JP2023132194A - Determination system, determination method and determination program for food - Google Patents

Abstract

To provide a determination system for food, which appropriately determines whether the target food belongs to a period before or after its best to eat.SOLUTION: According to an embodiment, a food determination system comprises a determination unit and a notification control unit. The determination unit determines whether the target food belongs to before or after the best time of eating based on the input information entered for the target food and the relationship between the degree of similarity and the chromaticity with respect to the best time of eating for the item to which the target food belongs. The notification control unit notifies the notification information on the target food including the determination result regarding the target food in the determination unit.SELECTED DRAWING: Figure 5

Description

Embodiments of the present invention relate to a food determination system, determination method, and determination program.

Regarding the target food to be determined, the degree of similarity to the ripe state is determined based on, for example, human sensation. However, when determining the degree of similarity of a target food to its ripe state based on human senses, the knowledge and experience of the person making the determination is required. For this reason, when determining the above-mentioned similarity of target foods based on human senses, opinions may differ from person to person, or there may be a lack of knowledge and experience regarding the target food items, etc. Appropriate judgment may not be made. Therefore, there is a need to appropriately determine the degree of similarity of a target food to be determined relative to its ripe state without relying on human senses.

In addition, for fruits and the like, the time when they are harvested is not necessarily the best time to eat them, but the best time to eat them is when a certain amount of time has elapsed since the time of harvest. Therefore, in addition to the degree of similarity with respect to the ripe state of the target food to be determined, it is required to appropriately determine whether the target food is before or after the ripe state.

JP 2016-85117 Publication

The problem to be solved by the present invention is to provide a food determination system, a determination method, and a determination program that appropriately determine whether a target food is in a period before or after its ripeness.

According to the embodiment, a food determination system includes a determination unit and a notification control unit. The determination unit determines whether the target food is before or after its ripening time, based on the input information input about the target food and the relationship between the degree of similarity and chromaticity with respect to the ripeness state of the item to which the target food belongs. judge. The notification control unit notifies notification information about the target food including the determination result of the target food by the determination unit.

According to the present invention, it is possible to provide a food determination system, a determination method, and a determination program that appropriately determine whether a target food is in a period before or after its ripeness.

FIG. 1 is a block diagram schematically showing a determination system according to a first embodiment. FIG. 2 is a schematic diagram illustrating an example of the relationship between the degree of similarity and chromaticity with respect to the ripe state of one item, which is included in the relational data in the first embodiment. FIG. 3 is a schematic diagram showing an example of the relationship between the degree of similarity and chromaticity with respect to the ripe state of one item other than the item shown in FIG. 2, which is included in the relational data in the first embodiment. It is. FIG. 4 is a schematic diagram showing an example of relational data stored in the data storage unit in the first embodiment. FIG. 5 is a flowchart schematically showing an example of a determination process regarding a target food, which is performed by the determination unit in the first embodiment. FIG. 6 is a schematic diagram showing a plurality of examples of determination results regarding target foods by the determination unit in the first embodiment. FIG. 7 is a flowchart schematically showing an example of a process of correcting the relationship between similarity and chromaticity for an item to which a target food belongs, which is performed by the correction unit in the first embodiment. FIG. 8 is a schematic diagram illustrating an example of the correction result by the correction unit of the relationship between similarity and chromaticity for the items shown in FIG. 2 in the first embodiment. FIG. 9 is a schematic diagram showing another example of the correction result by the correction unit of the relationship between similarity and chromaticity for the items shown in FIG. 2, different from FIG. 8, in the first embodiment. FIG. 10 is a block diagram schematically showing a determination system according to a modification.

The food determination system (1) of the embodiment includes a determination section (15) and a notification control section (17). The determination unit (15) determines whether the target food is before or after the best time to eat based on the input information input about the target food and the relationship between the degree of similarity (ε) and chromaticity with respect to the best time to eat state of the item to which the target food belongs. Determine which of the following. The notification control unit (17) causes notification information about the target food including the determination result about the target food by the determination unit (15) to be notified. Thereby, whether the target food is ready to eat or not can be appropriately determined without relying on human senses. Further, the user of the determination system (1) can appropriately grasp information regarding the target food, including whether the target food is before or after its best to eat.

In the determination system (1) of the embodiment, the determination unit (15) calculates chromaticity information of the target food using input information. The determination unit (15) then determines the target food based on the calculated chromaticity information of the target food and the relationship between the degree of similarity (ε) and chromaticity for the item to which the target food belongs. Thereby, it is more appropriately determined whether the target food is before or after the best time to eat it.

The determination system (1) of the embodiment further includes a correction unit (16), and the correction unit (16) calculates the degree of similarity () of the item to which the target food belongs based on the environment in which the input information about the target food is acquired. Correct the relationship between ε) and chromaticity. The determination unit (15) determines the target food based on the relationship between the degree of similarity (ε) and the chromaticity, which has been corrected in accordance with the environment in which the input information was acquired. As a result, it is appropriately determined whether the target food is before or after its best time to eat, depending on the environment in which the input information about the target food has been acquired.

In the determination system (1) of the embodiment, the correction unit (16) determines the color when the degree of similarity (ε) reaches each of one or more set values in the environment in which the input information is acquired regarding the item to which the target food belongs. Calculate degree information. Then, the correction unit (16) calculates the similarity (ε) and chromaticity of the item to which the target food belongs based on the relationship between one or more setting values regarding the similarity (ε) and the calculated chromaticity information. Correct the relationship between As a result, the relationship between the degree of similarity (ε) and the chromaticity of the item to which the target food belongs is appropriately corrected in accordance with the environment in which the input information about the target food was acquired.

The determination method for food according to the embodiment is based on the input information input about the target food and the relationship between the degree of similarity (ε) and chromaticity with respect to the ripe state of the item to which the target food belongs. Determine whether the food is ready to eat or not. In the determination method, notification information regarding the target food including the determination result for the target food is notified. Thereby, whether the target food is ready to eat or not can be appropriately determined without relying on human senses. In addition, people can appropriately grasp information regarding the target food, including whether the target food is before or after its best to eat.

The determination program for food according to the embodiment includes input information inputted to the processing device (10; 10, 100) about the target food, and the degree of similarity (ε) and color of the item to which the target food belongs to the ripe state. It is determined whether the target food is before or after its best to eat based on the relationship with the degree of consumption. Then, the determination program causes the processing device (10; 10, 100) to notify notification information about the target food including the determination result about the target food. Thereby, whether the target food is ready to eat or not can be appropriately determined without relying on human senses. In addition, people can appropriately grasp information regarding the target food, including whether the target food is before or after its best to eat.

Hereinafter, embodiments will be described with reference to the drawings.

(First embodiment)
First, a first embodiment will be described as an example of the embodiment. FIG. 1 shows a determination system 1 according to a first embodiment. As shown in FIG. 1, the determination system 1 includes a processing device (analysis device) 10. The processing device 10 includes a processing execution section 11, a data storage section 12, a communication interface 13, and a user interface 14. The processing execution section 11 includes a determination section 15 , a correction section 16 , and a notification control section 17 , and each of the determination section 15 , the correction section 16 , and the notification control section 17 performs a part of the processing performed by the processing execution section 11 . .

In one example, the processing device 10 is, for example, a terminal device such as a smartphone or a computer, and includes a processor or an integrated circuit, and a storage medium such as a memory. In the processing device 10, a processor or an integrated circuit functions as a processing execution unit 11, and a storage medium functions as a data storage unit 12. The processor or integrated circuit of the processing device 10 includes any one of a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), a microcomputer, an FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor), and the like. The processing device 10 may include only one integrated circuit or the like, or may include a plurality of integrated circuits or the like. Furthermore, the processing device 10 may include only one storage medium, or may include multiple storage media.

In the processing device 10, a processor or the like executes a program or the like stored in a storage medium or the like, so that the processing execution unit 11 performs the processing described below. In one example, a program executed by a processor or the like in the processing device 10 may be stored in a computer (server) connected via a network such as the Internet, or a server in a cloud environment. In this case, the processor of the processing device 10 downloads the program via the network. For example, if the processing device 10 is a terminal device such as a smartphone, by downloading a dedicated application via a network, the processor of the processing device 10 or the like can execute the processing described below by the processing execution unit 11.

Further, in one example, the processing device 10 is configured from a server in a cloud environment. The infrastructure of the cloud environment is composed of virtual processors such as virtual CPUs and cloud memory. In a server in a cloud environment serving as the processing device 10, a virtual processor or the like functions as the processing execution unit 11, and the processing executed by the processing execution unit 11, which will be described later, is performed by the virtual processor or the like. The cloud memory then functions as the data storage unit 12.

In the determination system 1, the processing device 10 may communicate with other processing devices via the communication interface 13. Thereby, the processing device 10 can receive information from other processing devices and can transmit information to other processing devices. A user of the processing device 10 or the like can input operations on the user interface 14 . The user interface 14 of the processing device 10 can notify the user of the processing device 10 of information. In the user interface 14, information is notified by either screen display or audio transmission, for example. In one example, user interface 14 includes a touch panel. Then, operations can be input on the touch panel, and information and the like are displayed on the touch panel. Note that the user interface 14 may be provided separately from the processing device 10.

In this embodiment, a determination is made using the determination system 1 regarding the target food to be determined. The item to which the target food belongs is not particularly limited as long as it is a food that humans and animals (including terrestrial and aquatic animals) ingest orally. Here, the food items include, for example, fresh items, such as vegetable items, meat items, fruit items, and the like. Specifically, for example, vegetable items include “green peppers,” “tomatoes,” and “cabbage,” meat items include “chicken,” “pork,” and fruit items include “apples,” and “cabbage.” Examples include "banana" and the like. In addition, food items include, for example, processed items, such as dried/dried/smoked items, alcoholic beverage items, dairy products, and fermented food items. Specifically, for example, dried/dried/smoked items include "fish," "meat," and "fruit" (more specific items such as "horse mackerel" are also acceptable), while alcoholic beverages include "wine." Examples of dairy products include cheese and yogurt, and examples of fermented food items include natto. The processing execution unit 11 acquires input information input about the target food. The input information regarding the target food includes, for example, an image of the target food. In addition, the input information regarding the target food may include color data, size data, etc. of the target food, and the color data of the target food may be, for example, the measurement result of the spectroscopic spectrum of the target food. good.

The input information regarding the target food may be input to the processing device 10 by operating the user interface 14 or the like, and may be input to the processing device 10 by other processing devices or devices other than the processing device (for example, smartphones, camera devices with communication functions, and computers). etc.) may be received by the processing device 10 via the communication interface 13. In one example, the processing device 10 is a terminal device such as a smartphone, and includes a photographing unit such as a camera. Then, the processing execution unit 11 acquires the image of the target food photographed by the photographing unit as input information regarding the target food.

The data storage unit 12 stores relational data, and in the relational data, for each of a plurality of food items, that is, for each item, the relationship between the degree of similarity to the ripe state and the chromaticity of the food. is shown. In one example, for each of one or more items of vegetables, one or more items of meat, and one or more items of fruit, the relationship between the aforementioned similarity and chromaticity is shown in the relational data. . In other words, the data storage unit 12 stores, for each item, relational data that associates similarity with respect to the ripeness state and food chromaticity (chromaticity coordinates). Note that the data storage unit 12 may store relational data in which one degree of similarity is associated with food chromaticity, or relational data in which multiple degrees of similarity are associated with food chromaticity for one item. may be remembered.

In the following explanation, the term "similarity" will be used, such as "similarity to the ripe state", but instead of "similarity", "degree of match", "degree of approximation", and "closeness" will be used. You may also use terms such as. In other words, a parameter indicating how much the current state of the food is similar to (matches) the state of the food when it is ready to eat (best state), such as "similarity to the state when it is ready to eat", is It is sufficient if it is stipulated. The “similarity to the ripe state” becomes higher as the food is closer to the ripe state. Note that it may be a parameter that indicates how much the current state deviates from the best state (best state), such as "degree of deviation from the best state." In this case, the "degree of deviation from the ripe state" becomes smaller as the food approaches the ripe state. Moreover, "the best time to eat" refers to the most appropriate time to eat food. In the following description, "similarity ε" is defined as a parameter of "similarity to the ripe state".

In the relational data, the chromaticity of each of the plurality of items is indicated by chromaticity coordinates in a predetermined color space. Therefore, in the relational data, the relationship between the similarity ε and the chromaticity coordinates in a predetermined chromaticity diagram is shown as the relationship between the similarity ε and the chromaticity for each of the plurality of items. In one example, for each of a plurality of items, the relationship between the degree of similarity ε with respect to the ripe state and the chromaticity coordinates in the xy chromaticity diagram or the Lab chromaticity diagram is shown in the relational data.

In addition, in relational data, for each item, as a relationship between similarity ε and chromaticity coordinates in a predetermined chromaticity diagram, changes in chromaticity coordinates in a predetermined chromaticity diagram corresponding to changes in similarity ε Information about is shown. In one example, information about a change in chromaticity coordinates in a predetermined chromaticity diagram that corresponds to a change in similarity ε is indicated by a locus in the predetermined chromaticity diagram. Here, a "trajectory that provides information about a change in chromaticity coordinates in a predetermined chromaticity diagram corresponding to a change in similarity ε" is referred to as a "trajectory T." In the relational data, formula information of the trajectory T is shown for each item as a relationship between the degree of similarity ε and the chromaticity coordinates in a predetermined chromaticity diagram. Therefore, in relational data, for one item, one degree of similarity and food chromaticity are associated as equation information of the trajectory T (Nth-order equation of x in the xy chromaticity coordinate system. N: natural number). Alternatively, as a plurality of coordinate data along which the trajectory T passes, relational data in which a plurality of degrees of similarity and food chromaticity are associated may be stored.

In the relational data, each trajectory T has a starting point and an ending point. Then, for each of the plurality of items, the chromaticity at the time when the elapsed time from the time of harvest (harvesting) is the shortest within the period corresponding to the trajectory T is indicated by the chromaticity coordinates of the starting point of the trajectory T, The chromaticity at the time when the elapsed time from the time of harvest (harvesting) is the longest within the period corresponding to the trajectory T is indicated by the chromaticity coordinates of the end point of the trajectory T. Further, for each trajectory T, a degree of similarity ε with respect to the ripe state is set for each of a plurality of chromaticity coordinates (points) on the trajectory T including both ends (start point and end point). Therefore, in the relational data, a degree of similarity ε is set for each of the plurality of chromaticity coordinates (points) forming the trajectory T for each item.

FIG. 2 shows an example of the relationship between the degree of similarity ε and the chromaticity (chromaticity coordinate) with respect to the ripeness state for one item α1. Item α1, for which the relationship between similarity ε and chromaticity coordinates is shown in FIG. Moreover, the degree of similarity ε decreases as time passes from the time of harvest (the time of harvest). Furthermore, there are processed foods that fall under item α1. Further, in the relationship shown in the example of FIG. 2, the similarity ε is set to a value of 0 to 100, and the higher the similarity ε to the ripe state, the larger the value is set. In an example of FIG. 2, the relationship between the similarity ε and the chromaticity coordinates in the xy chromaticity diagram is shown for item α1, and the change in the chromaticity coordinates in a predetermined chromaticity diagram corresponding to the change in the similarity ε is shown. A trajectory Ta is shown as the aforementioned trajectory T that provides information on the following.

In the relationship between similarity ε and chromaticity for item α1 shown in FIG. For the chromaticity coordinate corresponding to the end point A2 of Ta, 0, which is the lowest value, is set as the value of similarity ε. Then, a value of similarity ε is set for each of a plurality of chromaticity coordinates (points) on the trajectory Ta from the starting point A1 to the ending point A2. In the relationship for item α1 shown in FIG. 2, the value of similarity ε decreases along the trajectory Ta from the starting point A1 to the ending point A2. For example, on the trajectory Ta, the values of similarity ε are set to 80, 60, 40, and 20 for the chromaticity coordinates corresponding to points A3, A4, A5, and A6, respectively.

Furthermore, the ratio of the amount of change in similarity ε to the amount of movement along the trajectory Ta in the xy chromaticity diagram is defined. In other words, the amount of change in similarity ε per unit distance is defined. In the example of FIG. 2, the above-mentioned ratio is higher in the starting point A1 on the trajectory Ta and the area in its vicinity than in the ending point A2 on the trajectory Ta and the area in its vicinity. Therefore, the moving distance along the trajectory Ta from the starting point A1 where the value of similarity ε is 100 to the point A3 where the value of similarity ε is 80 is from the point A6 where the value of similarity ε is 20 to the point A3 where the value of similarity ε is 80. It is smaller than the moving distance along the trajectory Ta to the end point A2 whose value is 0. As described above, in the relationship for the item α1 shown in FIG. 2, the ratio of the amount of change in the degree of similarity ε to the amount of movement along the trajectory Ta changes depending on the area on the trajectory Ta, and becomes non-uniform. However, although it is preferable that the above-mentioned ratio be non-uniform, it may be uniform or substantially uniform over the entire trajectory Ta from the starting point A1 to the ending point A2.

FIG. 3 shows an example of the relationship between the degree of similarity ε and the chromaticity (chromaticity coordinates) with respect to the ripe state for one item α2 different from the item α1 shown in FIG. Item α2, for which the relationship between similarity ε and chromaticity coordinates is shown in FIG. Once the time has passed, it is ready to eat. Furthermore, there are processed foods that fall under item α2, such as wine. Furthermore, the degree of similarity ε of item α2 decreases as time passes after the item is ripe for consumption. In the relationship shown in the example of FIG. 3 as well, the similarity ε is set to a value between 0 and 100, and the higher the similarity ε with respect to the ripe state, the larger the value is set. In an example of FIG. 3, the relationship between the similarity ε and the chromaticity coordinates in the xy chromaticity diagram is shown for item α2, and the change in the chromaticity coordinates in a predetermined chromaticity diagram corresponding to the change in the similarity ε is shown. A trajectory Tb is shown as the aforementioned trajectory T that provides information on the following.

Also in the relationship between similarity ε and chromaticity for item α2 shown in FIG. ), a value of similarity ε is set for each of them. Then, 0, which is the lowest value, is set as the value of similarity ε for the chromaticity coordinate corresponding to the end point B2 of the trajectory Tb. However, in the relationship for item α2 shown in FIG. 3, 70 is set as the value of similarity ε with respect to the chromaticity coordinate corresponding to starting point B1 of trajectory Tb. Then, the maximum value of 100 is set as the value of the similarity ε for the chromaticity coordinate corresponding to the point B3 between the starting point B1 and the ending point B2 on the trajectory Tb. In this way, depending on the food, the starting point of the trajectory T may not be the best time to eat (coordinates with the highest degree of similarity ε in the trajectory T). In this case, there is a point that is ripe for eating somewhere in the coordinates other than the starting point. Furthermore, depending on the food, the degree of similarity ε, which once decreased in the trajectory T, may start to increase.

In the relationship for item α2 shown in FIG. 3, the chromaticity at the time of eating is indicated by the chromaticity coordinates of point B3. The period from the starting point B1 to the point B3 on the trajectory Tb corresponds to a period in which the degree of similarity to the ready-to-eat state of item α2 is increasing, and corresponds to a period before the ready-to-eat state of item α2. On the other hand, the period from point B3 to end point B2 on trajectory Tb corresponds to a period in which the degree of similarity to the ripe state of item α2 is decreasing, and corresponds to a period after the ripe state of item α2.

FIG. 4 shows an example of relational data showing the relationship between the degree of similarity ε and the chromaticity for each of a plurality of items in the ripe state. The relationship data in the example of FIG. 4 shows the relationship between similarity ε and chromaticity for each of a plurality of items including green peppers, chicken, and apples. In addition, in the relational data in FIG. 4, for each item, a trajectory T, which is information about changes in chromaticity coordinates in the xy chromaticity diagram corresponding to changes in similarity ε, and formula information for the trajectory T are provided. , shown. For example, for green peppers, chicken, and apples, the equation information of the trajectory T is y=f1(x), y=f2(x), y=f3(x ) is shown.

Further, in the relational data of FIG. 4, the chromaticity coordinates of the starting point and ending point of the trajectory T are shown for each of the plurality of items. For example, for green peppers, chicken, and apples, the chromaticity coordinates of the starting point of the trajectory T are (x1a, y1a), (x2a, y2a), and (x3a, y3a), respectively, and the chromaticity coordinates of the ending point of the trajectory T , (x1b, y1b), (x2b, y2b), and (x3b, y3b) are shown, respectively. Furthermore, in the relational data of FIG. 4, for any item, the lowest value such as 0 is set as the value of the similarity ε for the chromaticity coordinate corresponding to the end point of the trajectory T. Note that although not shown in FIG. 4, each chromaticity coordinate is preferably associated with a degree of similarity ε in each chromaticity coordinate.

Furthermore, for items such as green peppers and chicken that are ripe for consumption at the time of harvest (harvesting), the highest value such as 100 is set as the value of similarity ε for the chromaticity coordinate corresponding to the starting point of trajectory T. . For items whose best time to eat is at the time of harvest (harvesting), it is shown that the starting point of the trajectory T is the point in the trajectory T where the degree of similarity ε has the highest value. On the other hand, for items that are ripe for eating after a certain period of time has elapsed from the time of harvest, in addition to the chromaticity coordinates of the start and end points of the trajectory T, the chromaticity coordinates of the point where the similarity ε has the highest value such as 100 in the trajectory T. is shown. For example, for an apple, (x3c, y3c) is shown as the chromaticity coordinates of the point at which the similarity ε has the highest value on the trajectory T.

Here, generation of relational data will be explained. The generation of the relational data may be performed using the processing execution unit 11 of the processing device 10, or may be performed using a processing device other than the processing device 10. In generating the relational data, information indicating the above-mentioned relationship between the degree of similarity ε and the chromaticity with respect to the ripeness state is generated for each of the plurality of items. In one example, information indicating the above-mentioned relationship for item α1 is generated as follows. Note that for items other than item α1, information indicating the relationship between similarity ε and chromaticity is generated in the same manner as the information indicating the above-mentioned relationship for item α1.

In the generation of information indicating the above-mentioned relationship for item α1, images and the like of food belonging to item α1 are acquired at each of a plurality of time points after the time of harvest (harvesting time). Then, based on the image, etc., color information of the item α1 is acquired for each of a plurality of times when the image, etc. was acquired. At this time, RGB values and the like of a predetermined area of the image are acquired as color information. Then, the chromaticity information of the item α1 is calculated based on the color information of the item α1 for each of the plurality of time points at which the image etc. were acquired, and for example, an xy chromaticity diagram etc. Chromaticity coordinates in a predetermined chromaticity diagram are calculated as chromaticity information of item α1.

Further, for each of a plurality of time points at which images and the like are acquired, a setting value of the degree of similarity ε with respect to the ripe state of the item α1 is set. At this time, the set value of the degree of similarity ε is set based on, for example, information inputted by a person with extensive knowledge and experience regarding the item α1. Then, by performing curve interpolation or curve fitting on the chromaticity coordinates at multiple points in time when images etc. were acquired, changes in the chromaticity coordinates in a predetermined chromaticity diagram corresponding to changes in similarity ε are performed for item α1. The above-mentioned trajectory T, which is information about the trajectory T, is formed, and formula information of the formed trajectory T is calculated.

In calculating the formula information of the trajectory T, constants of the formula of the formula information may be derived by a fitting calculation. By calculating the formula information of the trajectory T for the item α1, the chromaticity coordinates and the like corresponding to the starting point and the ending point in the trajectory T of the item α1 are calculated. In addition, in the generation of information indicating the above-mentioned relationship for item α1, multiple colors on the formed trajectory For each degree coordinate (point), a value of similarity ε with respect to the ripe state of item α1 is set. As described above, information indicating the relationship between similarity ε and chromaticity is generated for item α1.

The determination unit 15 determines the target food based on the input information regarding the target food to be determined and the relationship between the degree of similarity ε and the chromaticity of the item to which the target food belongs. FIG. 5 shows the determination process regarding the target food, which is performed by the determination unit 15. When the process of FIG. 5 is started, the determination unit 15 acquires input information about the target food, such as a photographed image of the target food (S51). Then, the determination unit 15 identifies the item to which the target food belongs (S52). That is, it is specified which item, such as green pepper, chicken, or apple, the target food corresponds to. The item to which the target food belongs may be identified based on information input by a person or the like through the user interface 14 or the like, or may be identified using AI based on an image of the target item.

Then, the determination unit 15 calculates the chromaticity information of the target food using the input information about the target food (S53). In one example, the determination unit 15 extracts RGB values, etc. in a predetermined area (predetermined pixel) in a photographed image of the target food as color information of the target food. Then, the determination unit 15 calculates the chromaticity information of the target food based on the extracted color information of the target food, such as the above-mentioned RGB values. At this time, before calculating the chromaticity information of the target food, appropriate correction processing may be performed on the RGB values of the target food.

The chromaticity information of the target food is indicated by, for example, chromaticity coordinates in a predetermined chromaticity diagram. In one example, the chromaticity information of the target food is represented by chromaticity coordinates in an xy chromaticity diagram or a Lab chromaticity diagram. Then, the determination unit 15 obtains the relationship between the degree of similarity ε and the chromaticity with respect to the ripe state of the item to which the target food belongs (S54). At this time, the determination unit 15 reads, for example, the above-mentioned relationship regarding the item to which the target food belongs from the relationship data stored in the data storage unit 12.

Then, the determination unit 15 determines the degree of similarity ε of the target food with respect to the ripe state based on the chromaticity information of the target food corresponding to the input information about the target food and the above-mentioned relationship regarding the item to which the target food belongs. A determination is made (S55). Further, the determining unit 15 determines whether the target food is before or after its ripening time, based on the chromaticity information of the target food and the above-mentioned relationship regarding the item to which the target food belongs (S56). Thereby, it is determined whether or not the target food is already past its best time to eat. Note that if the target food belongs to an item that is ripe for eating at the time of harvest (harvesting), it is determined that the target food is past the ripe time in any situation. Then, the determination unit 15 outputs the determination result regarding the target food (S57). In addition, in the determination process regarding the target food performed by the determination unit 15, only one of step S55 and step S56 may be performed.

FIG. 6 shows a plurality of examples of the determination results of the target food by the determination unit 15. In the example shown in FIG. 6, the target food belongs to the aforementioned item α1. As for the relationship between similarity ε and chromaticity for item α1, similar to the example in FIG. As T, a trajectory Ta is shown. In one of the plurality of examples shown in FIG. 6, the chromaticity coordinates corresponding to the point M1 on the locus Ta in the xy chromaticity diagram are calculated as the chromaticity information of the target food. In this case, the determination unit 15 specifies the chromaticity coordinate corresponding to the point M1 on the trajectory Ta, and calculates the similarity of the target food with respect to the ripe state by determining the value of similarity ε set for the specified chromaticity coordinate. Let the degree be ε. Therefore, a value of 65, which is the value set for the point M1, is derived as the degree of similarity ε of the target food.

In another example of the plurality of examples shown in FIG. 6, the chromaticity coordinates corresponding to a point M2 that deviates from the trajectory Ta in the xy chromaticity diagram are calculated as the chromaticity information of the target food. In this case, the determination unit 15 specifies the chromaticity coordinate (position) closest to the point M2 on the trajectory Ta, and uses the value of the similarity ε set for the specified chromaticity coordinate as the target for the ripe state. Let the degree of food similarity be ε. Therefore, point A7 is specified as the closest position to point M2 on trajectory Ta, and 45, which is the value set for the chromaticity coordinate corresponding to point A7, is derived as the similarity ε of the target food. Ru.

In one example, when the chromaticity coordinates of the target food deviate from the trajectory Ta, the determination unit 15 calculates the straight-line distance from the chromaticity coordinates of the target food to the nearest chromaticity coordinate (position) on the trajectory Ta. is calculated. For example, when the chromaticity coordinates corresponding to the point M2 are calculated as the chromaticity information of the target food, the determination unit 15 calculates the straight-line distance d1 between the point M2 and the point A7 on the trajectory Ta. In this case, the determination unit 15 calculates the likelihood of the determination result regarding the similarity ε of the target food, etc., based on the calculated straight-line distance. At this time, the smaller the calculated straight-line distance, the higher the likelihood.

Furthermore, the target food belongs to the item α2 mentioned above, and the relationship between the similarity ε and the chromaticity for the item α2 is as follows in the xy chromaticity diagram corresponding to the change in the similarity ε, as in the example in FIG. Assume that a trajectory Tb is shown as a trajectory T that provides information about changes in chromaticity coordinates. In this case as well, the degree of similarity ε of the target food with respect to the ripe state is calculated by performing the same processing as that described in the plurality of examples in FIG. 6 . Furthermore, if the target food belongs to item α2 and the relationship between similarity ε and chromaticity for item α2 shows a trajectory Tb similar to the example in FIG. 3, the same process as described in the multiple examples in FIG. By performing this process, it is determined whether the target food is ready to eat or not.

For example, if the chromaticity coordinates of the target food or the position closest to the chromaticity coordinates of the target food on the trajectory Tb is located between the starting point B1 and point B3 of the trajectory Tb, the determination unit 15 determines whether the target food It is determined that the target food is before the ripe state and that the degree of similarity ε of the target food with respect to the ripe state is increasing. On the other hand, if the chromaticity coordinates of the target food or the position closest to the chromaticity coordinates of the target food on the trajectory Tb is located between the point B3 and the end point B2 of the trajectory Tb, the determination unit 15 determines that the target food It is determined that the target food is after the ripe state and that the degree of similarity ε of the target food with respect to the ripe state is decreasing.

The notification control unit 17 generates notification information about the target food including the determination result of the target food determined by the determination unit 15 as described above. Then, the notification control unit 17 displays the generated notification information on the display screen constituting the user interface 14 to notify notification information about the target food. The notification information about the target food shows the determination result of the degree of similarity ε of the target food with respect to the ripeness state, and the determination result of the degree of similarity ε of the target food is, for example, one of the above-mentioned values of 0 to 100. Indicated by value.

In one example, in the notification information, the determination result regarding the degree of similarity ε of the target food may be shown as either a three-level evaluation, a five-level evaluation, or the like. Furthermore, the determination result regarding the degree of similarity ε of the target food may be indicated by symbols such as "circle", "triangle", and "cross" instead of numerical values. In one example, the above-mentioned trajectory T regarding the item to which the target food belongs is shown in the notification information regarding the target food. Then, the chromaticity coordinates (position) corresponding to the target food on the trajectory T are shown as a determination result regarding the similarity ε of the target food. In one example, in addition to the determination result regarding the similarity ε of the target food, the likelihood of the determination result derived as described above may be indicated in the notification information.

In addition, if the target food belongs to an item that is ripe for eating after a certain period of time after harvest, in addition to or in place of the determination result regarding the similarity ε of the target food. The determination result as to whether the target food is ready to eat or not is shown in the notification information. For example, if the target food belongs to item α2 and the relationship between similarity ε and chromaticity for item α2 shows a trajectory Tb similar to the example in FIG. 3, in the notification information, as the determination result for the target food, For example, it is displayed as "73 (rising)". When "73 (increasing)" is shown as the determination result for the target food, it indicates that the degree of similarity ε of the target food to the ripe state is 73 and that the target food is in the state before the ripe state. .

Furthermore, before the determination process for the target food is performed by the determination unit 15 as described above, the correction unit 16 corrects the above-mentioned relationship between the similarity ε and the chromaticity for the item to which the target food belongs. Good too. In this case, the correction unit 16 corrects the above-mentioned relationship regarding the item to which the target food belongs based on the environment in which the input information regarding the target food was acquired. When the correction unit 16 performs the correction, the determination unit 15 determines the target food as described above based on the relationship between the similarity ε and the chromaticity, which has been corrected in accordance with the environment in which the input information was acquired. judge.

FIG. 7 shows a process of correcting the relationship between similarity ε and chromaticity for the item to which the target food belongs, which is performed by the correction unit 16. The process in FIG. 7 is performed before the above-described determination process by the determination unit 15 is performed. That is, the process in FIG. 7 is performed before the process in step S55 in the flowchart shown in FIG. 5 is executed. When the process of FIG. 7 is started, the correction unit 16 acquires information regarding the environment in which input information regarding the target food was acquired (S71). Then, the correction unit 16 acquires the relationship between the degree of similarity ε and the chromaticity for the item to which the target food belongs, by reading from the above-mentioned relationship data (S72).

Then, the correction unit 16 corrects the above-mentioned trajectory T regarding the item to which the target food belongs based on the environment in which the input information about the target food was acquired (S73). As a result, the trajectory T, which is information about changes in chromaticity coordinates in a predetermined chromaticity diagram corresponding to changes in similarity ε, for the item to which the target food belongs is transferred to the environment where the input information about the target food was acquired. Corrected and corrected. Then, the correction unit 16 calculates formula information in a predetermined chromaticity diagram of the corrected trajectory T (S74). In correcting the trajectory T and calculating formula information for the trajectory T after correction, translation of the trajectory T before correction, expansion or reduction of the trajectory T before correction (partial expansion/reduction, overall expansion/reduction) are performed. (including reduction), fitting calculations, etc.

Then, the correction unit 16 sets, for each of the plurality of chromaticity coordinates (points) on the corrected trajectory T, a value of similarity ε with respect to the ripe state of the item to which the target food belongs (S75). At this time, for example, based on the value of similarity ε set for each chromaticity coordinate on the trajectory T before correction, similarity is determined for each chromaticity coordinate on the trajectory T after correction. The value of degree ε is set. By performing the above-described processing, the relationship between the degree of similarity ε and the chromaticity of the item to which the target food belongs is corrected.

In one example, the input information about the target food includes a photographed image of the target food, and the information about the environment in which the input information about the target food was obtained includes spec information of the photographing device that photographed the target food, Information about the lighting environment at the time of photographing the target food, the background color of the target food at the time of photographing the target food, etc. is acquired. In this case, the correction unit 16 identifies the environment in which the input information regarding the target food was acquired, that is, the environment in which the target food was photographed, based on the above-mentioned information and the like. Then, the correction unit 16 corrects the above-mentioned trajectory T regarding the item to which the target food belongs, based on the identification result regarding the environment in which the target food was photographed, to obtain the similarity ε and chromaticity for the item to which the target food belongs. Correct the relationship between

In another example, with respect to the item to which the target food belongs, foods whose similarity ε to the ripe state meets one or more set values are stored in the same environment as the environment in which the input information about the target food was acquired. Take a photo with At this time, for example, with respect to the item to which the target food belongs, foods with similarity ε values of 100 (highest value) and 0 (lowest value) are photographed in an environment in which input information about the target food has been acquired. Ru. Then, the correction unit 16 acquires images of foods whose similarity degree ε corresponds to one or more set values as information regarding the environment in which the input information about the target food was acquired. For example, before acquiring input information about the target food, in the same environment as the environment in which input information about the target food is acquired, a sample product with a similarity ε value of 100 and a sample product with a similarity ε value of 0 are selected. The trajectory T is corrected by photographing in advance a sample product having a plurality of degrees of similarity ε such as , and the like. At this time, the sample product may be an actual sample (a sample that can be eaten) that is the same as the target food, or a food sample made of resin or the like (a sample that is impossible to eat). . Further, the sample product may be a virtual sample (a sample that cannot be touched) virtually displayed in the imaging section.

The correction unit 16 selects images of foods whose similarity degree ε satisfies one or more setting values to determine whether the similarity degree ε is one or more in the environment in which the input information about the target food is acquired, with respect to the item to which the target food belongs. Calculate chromaticity information for each of the set values. At this time, chromaticity coordinates and the like in a predetermined chromaticity diagram are calculated as chromaticity information. Then, the correction unit 16 calculates the degree of similarity ε for the item to which the target food belongs, based on the relationship between the above-mentioned one or more setting values regarding the degree of similarity ε for the item to which the target food belongs and the calculated chromaticity information. Correct the relationship with chromaticity. At this time, the correction unit 16 corrects, for example, the chromaticity coordinates in a predetermined chromaticity diagram when the degree of similarity ε becomes each of one or more set values, from the trajectory T before correction. Then, the correction unit 16 corrects the trajectory T in accordance with the corrected position of the chromaticity coordinate when the degree of similarity ε becomes each of one or more set values.

FIG. 8 shows an example of the correction result by the correction unit 16 of the relationship between the degree of similarity ε and the chromaticity for the item α1 shown in FIG. In the example of FIG. 8, the above-mentioned trajectory Ta is corrected by correcting the relationship between similarity ε and chromaticity for item α1. In the example of FIG. 8, the maximum value of 100 is set as the above-mentioned one or more setting values of the degree of similarity ε for the item α1. Then, the food item α1 with the similarity degree ε of 100, which is the set value, is photographed in the environment in which the input information about the target food has been acquired.

Then, the correction unit 16 determines the xy chromaticity of the item α1 when the similarity ε becomes 100 in the environment where the input information about the target food is obtained, based on the image taken of the food with the similarity ε of 100. Calculate the chromaticity coordinates (chromaticity information) in the diagram. Thereby, the chromaticity coordinate corresponding to the point N1 is calculated as the chromaticity coordinate when the degree of similarity ε is 100 in the environment in which the input information was acquired. Then, the correction unit 16 corrects the chromaticity coordinates of the starting point from the trajectory Ta to the chromaticity coordinates corresponding to the point N1, and parallelizes the trajectory Ta before correction in the xy chromaticity diagram to a state where the starting point is the point N1. Moving. As a result, the trajectory Ta is corrected to a trajectory Tn having the point N1 as the starting point. Then, the correction unit 16 calculates the formula information of the corrected trajectory Tn, and corrects the relationship between the similarity ε and the chromaticity for the item α1.

FIG. 9 shows another example of the correction result by the correction unit 16 of the relationship between the degree of similarity ε and the chromaticity for the item α1 shown in FIG. 2, which is different from that shown in FIG. In the example of FIG. 9 as well, the above-mentioned trajectory Ta is corrected by correcting the relationship between the similarity ε and the chromaticity for the item α1. In the example of FIG. 9, the maximum value of 100 and the minimum value of 0 are set as the one or more setting values of the similarity degree ε for the item α1. Then, for item α1, food items for which the similarity degree ε is 100, which is the set value, and food items for which the similarity degree ε is 0, which is the set value, are each photographed in the environment in which input information about the target food has been acquired. do.

Then, the correction unit 16 determines the xy chromaticity of the item α1 when the similarity ε becomes 100 in the environment where the input information about the target food is obtained, based on the image taken of the food with the similarity ε of 100. Calculate the chromaticity coordinates (chromaticity information) in the diagram. As a result, the chromaticity coordinates corresponding to the point Q1 are calculated as the chromaticity coordinates when the degree of similarity ε is 100 in the environment in which the input information was acquired. In addition, the correction unit 16 calculates the xy chromaticity of the item α1 when the similarity ε becomes 0 in the environment in which the input information about the target food is obtained, based on the image taken of the food whose similarity ε is 0. Calculate the chromaticity coordinates (chromaticity information) in the diagram. As a result, the chromaticity coordinate corresponding to the point Q2 is calculated as the chromaticity coordinate when the degree of similarity ε becomes 0 in the environment in which the input information was acquired.

Then, from the trajectory Ta, the correction unit 16 corrects the chromaticity coordinates of the starting point to the chromaticity coordinates corresponding to the point Q1, and corrects the chromaticity coordinates of the end point to the chromaticity coordinates corresponding to the point Q2. Then, the correction unit 16 corrects the trajectory Ta before correction to a state where the point Q1 is the starting point and the point Q2 is the ending point, and the trajectory Ta has the point Q1 as the starting point and the point Q2 as the ending point. The trajectory is corrected to the trajectory Tq. Then, the correction unit 16 calculates the formula information of the corrected trajectory Tq, and corrects the relationship between the similarity ε and the chromaticity for the item α1. Here, the trajectory Ta before correction is corrected to the trajectory Tq by, for example, parallel translation and reduction in the xy chromaticity diagram. Further, in calculating the formula information of the trajectory Tq, a constant of the formula of the formula information may be derived by a fitting calculation.

Note that for items other than item α1, the relationship between similarity ε and chromaticity can be corrected in the same manner as the example in FIG. 8 and the example in FIG. 9. Furthermore, even if a value other than 100 (highest value) and 0 (lowest value) can be set as one or more of the above-mentioned setting values regarding similarity ε, the example in FIG. 8 and the example in FIG. Similarly to either method, the relationship between similarity ε and chromaticity can be corrected. In this case, the settings are made based on information input by a person with extensive knowledge and experience regarding item α1.

In one example, each of a plurality of arbitrary foods different from the target food among the items to which the target food belongs is photographed in the same environment as the environment in which input information about the target food was acquired. For example, if the item to which the target food belongs is green pepper, each of a plurality of arbitrary green peppers different from the target food is photographed in an environment in which input information about the target food is acquired. Then, the correction unit 16 obtains images of each of a plurality of arbitrary foods as information regarding the environment in which the input information regarding the target food was obtained.

In this example, the correction unit 16 calculates, for each of the plurality of photographed foods, chromaticity information in the environment in which input information about the target food was acquired, based on the photographed image. Then, the correction unit 16 corrects the relationship between the degree of similarity ε and the chromaticity for the item to which the target food belongs, based on the chromaticity information for each of the plurality of photographed arbitrary foods. For example, two arbitrary foods that are different from the target food among the items to which the target food belongs are photographed in an environment in which input information about the target food has been acquired, and the correction unit 16 corrects the target food for each of the two photographed foods. Assume that chromaticity coordinates in an xy chromaticity diagram are calculated as chromaticity information in an environment in which input information about food is acquired. In this case, the correction unit 16 corrects the relationship between the degree of similarity ε and the chromaticity of the item to which the target food belongs based on, for example, the slope of a straight line connecting the chromaticity coordinates of the two photographed foods. .

In another example, a predetermined replica of the item to which the target food belongs or an object that becomes a predetermined white color is photographed under the same conditions as the conditions under which the input information regarding the target food was acquired. For example, if the item to which the target food belongs is an apple, a replica of the apple is photographed. Moreover, examples of objects that turn a predetermined white color include price tags placed near target foods in supermarkets and the like. Then, the correction unit 16 acquires a predetermined replica or an image of a predetermined white object as information regarding the environment in which the input information regarding the target food was acquired.

In this example, the correction unit 16 calculates chromaticity information regarding the photographed predetermined replica or the predetermined white object based on the photographed image in the environment in which input information about the target food was acquired. Then, the correction unit 16 corrects the relationship between the degree of similarity ε and the chromaticity of the item to which the target food belongs based on the chromaticity information about the photographed replica or white object. For example, it is assumed that the correction unit 16 calculates chromaticity coordinates in an xy chromaticity diagram as chromaticity information in the environment in which input information about the target food is acquired for a predetermined replica or a predetermined white object. In this case, the correction unit 16 corrects the relationship between the degree of similarity ε and the chromaticity of the item to which the target food belongs based on the chromaticity coordinates of the replica or the white object.

As described above, in this embodiment, the relationship between the degree of similarity ε and the chromaticity with respect to the ripe state is shown for each item in the relational data. Then, the determination unit 15 determines the degree of similarity ε of the target food with respect to the ripe state and the degree of similarity ε of the target food with respect to the ripe state, based on the input information about the target food and the relationship between the degree of similarity ε and the chromaticity of the item to which the target food belongs. To determine whether food is ready to eat or not. Therefore, the degree of similarity ε to the ripe state of the target food and whether it is before or after the ripe state of the target food can be appropriately determined without relying on human senses.

Furthermore, in the present embodiment, notification information about the target food including the above-described determination result about the target food is notified. Therefore, the users of the determination system 1 and the processing device 10 can appropriately grasp information about the target food, including the above-mentioned degree of similarity ε of the target food and whether the target food is before or after its best to eat. It becomes possible.

Further, in the present embodiment, the determination unit 15 calculates chromaticity information of the target food using input information about the target food. The determining unit 15 then determines the target food based on the calculated chromaticity information of the target food and the relationship between the degree of similarity ε and the chromaticity for the item to which the target food belongs. Therefore, for the target food, the degree of similarity ε with respect to the ripe state and whether it is before or after the ripe state are determined more appropriately.

Furthermore, in the present embodiment, the correction unit 16 corrects the relationship between the degree of similarity ε and the chromaticity of the item to which the target food belongs, based on the environment in which the input information about the target food is acquired. The determining unit 15 then determines the target food based on the relationship between the degree of similarity ε and the chromaticity, which has been corrected in accordance with the environment in which the input information was acquired. Therefore, the degree of similarity ε with respect to the ripe state of the target food, whether it is before or after the ripe state, etc. of the target food are appropriately determined in accordance with the environment in which the input information about the target food is acquired.

Further, in one example of the present embodiment, the correction unit 16 calculates chromaticity information when the similarity ε becomes each of one or more set values in the environment where the input information is acquired for the item to which the target food belongs. do. Then, the correction unit 16 corrects the relationship between the similarity ε and the chromaticity for the item to which the target food belongs, based on the relationship between one or more setting values regarding the similarity ε and the calculated chromaticity information. Therefore, the relationship between the degree of similarity ε and the chromaticity of the item to which the target food belongs is appropriately corrected in accordance with the environment in which the input information about the target food is acquired.

(Modified example)
In a modification shown in FIG. 10, the determination system 1 includes two processing devices 10, 100. In this modification, the processing device 10 includes the above-described processing execution section 11, data storage section 12, and communication interface 13. The processing device 100 includes a processing execution section 101, a data storage section 102, a communication interface 103, and a user interface 104. In this modification, the processing execution section 11 of the processing device 10 performs the processing by the determination section 15 and the processing by the correction section 16, and the processing execution section 101 of the processing device 100 performs the processing by the notification control section 17 described above. conduct.

The processing device 100 is a terminal device such as a smartphone or a computer, and includes a processor or an integrated circuit, and a storage medium such as a memory. In the processing device 100, a processor or an integrated circuit functions as a processing execution unit 101, and a storage medium functions as a data storage unit 102. Furthermore, the processing device 100 communicates with other processing devices such as the processing device 10 via the communication interface 103. Further, the configuration of the user interface 104 is similar to the user interface 14 described above. In this modification, the processing device 10 is a computer different from the processing device 100 or a server in a cloud environment. In the processing device 10, a processor or an integrated circuit of a computer, or a virtual processor functions as a processing execution unit 11, and a storage medium of the computer or a cloud memory functions as a data storage unit 12.

In the determination system 1 of this modification, the processing execution unit 11 of the processing device 10 and the processing execution unit 101 of the processing device 100 cooperate to perform the aforementioned processing by the determination unit 15, correction unit 16, and notification control unit 17. be exposed. That is, a plurality of processing devices cooperate to perform the above-described processing by the determination unit 15, correction unit 16, and notification control unit 17. Also in this modification, the above-described processing by the determination unit 15, correction unit 16, and notification control unit 17 is performed similarly to the above-described embodiments. Therefore, this modification also has the same functions and effects as the above-described embodiments.

Note that in one modification, the processing by the determination unit 15 may be performed by the processing execution unit 101 of the processing device 100, and in another modification, the processing by the correction unit 16 may be performed by the processing execution unit 101 of the processing device 100. 101. Further, in a modification, a part of the processing by the determination unit 15 is performed by the processing execution unit 101 of the processing device 100, and the remaining part of the processing by the determination unit 15 is performed by the processing execution unit 11 of the processing device 10. May be done. In this case, for example, the processing execution unit 101 calculates chromaticity information of the target food based on input information about the target food. Then, the processing execution unit 11 determines the target food as described above based on the chromaticity information of the target food and the relationship between the degree of similarity ε and the chromaticity for the item to which the target food belongs.

Therefore, in the embodiments, the above-described processing by the determination unit 15, correction unit 16, and notification control unit 17 may be performed by one or more of the terminal device, computer, and server in the cloud environment that constitute the determination system 1. Thereby, the same operation and effect as the above-mentioned embodiment etc. are produced.

According to at least one of these embodiments, based on the input information input about the target food and the relationship between the degree of similarity and chromaticity with respect to the ripe state of the item to which the target food belongs, It is determined which one is. Thereby, it is possible to provide a food determination system, a determination method, and a determination program that appropriately determine whether a target food is in a period before or after its best-to-eat state.

Although several embodiments of the invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and changes can be made without departing from the gist of the invention. These embodiments and their modifications are included within the scope and gist of the invention, as well as within the scope of the invention described in the claims and its equivalents.

DESCRIPTION OF SYMBOLS 1... Judgment system, 10, 100... Processing device, 11, 101... Process execution unit, 12, 102... Data storage unit, 15... Judgment unit, 16... Correction unit, 17... Notification control unit, ε... Similarity.

Claims (6)

Determining whether the target food is before or after the ripe time based on the input information input about the target food and the relationship between similarity and chromaticity with respect to the ripe state of the item to which the target food belongs. and a determination section;
a notification control unit that causes notification information about the target food including the determination result of the target food by the determination unit;
A food judgment system comprising: The determination unit calculates chromaticity information of the target food using the input information, and calculates the calculated chromaticity information of the target food, and the similarity and color of the item to which the target food belongs. 2. The determination system according to claim 1, wherein the determination is made regarding the target food based on the relationship with degree. further comprising a correction unit that corrects the relationship between the degree of similarity and the chromaticity for the item to which the target food belongs, based on the environment in which the input information about the target food was acquired;
The determination unit determines the target food based on the relationship between the degree of similarity and the chromaticity corrected in accordance with the environment.
The determination system according to claim 1 or 2. The correction unit calculates chromaticity information regarding the item to which the target food belongs when the degree of similarity becomes each of one or more set values in the environment in which the input information is obtained, and calculates chromaticity information regarding the degree of similarity. correcting the relationship between the similarity and the chromaticity for the item to which the target food belongs based on the relationship between the one or more setting values and the calculated chromaticity information;
The determination system according to claim 3. Determining whether the target food is before or after the ripe time based on the input information input about the target food and the relationship between similarity and chromaticity with respect to the ripe state of the item to which the target food belongs. and;
a step of notifying notification information about the target food including a determination result about the target food;
A method for determining food, comprising: In the processing equipment,
Determining whether the target food is before or after the ripe time based on the input information input about the target food and the relationship between similarity and chromaticity with respect to the ripe state of the item to which the target food belongs. let me,
Notifying notification information regarding the target food including the determination result regarding the target food;
Judgment program for food.