the first estimated yield result 604 of preset ID 601 with date 602 being January 2nd, the difference between the value "20" of the number of highly mature fruits 603 with date 602 being January 1st and the value "5" of the number of highly mature fruits 603 with date 602 being January 2nd is taken, and it is estimated to be "15".

[0122] Figure 7 shows a method for calculating the error obtained by regression analysis.

[0123] The estimated yield result 702 is the value obtained by summing up the estimated yield results 604 for each date 602 in the combination of preset ID 601.

[0124] To obtain the correlation coefficients α and β, this estimated yield result 702 and the total field yield 703 are plotted on the XY coordinates. The correlation coefficients α and β are obtained from the plotted XY coordinates, and Y = αX + β is calculated.

[0125] Using this regression equation, the server device 101 calculates the regression estimated value 704 of the entire field from the estimated yield result. As described above, the yield is predicted using FIGS. 5 to 7.

[0126] Regarding yield prediction, there are various other known techniques in addition to the above, and the present invention is not limited to the above yield prediction method.

[0127] Needless to say, the object of the present invention can also be achieved by supplying a recording medium recording a program that realizes the functions of the above-described embodiments to a system or device, and causing a computer (or CPU or MPU) of the system or device to read and execute the program stored in the recording medium.

[0128] In this case, the program itself read from the recording medium realizes the novel functions of the present invention, and the recording medium recording the program constitutes the present invention.

[0129] As recording media for supplying a program, for example, flexible disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, DVD-ROMs, magnetic tapes, non-volatile memory cards, ROMs, EEPROMs, silicon disks, etc. can be used.

[0130] Further, by executing the program read by the computer, not only the functions of the above-described embodiments are realized, but also based on the instructions of the program, the OS (operating system) etc. running on the computer performs part or all of the actual processing, It goes without saying that the case where the functions of the above-described embodiments are realized by the processing is also included.

[0131] Furthermore, after the program read from the recording medium is written into the memory provided in the function expansion board inserted into the computer or the function expansion unit connected to the computer, based on the instructions of the program code, the CPU etc. provided in the function expansion board or the function expansion unit performs part or all of the actual processing, It goes without saying that the case where the functions of the above-described embodiments are realized by the processing is also included.

[0132] Also, the present invention may be applied to a system composed of a plurality of devices or to an apparatus composed of a single device. Needless to say, the present invention is also applicable when achieved by supplying a program to a system or an apparatus. In this case, by reading the recording medium storing the program for achieving the present invention into the system or the apparatus, the system or the apparatus can enjoy the effects of the present invention.

[0133] The form of the above program may be in the form of object code, program code executed by an interpreter, script data supplied to the OS (operating system), etc.

[0134] Furthermore, by downloading and reading a program for achieving the present invention from a server, a database, etc. on a network by means of a communication program, the system or device can enjoy the effects of the present invention. Note that all configurations combining the above-described respective embodiments and their modifications are also included in the present invention.

Description of Reference Numerals

[0135] 100 Information processing system 101 Server device 102 Database server 200 Network camera 201 Mobile terminal

Claims

1. Management means for managing a plurality of images of agricultural and livestock products taken at a first location, Acquisition means for acquiring an image of agricultural and livestock products taken at a second location, Display means for acquiring an image similar to the image acquired by the acquisition means from the images managed by the management means and displaying it side by side with the image acquired by the acquisition means An information processing system characterized by comprising.

2. The display means, Identifying and displaying the differences between the image similar to the image acquired by the acquisition means and the image acquired by the acquisition means The information processing system according to claim 1, characterized by the above.

3. The display means, Displaying the images similar to the image acquired by the acquisition means in an order based on the similarity degree The information processing system according to claim 1, characterized by the above.

4. The display means, Displaying the images similar to the image acquired by the acquisition means in an order of similarity degree based on the similarity criteria for which selection is accepted from the user The information processing system according to claim 1, characterized by the above.

5. The similarity means, The variety, growth days, growth environment of the agricultural and livestock products included in the image, The number of leaves, leaf length, leaf size, leaf density, leaf pattern, leaf shape, leaf color of the crop, Stem length, stem thickness, stem density, stem pattern, stem shape, stem color, Number of flowers, flower length, flower size, flower density, flower pattern, flower shape, flower color, flower blooming rate, Number of fruits, fruit length, fruit size, fruit density, fruit pattern, fruit shape, fruit color, Number of seeds, seed length, seed size, seed density, seed pattern, seed shape and seed color, The length or size of all or part of the body of the livestock product, body temperature, pulse rate, heart rate, stress level, and Soil color, soil particle size, soil particle shape, water color, water temperature, water components, air temperature, humidity, At least one of the composition, hue, contrast, brightness and shooting date of the image Is the same or similar The information processing system according to claim 1, characterized by the above.

6. A management step of managing a plurality of images of agricultural and livestock products taken at a first location, An acquisition step of acquiring an image of agricultural and livestock products taken at a second location, A display step of acquiring an image similar to the image acquired by the acquisition step from the images managed by the management step and displaying it side by side with the image acquired by the acquisition step A control method for an information processing system characterized by comprising.

7. At least one computer, A program for causing the at least one computer to function as each means of the information processing system according to any one of claims 1 to 5.

Images